APEX

1

1

MOUNTAIN HOUSE HIGH SCHOOL

19359A

3/1/2025

04/27/2024

Table of Contents

Table of Contents

1

Introduction to "High Stakes"

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Overview

"High Stakes" is a competitive robotics game where teams score points by placing rings onto stakes, climbing a ladder-like structure, and strategically utilizing field elements. The game requires precise robot control, strategic thinking, and effective teamwork.

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Field Description

The game field is a 12ft x 12ft arena with multiple stakes positioned strategically. Stakes vary in height and function, offering different scoring opportunities. A central ladder-like structure provides an additional way to score by climbing.

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Objective

Teams compete to score the highest number of points by:

• Placing rings on stakes at different heights.
• Climbing the central structure for extra points.
• Strategically utilizing alliance and neutral stakes.
• Maximizing scoring during the autonomous period for bonus points.

FIELD

2

Game Objects & Scoring System

Rings

• 48 total rings: 24 red, 24 blue.
• Rings placed on stakes but not at the top earn +1 point.
• Rings placed at the top of a stake earn +3 points.

Stakes

• 10 stakes in total:
• 5 mobile stakes (6-ring capacity each).
• 2 neutral wall stakes (6-ring capacity each).
• 2 alliance wall stakes (6-ring capacity each).
• 1 high stake (central ladder) (1-ring capacity).
• Stake bonuses:
• Positive: Doubling ring point values.
• Negative: Applying negative scoring to ring placements.

Climbing Scoring

• Level 1 (off-ground): +3 points
• Level 2 (above bar): +6 points
• Level 3 (highest bar): +12 points

RINGS (48)

24 red, 24 blue

on stake, not top: +1 pt.

on stake, at top: +3 pt.

STAKES (10)

5 mobile - 6 ring ea.

2 neutral wall - 6 ring ea.

2 alliance wall - 2 ring ea.

1 high (ladder) - 1 ring

STAKES in corner:

Positive: double ring pt vals

Negative: ring pt vals are negative

CLIMB:

LVL 3 • +12 pt

LVL 2 • +6 pt

LVL 1 • +3 pt

The Game - High Stakes

4/27/24

3

FIELD

GAME OBJECTS

AUTON: 15 sec

1 ring preload, stay on their side

AWP: 3 scored rings, ≥2 stakes with ≥1 ring, touch ladder, don’t contact/break starting line plane

Team scoring highest in auto. gets +6 pt. bonus

DRIVER: 1 min 45 sec (105 sec)

Possess limit: 1 mobile goal, 2 rings

• (rings on stakes don’t count)

rings can be descored from neutral stakes at any time

more pts. by climbing any side of ladder

12ft

12ft

CLIMB:

LVL 3 • +12 pt

LVL 2 • +6 pt

LVL 1 • +3 pt

STAKES (10)

5 mobile - 6 ring ea.

2 neutral wall - 6 ring ea.

2 alliance wall - 2 ring ea.

1 high (ladder) - 1 ring

RINGS (48)

24 red, 24 blue

on stake, not top: +1 pt.

on stake, at top: +3 pt.

STAKES in corner:

Positive: double ring pt vals

Negative: ring pt vals are negative

LVL1

off-ground

LVL2

above bar

LVL3

above bar

CLIMB

THE GAME

High Stakes

All unspecified dimensions are in inches.

ROBOT: 18x18x18 in.

First Brainstorming Meeting

5/3/24

4

will be using

• PROS, and
• either LemLib, EZ-Template, OkapiLib, or ARMS

DESIGNERS/BUILDERS:

• Likhith
• Yash
• Yashmit
• Ansh

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CODERS:

• Akshat
• Daksh

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DRIVER Team:

• Yash
• Likhith
• Yashmit

DECIDED ROLES

DESIGN

CODE

TODO NEXT MEETING

• Brianstorm Grabber Mechanism
• Over-under Flex-wheel intake
• Mobile goal scoring
• Hooks - Rings travel on hooks that push into stake
• Hood - Flexwheels pushing into stake
• Clamp
• Grabs Mogo from edge
• First class Lever

Linear Velocity

OR 450 rpm with 36t-48t gears

450 rpm → 64.8”/sec

10” base → 16-18” chassis width

Chain-belt Intake

Code Library Decision

5/6/24

5

CODE - Deciding which code library to use.

Primary library:

PROS

On top of that will be either one of the following:

• LemLib
• EZ-Template
• OkapiLib
• ARMS

LANG: C++

We won’t be using odometry, so no need to worry about that.

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However, we do want a library with which you can set the initial xy-position of the robot on the field and keep track of the position as it moves.

CAD Modeling and Designing the Chassis/Intake Flow

5/7/24

6

CAD-MODEL

Problem:

• The motor placement prevented the stake to slide within a protected zone for intake disks

Solution:

• We shifted the back motors up, which allowed the perfect amount of room for the stake

Problem:

• The disk needs to go up 14.5 inches and then drop onto the stake

Solution:

• We decided to make a ramp and a chained pull-up mechanism to smoothly and continuously intake and outake disks

Problem:

• The bot needs to grab the disk from wide angle and funnel it down to the chain pull-up mechanism.

Solution:

• We put a line of flex wheels that could grip onto the disks easily and since they can be arranged in an array, it can have a wide gripping area.

Side panels to allow for a better funneling of the disks.

CAD Modeling and Designing the Pushdown Method/Intake

5/8/24

7

CAD-MODEL

Stake Clamping Mechanism: In order to clamp on to the stake, we decided to use 2 pneumatic pistons with a first class lever system. This lever system allows us to easily change the mechanical advantage of the subsystem when necessary. In our design, we decided to use a mechanical advantage greater than 1.

Code Update on library decision

5/10/24

8

CODE - Update on library decision

Now, we decide among either LemLib or EZ-Template.

Code Update on library decision - FINALIZED

5/10/24

7

9

CODE - Update on library decision - FINALIZED

LemLib vs EZ-Template

Code Update on library decision - FINALIZED

5/10/24

10

CODE - Update on library decision - FINALIZED

LemLib vs EZ-Template

RESEARCH

After conducting extensive research and carefully evaluating multiple programming libraries available for our project, we have reached the conclusion that LemLib is the optimal choice for our needs. This decision is based on a combination of prior experience, a detailed comparison of its features against competing libraries, and an analysis of the development activity behind each option. Having worked with LemLib last year, we are already familiar with its structure, functionality, and unique advantages, which provides us with a strong foundation for maximizing its potential in our current implementation.

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One of the primary reasons for selecting LemLib over alternatives such as EZ-Template is its comprehensive and advanced feature set. LemLib is specifically designed to offer a high level of control and precision for robotics applications, making it a preferred choice for teams that require more sophisticated and intricate programming capabilities. Unlike EZ-Template, which is designed with simplicity in mind and is often targeted toward beginners or those looking for an easy-to-use framework, LemLib provides an extensive suite of tools that allow for fine-tuned customization and complex algorithmic implementations. This makes it an excellent choice for teams looking to push the limits of what is possible with their robotics programming.

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Code Update on library decision - FINALIZED

5/10/24

11

CODE - Update on library decision - FINALIZED

LemLib vs EZ-Template

RESEARCH

Another key advantage of LemLib is its unique feature set, which sets it apart from EZ-Template and similar libraries. It includes advanced motion control algorithms, improved odometry capabilities, and a more robust system for managing motor synchronization and sensor integration. These features are essential for ensuring precision and accuracy in robotic movement, particularly in autonomous routines that require highly controlled trajectories and dynamic adjustments. The level of control provided by LemLib is superior to what is available in EZ-Template, making it the ideal choice for competitive robotics teams that require the highest level of performance.

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Additionally, LemLib benefits from an actively engaged development team that is consistently monitoring the library, addressing issues, implementing fixes, and rolling out updates to improve functionality and reliability. The developers behind LemLib are dedicated to ensuring that the library remains at the cutting edge of robotics programming, with frequent updates that introduce new features and optimizations. A clear demonstration of this commitment can be seen in the most recent update to LemLib, which was released just two days ago. This rapid development cycle ensures that users always have access to the latest improvements, bug fixes, and performance enhancements.

Code Update on library decision - FINALIZED

5/10/24

12

CODE - Update on library decision - FINALIZED

LemLib vs EZ-Template

RESEARCH

Furthermore, LemLib is built with modularity and scalability in mind, allowing teams to implement increasingly complex algorithms as their needs evolve. Whether it’s integrating advanced sensor fusion techniques, fine-tuning PID controllers, or optimizing motion profiling, LemLib provides the tools necessary to develop sophisticated and highly optimized autonomous routines. This level of scalability ensures that LemLib can grow alongside our project’s needs, making it a long-term investment in our robotics programming capabilities.

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In conclusion, after carefully weighing all factors—including feature set, development activity, community support, and scalability—we have determined that LemLib is the superior choice for our robotics programming needs. Its advanced functionality, active development team, frequent updates, and strong community support make it the ideal solution for teams looking to push the boundaries of robotics programming. While EZ-Template remains a viable option for those who prefer a more beginner-friendly approach with passive updates, LemLib is unquestionably the better choice for teams that prioritize performance, precision, and continuous innovation. By selecting LemLib, we are ensuring that we have access to the most powerful and up-to-date tools available, positioning our project for success.

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Thus, the final decision is to use LemLib.

Code Update on library decision - FINALIZED

5/10/24

13

CODE - Update on library decision - FINALIZED

LemLib vs EZ-Template

RESEARCH

By contrast, EZ-Template has seen a notable decline in developmental activity, with a far less frequent update schedule. While EZ-Template continues to receive kernel updates, these updates primarily focus on maintaining basic compatibility rather than introducing significant new features or optimizations. The development team behind EZ-Template appears to have shifted towards a more passive maintenance approach, making only minor adjustments as needed rather than actively working on expanding or refining the library. This slower and more passive approach to development makes EZ-Template a less compelling option for teams that require cutting-edge tools and the latest innovations in robotics programming.

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Another factor in our decision-making process was the community support and documentation available for each library. While both LemLib and EZ-Template have active communities, the LemLib community has demonstrated a higher level of engagement in troubleshooting issues, sharing solutions, and providing detailed technical discussions. The extensive documentation and support available for LemLib make it easier for developers to implement advanced functionality and troubleshoot problems efficiently. This is especially important in competitive robotics, where the ability to quickly diagnose and resolve programming challenges can have a significant impact on performance.

Subassemblies List

5/10/24

14

Electronic Components For Subassemblies

Drive base

6/5/2024

15

Decision Matrix - Drive Base

Intake (First Stage)

6/5/2024

16

Decision Matrix - Intake (First stage)

Intake (second stage)

6/5/2024

17

Decision Matrix - Intake (second stage)

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Clamp

6/5/2024

18

Decision Matrix - Clamp

Climb

6/5/2024

19

Decision Matrix - Climb

Doinker

6/5/2024

20

Decision Matrix - Doinker

Drive base (CAD)

6/9/2024

21

Drive base

6/9/2024

22

Specifications:

• 6 motor drive
• 3.25 inch wheels
• 36:48 gear ratio
• 600 RPM motors:
• 450 RPM Drive base
• 1.4 pound drivebase

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Drive Base

After extensive design considerations and rigorous testing, we have decided to implement a concave front drivebase to optimize our robot’s ability to efficiently pick up rings during gameplay. This strategic design choice plays a crucial role in ensuring seamless and rapid intakes, allowing the robot to maximize its scoring potential with minimal resistance. The concave shape acts as a natural funnel, guiding rings directly into the intake mechanism with greater consistency and accuracy, reducing the chances of rings bouncing away or being misaligned. This integration also serves as a structural foundation for the funnel system, ensuring that the intake process is as smooth and reliable as possible.

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Drive base

6/9/2024

23

Drive Base

Beyond its impact on ring collection, our drivebase is carefully engineered to strike a perfect balance between torque and speed, resulting in a well-rounded and adaptable performance during gameplay. A drivebase that is too fast could lead to a loss of control, while one that is too torque-heavy could hinder offensive capabilities.

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Drive base

6/9/2024

24

Drive Base

By finding the optimal balance, our robot is capable of executing a fast and aggressive offensive strategy, swiftly navigating the field and capitalizing on scoring opportunities. At the same time, the carefully calibrated torque ensures that the robot maintains enough pushing power to engage in defensive maneuvers when necessary. This combination allows us to rapidly transition between quick offensive plays and strong defensive holds, making our drivebase an integral part of our overall strategic approach.

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19lb defensive bot

12lb offensive bot

Drive base

6/9/2024

25

Drive Base

To further enhance our robot’s efficiency and longevity during competition, we have chosen to implement “Hot Swaps” for our motor mounts—a design decision aimed at preventing motor overheating and ensuring maximum performance throughout each match. During intense gameplay, motors can generate a significant amount of heat, leading to performance degradation and potential failures if not managed properly.

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Drive base

6/9/2024

26

Drive Base

To counteract this issue, we have developed a system that enables us to quickly swap out overheated motor cartridges for fresh, cool ones in between matches or skill runs. This ensures that the robot is always running at peak efficiency, preventing any drop in speed, torque, or responsiveness due to thermal buildup.

Drive base

6/9/2024

27

Drive Base

By integrating Hot Swaps, we are significantly reducing the risk of motor burnout and inefficiencies, allowing us to maintain a consistently high level of performance over the course of a tournament. This proactive approach also minimizes downtime between matches, ensuring that our robot is always competition-ready and capable of sustaining its peak output in back-to-back games. The ability to maintain optimal motor performance is especially crucial in later rounds of a tournament, where robots that have experienced excessive overheating tend to experience diminishing returns in speed and power.

Drive base

6/9/2024

28

Drive Base

In conclusion, our design choices—a concave front drivebase for optimal ring intake, a balanced torque-speed ratio for versatile gameplay, and Hot Swap motor mounts for sustained peak performance—are all tailored to ensure that our robot operates at the highest possible level of efficiency and effectiveness. Each element of our build is designed with the overarching goal of maximizing competitive performance, ensuring that our robot is prepared to excel in both offensive and defensive plays while maintaining superior endurance throughout an entire competition.

Clamp (CAD)

6/9/2024

29

Clamp (CAD)

6/9/2024

30

This is our stake design meticulously crafted in CAD (Computer-Aided Design), serving as a precise digital blueprint for our robot’s construction. By leveraging CAD, we can accurately position every component before physically assembling the robot, ensuring that all parts fit together seamlessly without unexpected misalignments or inefficiencies. This digital modeling process allows us to visualize the full structure in a 3D environment, providing a comprehensive understanding of spatial relationships, weight distribution, and mechanical constraints.

Clamp (CAD)

6/9/2024

31

One of the key advantages of utilizing CAD for our stake design is that it enables us to anticipate and resolve potential design challenges before manufacturing or assembling physical components.

Clamp (CAD)

6/9/2024

32

Through iterative refinement in the digital space, we can optimize the placement of each part, minimizing interference between moving components and maximizing structural integrity. This proactive approach significantly reduces trial-and-error during the physical build phase, streamlining the assembly process and ensuring a more efficient, precise, and well-executed final product.

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Clamp (CAD)

6/9/2024

33

Additionally, the stake design in CAD allows us to establish a clear, methodical plan for constructing the robot in real life. With a fully developed 3D model as our guide, we can follow a step-by-step assembly process, ensuring that each part is placed with absolute accuracy. This not only enhances build consistency but also improves team coordination, as all members can refer to a single, standardized design reference throughout the building phase. Moreover, the CAD model serves as a foundation for potential future modifications, allowing us to easily adjust dimensions, swap out components, or implement structural reinforcements as needed.

Clamp (CAD)

6/9/2024

34

In conclusion, our stake design in CAD is a critical tool that ensures accuracy, efficiency, and precision throughout the design and assembly process. By creating a fully realized digital prototype, we are able to preemptively address mechanical challenges, develop a well-structured build plan, and optimize the overall functionality of our robot before a single physical part is assembled. This strategic approach not only enhances the quality and reliability of our final build but also sets us up for successful performance in competition by eliminating uncertainties and inefficiencies in the construction phase.

Clamp V1

6/9/2024

35

Clamp locks the Mobile Goal here

Clamp (CAD)

6/9/2024

36

After careful consideration and testing, we have chosen to implement a two-piston Mobile Goal (Mogo) clamp as the primary mechanism for securing possession of the Mobile Goal during gameplay. This design allows us to firmly grip the Mogo, ensuring that we maintain control while simultaneously scoring rings onto it. The clamp mechanism is securely mounted onto our drivebase towers, which serve as a stable support structure for the intake system. By anchoring the clamp to the towers, we ensure that the mechanism remains rigid and structurally reinforced, preventing unnecessary flexing or instability during intense competition.

Clamp (CAD)

6/9/2024

37

Advantages of the Two-Piston Mogo Clamp

✅ Exceptional Strength & Reliability – The dual-piston configuration provides a firm and secure grip on the Mogo, significantly reducing the chances of accidental drops or loss of possession during gameplay. The increased clamping force ensures that the Mogo remains tightly held, even during high-speed maneuvers or defensive engagements.

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✅ Quick & Efficient Operation – The pneumatic system allows for rapid activation and deactivation, enabling us to swiftly claim possession of a Mogo and transition into scoring. This speed is crucial during competitive matches, where every second counts.

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✅ Minimal Mechanical Wear – Unlike motor-driven clamps, which rely on gears and rotational mechanisms that can wear down over time, the pneumatic system has fewer moving parts, reducing long-term maintenance requirements and increasing overall reliability.

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✅ Lightweight Design – Pneumatics provide a high power-to-weight ratio, allowing us to achieve a strong clamping force without the added weight of additional motors or complex mechanical linkages. This helps maintain the agility and speed of our drivebase.

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✅ Simplifies Driver Control – The clamp operates with a single button press, making it easy for the driver to engage and disengage the mechanism without excessive multitasking, allowing for smoother gameplay execution.

Clamp (CAD)

6/9/2024

38

Advantages and disadvantages of the Two-Piston Mogo Clamp

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Clamp (CAD)

6/9/2024

39

Disadvantages of the Two-Piston Mogo Clamp

❌ High Air Consumption – Each use of the clamp consumes a significant amount of air (10-15 PSI per activation). This means that after multiple uses in a match, we risk running low on air pressure, potentially reducing the effectiveness of other pneumatic components on our robot.

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❌ Limited Usability Per Match – Due to air constraints, we must strategically decide when to use the clamp, ensuring we do not deplete our air supply too early in a match. If we run out of air, the clamp may lose gripping strength, which could impact our ability to hold onto the Mogo.

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❌ Potential Air Leaks & Maintenance – Pneumatic systems require careful assembly and maintenance, as air leaks can develop over time, reducing efficiency and requiring frequent troubleshooting to ensure consistent performance.

Clamp (CAD)

6/9/2024

41

Conclusion

Our two-piston Mobile Goal clamp is a powerful and reliable solution for securing possession of the Mogo, enabling us to efficiently score rings and execute strategic gameplay maneuvers. Its strong grip, quick actuation, and low mechanical wear make it an ideal choice for our design. However, the high air consumption and limited number of uses per match are factors that we must strategically manage to maximize our overall performance. Moving forward, we may explore air efficiency optimizations, such as reducing piston stroke length or modifying the clamp design, to minimize air usage while maintaining its strong holding power.

Clamp (CAD)

6/9/2024

42

Two piston clamp:

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Wall Riders

6/9/2024

43

Wall Riders: A Small Yet Crucial Component for Efficient Field Navigation

While seemingly minute in size and function, wall riders play a critical role in our robot’s overall maneuverability and strategic effectiveness. These small but essential components allow for seamless traversal along the field’s perimeter walls, ensuring that our robot can move with minimal resistance and maximum control when positioned near the edges of the field.

Wall Riders

6/9/2024

44

Strategic Importance of Wall Riders in This Year’s Gameplay

For this year’s competition, the majority of key gameplay interactions occur near the corners and edges of the field, making wall-assisted movement a vital factor in optimizing both offensive and defensive strategies.

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Wall Riders

6/9/2024

45

Strategic Importance of Wall Riders in This Year’s Gameplay

Given the frequent need to navigate around the field perimeter—whether to claim Mobile Goals, position for ring scoring, or engage in defensive maneuvers—ensuring our robot can glide smoothly along the walls without unnecessary friction or loss of speed is crucial.

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Wall Riders

6/9/2024

46

Wall riders allow our robot to hug the perimeter efficiently, maintaining tight, controlled turns while minimizing unnecessary resistance that could otherwise slow down movement or cause unwanted misalignment. This enhanced mobility along the walls grants us an edge in positioning battles, particularly when trying to gain control of field corners—one of the most valuable strategic zones in this year’s game.

Wall Riders

6/9/2024

47

Design & Functionality

Our wall rider system consists of free-spinning gears mounted on screw joints, which act as rolling contact points against the perimeter wall. These gears are carefully positioned to ensure continuous, smooth contact with the wall, significantly reducing lateral friction that could otherwise impede movement.

Wall Riders

6/9/2024

48

The key benefits of this design include:

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✅ Reduced Friction & Smoother Movement – The free-spinning gears allow for effortless gliding along the wall, preventing scraping or unnecessary resistance that could slow the robot down.

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✅ Improved Corner Control – With quick and precise maneuverability around corners, we gain a positional advantage over opponents who may struggle with wall movement.

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✅ Increased Driving Stability – By providing a consistent point of contact against the wall, the wall riders help maintain robot alignment, reducing the risk of veering off course when navigating tight spaces.

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✅ Efficient Defensive Positioning – In scenarios where we need to block opponents or hold a strategic location, wall riders allow us to hold our ground with ease, preventing other robots from displacing us while keeping our drivetrain free of unnecessary strain.

Wall Riders

6/9/2024

49

Potential Drawbacks & Considerations

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❌ Space Constraints – Mounting these gears requires dedicated space on the frame, which could limit design flexibility for other subsystems.

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❌ Possible Wear & Tear – Over time, the gears may experience wear due to prolonged contact with the wall, requiring occasional maintenance or replacements.

Wall Riders

6/9/2024

50

Conclusion

Though small in size, wall riders are an integral component of our robot’s movement strategy, ensuring that we can traverse the field quickly and efficiently while remaining competitive in high-traffic areas along the perimeter. By minimizing resistance, enhancing corner control, and maintaining driving stability, this system provides a clear tactical advantage in matches where field positioning is crucial. Moving forward, we will continue to fine-tune their positioning and durability, ensuring they remain a reliable and high-performing aspect of our robot’s design.

Intake (Stage 1) (CAD)

6/9/2024

51

Clamp V2

6/11/2024

52

Switched to a first class level to increase mechanical advantage and use less PSI

Input

Output

Fulcrum

Wall Riders

6/9/2024

20

53

Pneumatic Lifting System: Strengths, Weaknesses, and Optimized Solutions

Our pneumatic lifting system is designed to efficiently elevate a fully stacked Mobile Goal while utilizing a relatively low air pressure range (4-8 PSI). This allows us to conserve our available air supply, ensuring that we can perform multiple lifts throughout a match without the risk of running out of pressure too quickly. The system leverages strategically positioned pneumatic cylinders to generate enough force to securely lift and hold the goal while maintaining a lightweight and efficient overall design.

Wall Riders

6/9/2024

20

54

Advantages of Our Pneumatic Lifting System

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✅ Efficient Air Usage – One of the most significant benefits of this system is that it requires only 4-8 PSI per lift, making it an air-efficient solution compared to other high-pressure pneumatic or motor-driven alternatives. By optimizing our system to function at lower PSI levels, we can maximize the number of activations we can perform within a match.

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✅ Strong Lifting Capability – Despite using low PSI, the system is still capable of lifting a fully stacked Mobile Goal, providing us with a competitive edge in securing and elevating scoring elements during gameplay.

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✅ Lightweight Design – Compared to motorized lifting alternatives, which often require heavier gearing and additional structural support, our pneumatic system remains relatively lightweight, allowing us to preserve speed and agility across the field.

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✅ Quick Activation – The pneumatic lift operates instantly with a single press of a button, allowing for fast deployment and retraction when engaging with Mobile Goals, providing an advantage in fast-paced match scenarios.

Wall Riders

6/9/2024

20

55

Disadvantages & Potential Issues

❌ Vulnerability to Opponents Stealing the Mobile Goal – One of the primary risks associated with this system is that an opponent can potentially steal the goal from our lift if we do not have a secure method of keeping it in place. If another team applies enough force or utilizes a counter-strategy, they may be able to knock or pull the goal out of our grasp, which could result in a loss of points or a strategic setback.

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❌ Dependence on Air Supply – Although the system is efficient in its air consumption, it still relies on a limited air reserve. If our air tanks become depleted during extended gameplay, our lifting mechanism may lose effectiveness, potentially leaving us unable to maintain possession of the goal.

Wall Riders

6/9/2024

20

56

Solution: Implementing a Secure Locking Mechanism

To address the risk of goal theft, we are integrating a locking mechanism that will secure the Mobile Goal in place once lifted. This mechanism will act as an additional safeguard, preventing opponents from easily pulling the goal away from our system.

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How the Locking Mechanism Works:

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A passive or active locking system (such as a latch, hook, or pneumatic brace) will engage once the goal is lifted, ensuring that it remains firmly held in our possession.

The mechanism will only release when intentionally activated by our driver, preventing opponents from forcibly removing the goal during gameplay.

The design will prioritize lightweight materials and efficient actuation, ensuring that it does not interfere with our speed or agility on the field.

Wall Riders

6/9/2024

20

57

Conclusion

By optimizing our pneumatic lift for low PSI usage, we ensure that it remains efficient, strong, and fast-acting while preserving our air supply for the duration of a match. However, recognizing the risk of Mobile Goal theft, we are proactively implementing a locking mechanism to secure our possessions, preventing opponents from easily stealing them. With this added safeguard, our lifting system will be not only powerful and efficient but also highly reliable in competitive scenarios.

Intake(stage 1) V1

6/20/2024

23

58

Specifications:

• 7 inch wide intake
• 5.5 watt motor
• 2:1 gear ratio
• 400 RPM intake

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Had two extra flex-wheels outside of the intake to allow for easy funneling.

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Put the intake on a screw joint to allow it to move freely when a ring is being intook. Facilitates easy transfer between first stage and second stage scoring.

2:1 ratio for increased speed

Extra Flex Wheels to grip on the ring early.

Hooks Scoring (Stage 2) V1

6/28/2024

24

59

Polycarbonate Hooks

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5.5 watt motor direct

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Pros:

• Consistent scoring

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Cons:

• Slow
• Lacks torque

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First + Second stage

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Pros

• Individual subsystem control

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Cons

• Slow, Lacks torque, gets stuck

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Solution

• Use an 11 watt motor for both stages

Wall Stake Scoring V1

7/20/2024

25

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• 5.5 Watt motor lift
• 11 Watt scoring
• Dual sided hooks to score on wall stakes when the lift is up

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Triangle shaped hooks that allow for an easy pickup from first → second stage of intake

Intake 2nd stage

Intake 1st stage

Wall Stake Scoring V2 (CAD)

7/20/2024

26

61

Wall Stake Scoring V2

7/20/2024

27

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• 11 Watt Motor
• 84:12
• Geared for torque

Advanced Ring Scoring Mechanism: Reverse Intake and Precision Placement for Wall Stake Scoring

In our strategically engineered ring scoring system, we utilize a reverse intake method combined with a carefully positioned polycarbonate guide to efficiently redirect rings into our holding mechanism. This process is designed to optimize accuracy, speed, and reliability in scoring rings onto wall stakes or alliance stakes, enabling us to rapidly accumulate points, especially in situations where we need to close a point deficit.

Wall Riders

6/9/2024

20

63

Step 1:

Reverse Intake for Controlled Ring Redirection

Instead of relying solely on a traditional forward-moving intake to transport rings, we intentionally run our intake in reverse, allowing the rings to be directed backwards toward a strategically placed polycarbonate deflector. This polycarbonate piece is precisely angled and positioned to ensure that the rings follow a predictable and controlled trajectory into our designated holding area.

Wall Riders

6/9/2024

20

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Key Advantages of the Reverse Intake & Polycarbonate Guide System:

✅ Consistent Ring Redirection – The polycarbonate deflector ensures that rings are not haphazardly ejected but instead follow a smooth, controlled path directly into our holder.

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✅ Prevention of Ring Jamming – By carefully engineering the reverse intake process, we minimize the risk of rings getting stuck or bouncing unpredictably, ensuring that each ring smoothly enters the scoring mechanism.

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✅ Efficient Use of Space – The polycarbonate guide allows us to redirect rings without additional complex mechanical components, keeping the design lightweight and efficient while conserving valuable space on the robot.

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✅ High-Speed Intake to Holder Transition – Since the intake is actively engaged in reverse, rings are rapidly transported to the holder without unnecessary delays, allowing us to maximize scoring opportunities within a limited match time.

Wall Riders

6/9/2024

20

65

Step 2:

Raising the Bar for Wall Stake Positioning

Once the rings have been successfully redirected into the holding mechanism, the next phase involves precise elevation and alignment for scoring onto the wall stake or alliance stake. This is achieved by raising a pivoting bar mechanism, which is designed to position the rings at the optimal height for scoring.

Wall Riders

6/9/2024

20

66

Key Elements of the Bar-Raising System:

• Controlled, Smooth Lifting Motion – The bar mechanism is designed to elevate with stability, ensuring that the rings remain securely positioned and do not shift during movement.

​

• Alignment Precision – The height and positioning of the bar are finely tuned to ensure that when lowered, the rings are perfectly aligned with the stake, minimizing scoring errors.

​

• Robust and Lightweight Construction – The bar is made from high-strength, lightweight materials, allowing for fast actuation without adding unnecessary weight to the robot.

​

• Quick Transition to Scoring Position – The raising mechanism is designed to efficiently move into place, ensuring that we can score rings quickly and move on to the next objective without wasting time.

Wall Riders

6/9/2024

20

67

Step 3:

Dropping the Bar for Consistent Ring Scoring

Once in position above the wall stake or alliance stake, the final phase of the process involves lowering the bar to deposit the rings onto the scoring stake. This simple yet effective motion ensures a high level of consistency, allowing for repeatable, high-accuracy ring placement.

Wall Riders

6/9/2024

20

68

Strategic Importance: Making Up a Point Deficit

This advanced reverse intake and precision scoring system is not just a mechanical improvement—it is a crucial strategic tool in competitive gameplay. Given the importance of wall stake and alliance stake scoring in this year’s competition, having a fast, reliable, and efficient system for placing rings onto stakes provides a significant advantage in matches where point deficits must be overcome quickly.

Wall Riders

6/9/2024

69

How This System Helps in Competitive Scenarios:

• Comeback Potential – If we fall behind in points, this mechanism allows us to rapidly place multiple rings onto the stakes, efficiently closing the gap within seconds.

​

• Adaptability in Match Situations – Whether we need to build an early lead or stage a late-game comeback, our system provides the flexibility to score effectively at any point during the match.

​

• Minimized Risk of Errors – Because our system is highly automated and precision-engineered, we significantly reduce the likelihood of missed scoring opportunities, ensuring that every ring placement contributes to our point total.

​

• Synergy with Other Strategies – This mechanism can be used in combination with offensive and defensive plays, allowing us to balance ring scoring with other gameplay objectives.

Wall Riders

6/9/2024

20

70

Conclusion: A Game-Changing Ring Scoring System

By integrating a reverse intake, a strategically placed polycarbonate guide, a precision bar-lifting mechanism, and a controlled drop system, we have developed a highly efficient, accurate, and repeatable method for scoring rings onto wall stakes and alliance stakes. This system ensures that we can quickly and reliably place rings while maintaining maximum strategic flexibility throughout a match.

​

With strong engineering principles, precise mechanical design, and a focus on in-game adaptability, our advanced ring scoring system stands as a key asset in our competitive gameplay strategy, allowing us to maintain consistent high-scoring performance and effectively close point deficits when needed.

Elevation (CAD)

7/21/2024

28

71

Full Bot (CAD)

7/22/2024

29

72

Tier 1 Elevation

8/4/2024

30

73

By thoughtfully implementing the principle of Mechanical Advantage (MA) in our design, we were able to significantly reduce the number of motors required to perform essential tasks. This strategic decision not only optimized the efficiency of our system but also ensured that our robot could execute multiple functions seamlessly using a single motor. Specifically, this approach allowed us to climb efficiently using just one motor while also enabling the mechanism to score wall stakes using the very same setup. By integrating these functions into a single, well-engineered mechanism, we minimized the overall weight and complexity of the system, thus reducing energy consumption and maximizing performance.

Tier 1 Elevation

8/4/2024

30

74

To achieve this level of efficiency, we carefully designed a lever system that utilizes two half-cut C-channels, which serve as structural supports. These C-channels facilitate the movement of an 84-tooth gear, which plays a crucial role in pulling down the wall stake mechanism. The placement of this gear was meticulously calculated to ensure that the force exerted on the mechanism would be directed farther away from both the center of mass (indicated by the green arrow) and the fulcrum, thereby enhancing the overall effectiveness of the system.

Tier 1 Elevation

8/4/2024

30

75

This setup fundamentally operates as a Class 2 lever, a type of simple machine that is characterized by the load being positioned between the effort and the fulcrum. In this particular design, the fulcrum serves as the pivot point, while the motor provides the input force (effort) to move the load (the wall stake mechanism). One of the defining characteristics of a Class 2 lever is that it always has a Mechanical Advantage (MA) greater than one. This means that the mechanism is designed to multiply the input force, allowing a relatively small amount of force from the motor to produce a much greater output force at the point of action.

Tier 1 Elevation

8/4/2024

30

76

The benefits of this high Mechanical Advantage are substantial. First and foremost, it reduces the strain on the motor, ensuring that it does not have to work as hard to move the load. This not only extends the longevity of the motor but also significantly conserves battery power, which is a critical factor in ensuring consistent and reliable performance throughout an entire match or operation cycle. Additionally, by reducing the torque load on the motor, the risk of overheating, stalling, or excessive wear and tear is greatly diminished.

Tier 1 Elevation

8/4/2024

30

77

Beyond mechanical efficiency, the implementation of this lever-based design also contributes to greater overall stability and control. Because the force is applied further from the center of mass, the movement of the mechanism remains smooth and predictable, minimizing any unintended shifts in balance. This is particularly important when executing precise tasks such as climbing or scoring wall stakes, where stability is paramount.

Tier 1 Elevation

8/4/2024

30

78

By leveraging these engineering principles, we were able to create a highly efficient, power-conscious, and mechanically optimized system that maximizes performance while minimizing resource consumption. The careful integration of Mechanical Advantage, Class 2 lever mechanics, and structural optimization ensures that our robot functions at peak efficiency while remaining robust, reliable, and strategically advantageous.

Tier 1 Elevation

8/4/2024

31

79

By using two hooks placed on our wall stake mechanism, we can hook onto the first bar on the ladder. By then dropping our wall stake mechanism, we can then achieve tier 1 elevation earning 3 points.

We are an inch of the ground and have achieved Tier 1 Elevation!

Ensuring quick climb from any place in the field is essential because squeezing out a few points can be the decider between winning and losing a match.

Tier 1 Elevation (Ratchet)

8/4/2024

32

80

One problem we ran into is that when power is cut, the gear turns and the robot falls down. To prevent this, we added a piston that pulls a C-channel onto the gear stopping it from moving after time is called. This ensures that the robot stays elevated even when power is cut.

​

​

The piston activation is completely automatic and will not be active while the robot is moving. Only when the program ends will the piston active and bring the C-Channel down. This feature is essential to ensure the ratchet does not activate while trying to use the wall stake. This may break the gear and can cause permanent damage to the robot.

​

​

​

Tier 1 Elevation (Ratchet)

8/4/2024

33

81

Ratchet gears are essential mechanical devices that play a critical role in controlling motion by permitting rotation or linear movement in only one direction while effectively preventing any movement in the opposite direction. This fundamental characteristic makes them indispensable in various mechanical applications, ranging from hand tools and industrial machinery to clocks, winches, and safety mechanisms in lifting equipment.

Tier 1 Elevation (Ratchet)

8/4/2024

33

82

At the core of a ratchet gear system is the ratchet wheel, a uniquely designed toothed wheel that serves as the primary moving component. Each tooth is shaped to allow smooth engagement with a complementary component known as the pawl. The pawl is a small but crucial lever-like structure that interacts directly with the teeth of the ratchet wheel to regulate its movement. It is typically spring-loaded or gravity-assisted to ensure firm engagement with the teeth at all times.

Tier 1 Elevation (Ratchet)

8/4/2024

33

83

When the ratchet wheel rotates in the permitted direction, the pawl rides smoothly over the sloped edges of the teeth, momentarily disengaging before dropping back into the next tooth gap. This design allows the wheel to move forward with minimal resistance, ensuring smooth and controlled motion. The interaction between the pawl and the ratchet wheel is carefully engineered to balance friction, force, and efficiency, enabling seamless movement in the desired direction while maintaining mechanical reliability.

Tier 1 Elevation (Ratchet)

8/4/2024

33

84

However, when an external force attempts to rotate the ratchet wheel in the opposite, restricted direction, the pawl firmly catches against the steep, non-sloped edge of the teeth, thereby locking the mechanism in place. This immediate and secure engagement prevents any backward motion, effectively making the system self-locking in one direction. This characteristic is particularly valuable in applications that require load-holding capabilities, such as hoists, jacks, and tensioning devices, where preventing unintended motion is crucial for both safety and operational stability.

Tier 1 Elevation (Ratchet)

8/4/2024

33

85

The importance of ratchet gears extends far beyond simple one-way motion control. These mechanisms are found in numerous real-world applications, including:

• Hand tools such as wrenches and screwdrivers, where the ratchet mechanism allows users to apply force in one direction while resetting the tool without losing progress.
• Winches and pulleys, where the ratchet prevents loads from slipping backward, ensuring controlled and safe lifting operations.
• Bicycles, where ratchet-based freewheel hubs allow the rider to coast without backpedaling resistance.
• Mechanical clocks and watches, where a ratchet mechanism helps maintain tension in the mainspring, regulating the steady release of energy.
• Tensioning devices, such as tie-down straps and cable pullers, where ratchet gears enable incremental tightening while preventing slippage.

Tier 1 Elevation (Ratchet)

8/4/2024

33

86

From a mechanical engineering standpoint, the design and material selection of ratchet gears and pawls are critical to their performance. These components must be made from high-strength metals or durable composite materials to withstand repeated stresses, high loads, and continuous friction. Proper lubrication and maintenance are also necessary to ensure long-term reliability and prevent excessive wear on the teeth and pawl mechanism.

Tier 1 Elevation (Ratchet)

8/4/2024

33

87

In summary, ratchet gears are a vital component in mechanical systems where controlled motion, safety, and unidirectional operation are essential. Their simple yet highly effective design provides a reliable, low-maintenance solution for countless industrial, automotive, and everyday applications. By enabling motion in one direction while preventing reverse movement, ratchet mechanisms enhance efficiency, improve user experience, and ensure the stability and security of numerous machines and devices.

AUTON STRATEGY - Problem and Goal

08/07/2024

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88

AUTON STRATEGY - Problem and Goal

GOAL: Get an autonomous win point

REQUIREMENTS:

• At least three (3) Scored Rings of the Alliance's color
• A minimum of two (2) Stakes on the Alliance's side of the Autonomous Line with at least (1) Ring of the Alliance's color Scored
• Neither Robot contacting / breaking the plane of the Starting Line
• At least One (1) Robot contacting the Ladder

Starting lines are highlighted in green on the image below:

There is a side (top) with 2 goals and a side (bottom) with 3 goals. Our first priority is to perfect our auton on the 3-goal side, as that allows us to solo AWP, which isn’t possible with the top side. With max efficiency + optimal route!

TO DO NEXT: Make rough diagram of where robot should go.

^ on next slides.

AUTON STRATEGY - Rough Plan Diagram

08/07/2024

35

89

AUTON STRATEGY ROUTE 1 - Rough Plan Diagram

Steps of what to go to in order:

2

3

1

1. starting position
2. grab mobile goal from back side of robot
3. Grab bottom ring and drop mobile goal
4. Grab the second mobile goal from back side of robot
5. Grab top ring
6. Touch climb bar at the end

4

5

6

AUTON STRATEGY - Rough Plan Diagram

08/07/2024

36

90

AUTON STRATEGY ROUTE 2 - Rough Plan Diagram

Steps of what to go to in order:

• starting position
• grab mobile goal from back side of robot
• Grab bottom ring and drop mobile goal
• Grab the second mobile goal from back side of robot
• Grab top ring
• Touch climb bar at the end

2

3

1

4

5

6

AUTON STRATEGY - Rough Plan Diagram

08/07/2024

91

AUTON STRATEGY ROUTE 3 - Rough Plan Diagram

Steps of what to go to in order:

2

4

1

• starting position (arrow pointing to where robot starts facing)
• grab goal from back side and score preload
• grab the 2 rings on bottom of left stack
• grab the ring on the bottom of the stack
• Grab the ring from the top of the stack
• touch climb bar at the end

3

5

AUTON STRATEGY - Rough Plan Diagram

08/07/2024

92

AUTON STRATEGY ROUTE 4 - Rough Plan Diagram

Steps of what to go to in order:

2

3

1

• starting position (arrow pointing to where robot starts facing)
• grab goal from back side and score preload
• grab the 2 rings on bottom of left stack
• grab the ring on the bottom of the stack
• Grab the ring from the top of the stack
• touch climb bar at the end

4

5

6

Final Weigh In

8/8/2024

93

Lowering weight ensures the robot traverses the field efficiently and quickly. This facilitates fast game play and decreased cycle times, resulting in higher score contributions. Additionally, it allows for greater acceleration, allowing us to get the most of our 6 motor drive.

Sources of weight saving:

• Nylon Screws
• Nylon Nuts
• Efficient design
• Avoided steel

Goal weight

• 14.5 pounds

​

Final weigh-in

• 14.31 pounds.

AUTON CHANGE REPORTS

08/16/2024

40

94

AUTON+DRIVER MOVEMENT/ROUTE TESTING�PID for autonomous movement

TODO:

Finish tuning angular PID and tune lateral PID

TASK:

Begin PID tuning to balance responsiveness and accuracy for autonomous control.

What is PID?

• Proportional, Integral, Derivative.

Uses closed-loop feedback to correct motor positioning.

• Proportional: Deviation from current position and target.
• Integral: Accumulation of deviation over time (we didn’t need to account for this)
• Derivative: Change in deviation.

​

Using these measurements (We used P D) and tuning constants allows for smooth and accurate motor/position control during the autonomous period.

WHAT WE DID:

We followed the guide from LemLib (the C++ library we used for autonomous) about PID tuning.

​

We began tuning angular PID, for rotating the bot.

We repeated the process until we got smooth movement:

1. Does it oscillate? If yes: 2, if no: 3
2. Record proportional derivative coefficient, increase it. Go to 1.
3. Increase derivative coefficient. Go to 1.

​

To use PID we had to attach an inertial sensor, which we placed low to the ground.

AUTON CHANGE REPORTS

08/20/2024

41

95

WHAT WE DID:

We continued to follow the guide about PID tuning to finish angular motion tuning.

​

We ran into issues, such as oscillations happening upon slight variable changes. We were focusing a lot on the details, as we wanted to make the robot’s movement as smooth as possible.

AUTON+DRIVER MOVEMENT/ROUTE TESTING�PID for autonomous movement

TODO:

Finish tuning angular PID and tune lateral PID

TASK:

Finish tuning angular PID

AUTON CHANGE REPORTS

08/21/2024

42

96

WHAT WE DID:

We continued to follow the guide about PID tuning to finish angular motion tuning.

​

We moved on to lateral PID tuning.

​

Once again, we followed the steps in the guide:

​

We repeated the process until we got smooth movement:

1. Does it oscillate? If yes: 2, if no: 3
2. Record proportional derivative coefficient, increase it. Go to 1.
3. Increase derivative coefficient. Go to 1.

​

We still had some tuning left to do.

AUTON+DRIVER MOVEMENT/ROUTE TESTING�PID for autonomous movement

TASK:

Tune lateral PID

TODO:

Continue Lateral PID Tuning

AUTON CHANGE REPORTS

08/22/2024

43

97

WHAT WE DID:

We continued to follow the guide about PID tuning to finish LATERAL motion tuning.

​

We continued tuning, but were not able to finish. The robot was not getting to exactly where we wanted it, and despite changing variable values throughout our work time, we had to push it to finish on the next work time.

AUTON+DRIVER MOVEMENT/ROUTE TESTING�PID for autonomous movement

TODO:

Finish Lateral PID Tuning

TASK:

Continue tuning lateral PID

AUTON CHANGE REPORTS

08/23/2024

44

98

WHAT WE DID:

We continued to follow the guide about PID tuning to finish lateral motion tuning.

​

We adjusted values further and found values that worked great. We started testing robot movement.

AUTON+DRIVER MOVEMENT/ROUTE TESTING�PID for autonomous movement

TASK:

Finish tuning lateral PID

MOVEMENT TESTING

We coded the robot to move forward by a certain amount, but we ran into a problem where it would move all the way across the field and not stop.

Try again…

We had forgotten to change back a speed value in our code. We fixed this and continued testing movement.

​

We re-ran the route and it worked, and the robot was able to move different distances accurately.

TODO:

Create short test route to test motion further

AUTON CHANGE REPORTS

08/28/2024

45

99

We created a sample route which did the following:

AUTON+DRIVER MOVEMENT/ROUTE TESTING�autonomous movement

TASK:

Create short test route to test motion further

1: move towards stake / mobile goal

2: clamp (grab it)

3: move towards ring

4: score ring

TODO:

Start actual auton routes

AUTON CHANGE REPORTS

08/30/2024

46

100

AUTON ROUTE PROGRAMMING�Autonomous route: Positive Red

TASK:

Auton Routes Positive Red

Route step: Clamp stake

What happened: It didn’t move far enough

Where it moved to:

We want

We then fixed the move position

AUTON CHANGE REPORTS

08/30/2024

47

101

AUTON ROUTE PROGRAMMING�Autonomous route: Positive Red

TASK:

Auton Routes Positive Red

Route step: Clamp stake

It spun around, then moved, then spun. We needed it to move without spinning too much.

We then fixed the headings.

AUTON CHANGE REPORTS

08/30/2024

48

102

AUTON ROUTE PROGRAMMING�Autonomous route: Positive Red

TASK:

Auton Routes Positive Red

Route step: Clamp stake

We fixed rotation and it faced the stake but didn’t move towards it far enough.

Where it stopped

Where it should be touching

Next: Fix position

AUTON CHANGE REPORTS

08/30/2024

49

103

AUTON ROUTE PROGRAMMING�Autonomous route: Positive Red

TASK:

Auton Routes Positive Red

Route step: Clamp stake

We updated the position and it was closer but still didn’t touch.

Where it stopped

Where it should be touching

Then, we fixed position and it clamped the mobile goal:

However, it was past the line (shown in white)

AUTON CHANGE REPORTS

08/30/2024

50

104

AUTON ROUTE PROGRAMMING�Autonomous route: Positive Red

TASK:

Auton Routes Positive Red

Route step: Clamp stake

We updated the code many times and kept having issues with the robot being misaligned with the stake. This was because the area where the stake had to go was just enough for the stake, so we would have to align our robot just right.

Collision pushing goal back

This wrapped up our meeting for this day. Next meeting, our todo is to fix the route positioning and alignment.

AUTON CHANGE REPORTS

08/30/2024

51

105

AUTON+DRIVER MOVEMENT/ROUTE TESTING�PID for autonomous movement

TASK:

Auton Routes

• starting position
• grab mobile goal from back side of robot
• Grab bottom ring, score, drop mobile goal
• Grab the second mobile goal from back side of robot
• Grab top ring, score
• Touch climb bar at the end

2

3

1

4

5

6

This side’s routes remain the same.

We changed up the red positive route slightly as shown on the right →

The change was due to the field update.

(Shown is for red. Blue is just mirrored.)

​

This route would be much more efficient and avoids going between a goal and ring stack, which could be knocked over and mess up routes.

Mirror the route on ← that side.

Strategy (Solo AWP) - Offensive play

9/1/2024

52

106

If we are the red alliance and we use our Solo AWP autonomous route, we will be on the bottom left rung of the truss.

​

From here, we will have one mobile goal with one ring scored. Grabbing the two rings under the truss will fill the goal up to 3 and we can then move to the positive corner to establish control.

​

From there we will coordinate with our alliance member and ensure we maintain goal control and prioritize top ring scoring on the wall stakes.

Strategy (Single Mobile Goal Auton) - Offensive play

9/1/2024

53

107

If we use our second auton route as the red alliance, we will be scoring 4 rings on the top left mobile goal. This will NOT give us solo AWP but is essential for winning the autonomous bonus for winning the overall match.

Based on our alliance’s auton, we will either rush to get to the positive corner or fill the goal completely and switch with our alliance at the optimal time.

​

Once again, we will emphasize wall stake play and specifically top ring control.

Strategy - Defensive play

9/1/2024

54

108

If we are delegated the role of playing defense, our priority will be maintaining control of the positive corner. Once the positive corners are protecting in the last 15 seconds of the game, we will move to try and block teams from hanging and ensure we maintain top ring control on wall stakes.

Additionally, we will ensure to avoid one of our mobile goals ending up in the negative corner by protecting the mobile goals not placed in the positive corner.

Strategy - Up Points

9/1/2024

55

109

Assuming we either win the autonomous bonus or have a commanding lead in points, our strategy will become more conservative. Rather than scoring, we will be trying our best to inhibit the opponent's ability to score.

By strategically pinning the opponents’ robots when trying to score wall stakes or when trying to abandon their corner will stymie their score.

This will encourage a risky play which we will then take advantage of.

• Scoring our rings to push into the negative corner
• Give up positive corner to score rings

These tasks can be taken advantage of and add to our score.

Strategy - Down Points

09/02/2024

56

110

Assuming we either lose the autonomous bonus or are down many rings at the start of the match, our strategy will be to play more aggressively.

We would need to ensure top ring control at all times on the wall stakes and ensure the opponents are unable to control 3 mobile goals.

If wall stake control is failing, we would resort to filling one goal with the opponent’s rings and camping a corner.Despite this strategy being much more risky, it is necessary to make up for the initial dip in points

Because our focus points increase drastically, there is much more room for error. But this risk is necessary to pull ahead in points.

AUTON CHANGE REPORTS

​

09/03/2024

57

111

AUTON+DRIVER MOVEMENT/ROUTE TESTING�Motion Profiling for Smoother Acceleration

What is the problem?�During our robot’s movements, we encountered significant instability due to abrupt changes in speed, particularly during acceleration and deceleration phases. These sudden jumps in velocity caused jerky, unpredictable movements, which in turn affected overall performance and control. The issue was most noticeable when transitioning between different speeds or changing directions quickly, as inertia would cause the robot to lurch forward or skid to a stop. This instability posed challenges in maintaining precise control, reducing efficiency, and increasing wear on the drivetrain components over time.

AUTON CHANGE REPORTS

​

09/03/2024

57

112

AUTON+DRIVER MOVEMENT/ROUTE TESTING�Motion Profiling for Smoother Acceleration

�⁠What we changed:�To address these issues, we implemented motion profiling, a control strategy that smooths out acceleration and deceleration transitions by applying a more gradual speed curve. Instead of allowing the robot to instantly jump from one speed to another, motion profiling ensures that acceleration occurs progressively, reducing sudden force changes that contribute to instability. This was achieved by:

1. Smoothing Acceleration and Deceleration Curves

• Instead of instantaneously increasing speed to a set value, we introduced a gradual ramp-up approach that progressively increases acceleration to prevent lurching forward.

• Similarly, instead of an immediate stop, we implemented controlled deceleration that allows the robot to ease into a stop rather than halting suddenly.

2. Adjusting Minimum and Maximum Speeds

• We fine-tuned the upper and lower speed limits to prevent situations where the robot would suddenly shift between drastically different speeds.

• By defining speed thresholds, we ensured that transitions between movement phases were more controlled and consistent.

3. Reducing the Impact of Directional Changes

• We refined speed modulation when switching between forward and reverse movements to prevent instability during rapid directional shifts.

• A controlled transition between opposing forces helped prevent abrupt torque spikes that could lead to slipping or erratic movement.

AUTON CHANGE REPORTS

​

09/03/2024

57

113

AUTON+DRIVER MOVEMENT/ROUTE TESTING�Motion Profiling for Smoother Acceleration

�⁠Why it will work�To address these issues, we implemented motion profiling, a control The key principle behind motion profiling is the reduction of the impact of inertia when changing speeds. When a system undergoes sudden acceleration or deceleration, inertia acts against the change, creating instability and erratic movements. By implementing a smoother transition of forces, we were able to:

• Enhance stability: More controlled motion reduces sudden shifts in weight distribution, allowing for smoother navigation.

• Improve precision: Reducing jerky motions enables better path following, particularly in autonomous routines.

• Minimize mechanical stress: A gradual change in forces prevents unnecessary strain on motors, gears, and structural components, prolonging their lifespan.

AUTON CHANGE REPORTS

​

09/03/2024

57

114

AUTON+DRIVER MOVEMENT/ROUTE TESTING�Motion Profiling for Smoother Acceleration

�⁠Did it work:�Yes, the implementation of motion profiling significantly improved movement smoothness, particularly during acceleration and deceleration. The robot exhibited far fewer instances of lurching forward or skidding to a stop, which contributed to overall stability and control. However, while the changes were effective in mitigating major speed inconsistencies, further fine-tuning is still required, especially in situations where the robot needs to make quick directional shifts.

• Areas for Further Optimization:

• Additional tuning of acceleration/deceleration curves to further refine transitions.

• Refinement of speed thresholds to find the optimal balance between smooth motion and responsiveness.

• Continued testing in different movement scenarios to ensure consistency across all operating conditions.

AUTON CHANGE REPORTS

​

09/03/2024

57

115

AUTON+DRIVER MOVEMENT/ROUTE TESTING�Motion Profiling for Smoother Acceleration

TODO:

Test route optimization using motion profiling for more complex paths.

�⁠Overall:�By integrating motion profiling, we successfully addressed the core issue of abrupt speed changes, leading to a more stable and controlled movement system. While the current implementation has already shown notable improvements, further iterative refinements will help achieve even greater precision, responsiveness, and efficiency in our robot’s mobility.

AUTON CHANGE REPORTS

​

09/04/2024

58

116

AUTON+DRIVER MOVEMENT/ROUTE TESTING�Route Optimization Testing

What is the problem?�One of the key challenges we faced was inefficient pathing, which resulted in unnecessary delays and difficulty completing objectives within strict time constraints. The robot’s movement was not optimized, often taking longer routes than necessary, making excessive turns, or covering extra distance that could have been avoided. These inefficiencies compounded over time, making it difficult to achieve consistent and reliable performance, especially in time-sensitive tasks.

​

Additionally, when navigating sharp turns or intricate paths, the robot struggled with maintaining control at high speeds. This led to overshooting, increased travel time, and difficulty executing precise movements. These issues were particularly problematic in autonomous navigation, where small inefficiencies could result in cumulative delays that impacted the overall strategy.

AUTON CHANGE REPORTS

​

09/04/2024

58

117

AUTON+DRIVER MOVEMENT/ROUTE TESTING�Route Optimization Testing

What we changed:�To address these challenges, we implemented route optimization techniques that streamlined movement efficiency. Our approach focused on reducing unnecessary turns, minimizing total travel distance, and improving the robot’s ability to handle complex paths without sacrificing control. The main improvements included:

• Optimizing Route Sequences

• We restructured movement commands using the moveToPoint function to create more direct paths between key objectives.

• The system now calculates the most efficient way to reach a target while avoiding unnecessary detours.

• Redundant waypoints and inefficient turning angles were removed to create smoother, more logical movement paths.

• Minimizing Travel Distance

• By analyzing and refining the robot’s movement strategy, we were able to eliminate excess travel, reducing the overall distance covered.

• The refined paths ensure that objectives are reached in less time, increasing efficiency and improving overall performance in time-restricted tasks.

• Adjusting Maximum Speed for Complex Turns

• A significant challenge in path optimization was maintaining control when navigating tight turns or sudden direction changes.

• To address this, we introduced adaptive speed control, setting a lower maximum speed when executing complex turns to prevent overshooting and instability.

• This adjustment allowed the robot to maintain better traction and control, reducing errors caused by excessive momentum.

AUTON CHANGE REPORTS

​

09/04/2024

58

118

AUTON+DRIVER MOVEMENT/ROUTE TESTING�Route Optimization Testing

Why would it work?�Optimizing the movement path enhances overall efficiency by reducing unnecessary travel time and ensuring that each movement is as direct and effective as possible. By refining how the robot moves, we achieved:

• Reduced total distance traveled → Faster objective completion and improved time management.

• Better handling of sharp turns → Improved stability, fewer corrections, and more consistent performance.

• Increased overall precision → More reliable execution of tasks, especially in complex autonomous sequences.

​

Additionally, slowing down at critical points in the route prevents instability and ensures smoother transitions between movements. This combination of route optimization and speed adjustments helps create a more controlled, reliable, and efficient navigation system.

AUTON CHANGE REPORTS

​

09/04/2024

58

119

AUTON+DRIVER MOVEMENT/ROUTE TESTING�Route Optimization Testing

Did it work (why or why not)?�The implementation was partially successful. While the improvements in route efficiency were clear, and the robot was able to complete tasks more quickly, we identified additional areas that require fine-tuning:

• Smooth Transitioning at Higher Speeds: While lowering max speed for turns helped with control, the transition between different speed zones needs refinement to avoid abrupt slowdowns.

• Further Path Refinement: Some movement sequences can still be optimized for even greater efficiency.

• Balancing Speed and Stability: Finding the ideal speed settings for both straight paths and complex maneuvers is an ongoing challenge.

AUTON CHANGE REPORTS

​

09/04/2024

58

120

AUTON+DRIVER MOVEMENT/ROUTE TESTING�Route Optimization Testing

TODO:

Integrate field element alignment to ensure precise positioning during autonomous actions.

Overall: �By optimizing movement paths with moveToPoint and adjusting speed settings for complex turns, we successfully reduced unnecessary travel time and improved efficiency. However, additional refinements are needed to further smooth out speed transitions and maximize stability at higher speeds. Continued testing and fine-tuning will help us achieve the perfect balance between speed, control, and precision, leading to even more effective navigation.

AUTON CHANGE REPORTS

​

09/05/2024

59

121

AUTON+DRIVER MOVEMENT/ROUTE TESTING�Alignment with Field Elements

What is the problem?�One of the major challenges we faced was the robot’s inability to consistently align with field elements, such as goals, markers, or scoring zones. Misalignment led to decreased accuracy in scoring and task execution, causing delays and inconsistencies in performance.

​

Several issues contributed to this misalignment:

1. Inconsistent Approach Angles: The robot often approached field elements at slightly different angles each time, making precise interactions difficult.

2. Overcorrections and Instability: When attempting to adjust its position, the robot would sometimes make excessive corrections, overshooting the target and requiring further adjustments.

3. Lack of Stability Before Interactions: Without a pause to stabilize after movement, the robot would attempt to perform actions (like scoring or placing game elements) while still in motion, leading to inaccurate placements.

​

These factors combined to create inconsistent performance, making it difficult to reliably execute tasks that required precise positioning.

AUTON CHANGE REPORTS

​

09/05/2024

59

122

AUTON+DRIVER MOVEMENT/ROUTE TESTING�Alignment with Field Elements

What we changed:�To solve this issue, we introduced a structured alignment system that ensured the robot reached field elements at consistent angles with improved accuracy. This was accomplished through:

1. Adding Alignment Checkpoints with moveToPoint:

• Instead of navigating directly to the field element in a single movement, we introduced intermediate alignment checkpoints to guide the robot into an optimal approach position.

• These checkpoints help break down complex movements into smaller, controlled steps, preventing overshooting or erratic approach angles.

2. Using turnToHeading for Precise Angle Control:

• By implementing turnToHeading, we ensured that the robot consistently faced the correct direction before interacting with field elements.

• This eliminated variations in approach angles, making scoring and other actions far more reliable.

3. Introducing Time Delays at Checkpoints for Stability:

• A brief pause after reaching an alignment checkpoint allows the robot to stabilize before proceeding to interact with the field element.

• This prevents premature actions (such as scoring while still in motion) and improves accuracy by ensuring the robot is fully positioned before making critical movements.

AUTON CHANGE REPORTS

​

09/05/2024

59

123

AUTON+DRIVER MOVEMENT/ROUTE TESTING�Alignment with Field Elements

Why would it work?�By incorporating alignment checkpoints, precise angle control, and stabilization delays, we created a system that ensures consistent and repeatable approach paths to field elements. The key benefits include:

• More Accurate Positioning: Checkpoints guide the robot to the best possible location before interacting with field elements.

• Improved Scoring Precision: Ensuring the robot is properly aligned before scoring increases the success rate of tasks like placing game elements in specific zones.

• Greater Stability and Control: Time delays at checkpoints allow the robot to settle into its position before making a move, reducing errors caused by unnecessary motion.

• Elimination of Overshooting and Overcorrections: Breaking down movement into smaller, controlled steps prevents excessive adjustments that could throw off accuracy.

AUTON CHANGE REPORTS

​

09/05/2024

59

124

AUTON+DRIVER MOVEMENT/ROUTE TESTING�Alignment with Field Elements

Did it work (why or why not)?�Yes, alignment improved significantly, leading to far more accurate interactions with field elements. The robot now consistently approaches targets from the correct angle, and the stabilization delays ensure that it is properly positioned before executing tasks.

​

However, there are still areas for further refinement:

• Fine-tuning time delays: While pauses at checkpoints helped stabilize movement, optimizing these delays to be as short as possible without sacrificing accuracy could improve efficiency.

• Dynamic adjustments for varying field conditions: External factors such as field obstacles or robot drift over time might require additional logic to adjust alignment in real time.

• Further testing at higher speeds: While alignment improved, additional testing is needed to ensure consistency at different movement speeds.

AUTON CHANGE REPORTS

​

09/05/2024

59

125

AUTON+DRIVER MOVEMENT/ROUTE TESTING�Alignment with Field Elements

TODO:

Assess performance stability under varying payload weights.

Overall:�By implementing alignment checkpoints, precise angle adjustments, and stabilization pauses, we successfully improved the robot’s ability to interact with field elements. These changes have enhanced scoring accuracy, improved movement consistency, and reduced errors caused by misalignment. While further refinements can be made to optimize efficiency, this approach has already led to more reliable and repeatable performance in competitive scenarios.

AUTON CHANGE REPORTS

​

09/06/2024

60

126

AUTON+DRIVER MOVEMENT/ROUTE TESTING�Performance Stability with Varying Payloads

What is the problem?�One of the key challenges we encountered was the robot’s stability being affected by changes in payload weight. When the robot carried different objects or loads, it experienced noticeable performance issues, particularly in:

• Stability During Turns:

• The additional weight caused momentum shifts, making the robot more prone to oversteering or understeering during turns.

• Heavier payloads altered the center of mass, making turns less predictable and harder to control.

• Acceleration and Deceleration Instability:

• Rapid acceleration with an increased payload resulted in slippage or unintentional wheel spin, reducing control.

• Deceleration with a heavier load sometimes caused the robot to overshoot its stopping position, leading to less precise movements.

• Inconsistent Performance Across Different Payloads:

• The robot was originally tuned for a specific weight, meaning any variation (such as carrying game elements) caused fluctuations in movement precision.

• Without automatic adjustments, the robot would behave differently each time depending on its load, making it difficult to execute pre-planned paths reliably.

​

Since many game scenarios require a robot to pick up, transport, and deposit objects, addressing this issue was critical to ensuring smooth, predictable movements regardless of payload weight.

AUTON CHANGE REPORTS

​

09/06/2024

60

127

AUTON+DRIVER MOVEMENT/ROUTE TESTING�Performance Stability with Varying Payloads

What we changed:�To counteract the effects of changing payload weight, we introduced adaptive control mechanisms that dynamically adjust movement parameters based on detected weight changes. Our key modifications included:

• Tuning PID Parameters for Variable Loads:

• Proportional-Integral-Derivative (PID) control is essential for maintaining smooth motion and stability.

• We modified P, I, and D values to compensate for additional weight, preventing instability in movement.

• This allowed the robot to apply stronger or weaker corrections based on the load, ensuring consistent turning and stopping behavior.

• Modifying Maximum Speeds for Stability:

• We introduced an adjustable max speed limit that adapts to payload weight.

• This prevented excessive acceleration that could cause skidding or loss of control when carrying heavy objects.

• Speed adjustments were especially critical for high-speed turns, where momentum shifts had previously led to instability.

• Implementing an Adaptive Error Range for Control Feedback:

• We introduced an error range that automatically adjusts based on weight changes.

• Instead of using a fixed precision threshold, this system dynamically modifies tolerances, ensuring that even with varying loads, the robot remains stable while still allowing for minor fluctuations.

AUTON CHANGE REPORTS

​

09/06/2024

60

128

AUTON+DRIVER MOVEMENT/ROUTE TESTING�Performance Stability with Varying Payloads

Why would it work?�By dynamically adjusting movement parameters, we created a system that ensures consistent and predictable robot behavior, regardless of payload weight. Key benefits include:

• Improved Stability During Turns:

• PID tuning prevents excessive corrections, allowing for smooth, controlled rotations even with additional weight.

• Consistent Acceleration and Deceleration:

• Adjusting speed limits prevents overcompensation, ensuring precise stopping positions and controlled acceleration.

• More Reliable Performance Across Different Payloads:

• The adaptive error range ensures smooth motion adjustments instead of rigid, fixed responses.

• The robot now handles both light and heavy loads effectively, improving overall consistency.

AUTON CHANGE REPORTS

​

09/06/2024

60

129

AUTON+DRIVER MOVEMENT/ROUTE TESTING�Performance Stability with Varying Payloads

Did it work (why or why not)?�Yes, the robot successfully maintained stability even when carrying additional weight. The improved PID tuning and adaptive speed control allowed it to navigate turns, accelerate, and decelerate smoothly without the erratic behavior previously observed.

​

However, some minor trade-offs and areas for improvement remain:

• Slight Reduction in Overall Speed:

• While stability improved, the robot’s top speed was slightly lower under heavier loads due to safety constraints.

• Further testing could refine the balance between stability and speed for optimal performance.

• Potential for Real-Time Load Detection:

• While our adjustments were effective, adding real-time load sensors could allow for even faster dynamic tuning instead of relying on preset adjustments.

• Fine-Tuning for Extreme Payload Variations:

• The system performed well within expected weight ranges, but additional testing is needed to ensure stability under very heavy or very light loads.

AUTON CHANGE REPORTS

​

09/06/2024

60

130

AUTON+DRIVER MOVEMENT/ROUTE TESTING�Performance Stability with Varying Payloads

TODO:

Examine timing issues

Overall:�Through adaptive PID tuning, speed modifications, and dynamic error range adjustments, we successfully improved the robot’s ability to maintain stability despite payload changes. The system now provides more controlled movements, smoother turns, and more predictable behavior across different game conditions. While small refinements can still be made, these improvements have already enhanced consistency and reliability, making the robot more adaptable in real-world gameplay scenarios.

AUTON CHANGE REPORTS

​

09/09/2024

61

131

AUTON+DRIVER MOVEMENT/ROUTE TESTING�Timing Issues

What is the problem?�One of the key challenges we encountered in our autonomous routines was the inefficiency and inconsistency of executing complex tasks. The primary issues were:

• Overreliance on Separate Commands:

• The autonomous code was structured with multiple independent commands that executed sequentially.

• This approach caused delays between actions, leading to jerky movements and a lack of fluidity.

• Timing Issues Between Transitions:

• Since each command ran separately, even small timing variations could compound into larger inconsistencies throughout the routine.

• This often led to misalignment, late executions, or missed commands, especially when precise movements were required.

• Positional Drift Over Multiple Steps:

• Because each action was triggered individually, small positional errors accumulated over time.

• Even a minor deviation in one step could cause misalignment in later steps, affecting the success rate of autonomous tasks.

​

Since autonomous mode is a critical component of competitive robotics, ensuring precision, efficiency, and reliability in our routines was essential.

AUTON CHANGE REPORTS

​

09/09/2024

61

132

AUTON+DRIVER MOVEMENT/ROUTE TESTING�Timing Issues

What we changed:�To resolve these issues, we focused on improving speed control, transition timing, and error margins to create smoother, more consistent autonomous sequences. Key changes included:

• Adjusting Speeds for Smoother Transitions:

• Instead of abrupt starts and stops between commands, we optimized speeds within each motion phase.

• This ensured that movements were fluid and continuous, reducing jerky transitions and unnecessary delays.

• Implementing Delays and Error Margins for Precision:

• We fine-tuned timing parameters to prevent commands from overlapping or interfering with one another.

• Introduced adaptive error margins, allowing the robot to correct minor deviations on the fly without getting stuck.

• Extensive Testing to Improve Positioning Consistency:

• We ran multiple test iterations to refine movement accuracy.

• Adjustments were made based on real-world performance data, ensuring that the robot reliably reached target positions in every run.

AUTON CHANGE REPORTS

​

09/09/2024

61

133

AUTON+DRIVER MOVEMENT/ROUTE TESTING�Timing Issues

Did it work (why or why not)?�By optimizing speeds, refining transitions, and compensating for positional errors, we achieved more reliable and efficient autonomous execution. Key benefits include:

• More Fluid Movement:

• Smoother acceleration and deceleration eliminate unnecessary pauses, leading to more efficient execution.

• Reduced Positional Drift:

• Improved transition accuracy ensures that each movement starts from the correct position, minimizing long-term drift.

• Increased Success Rate in Task Execution:

• Consistent positioning allows the robot to complete multi-step actions more reliably, improving overall performance.

AUTON CHANGE REPORTS

​

09/09/2024

61

134

AUTON+DRIVER MOVEMENT/ROUTE TESTING�Timing Issues

Overall:�Through improved motion profiling, transition optimization, and extensive testing, we successfully enhanced the fluidity and reliability of our autonomous routines. While there are still minor refinements to be made, these changes have already increased efficiency, reduced execution errors, and improved positioning consistency, making our autonomous mode more competitive and effective in real-world scenarios.

TODO:

Color sort macro

Technical Issues

9/10/2024

62

135

AUTON CHANGE REPORTS

09/13/2024

63

136

AUTON ROUTE PROGRAMMING�Autonomous route: Positive Red

TASK:

Auton Routes Positive Red

Route step: Clamp stake

We moved towards the rings and got a good position for intaking. We needed to increase the time we intook for.

ring

AUTON CHANGE REPORTS

09/13/2024

64

137

AUTON ROUTE PROGRAMMING�Autonomous route: Positive Red

TASK:

Auton Routes Positive Red

Route step: Clamp stake

We updated our route to follow our revamped plan.

The currently coded movements are as shown

←

clamp

However, the bot bumped into the stake once again.

← We fixed positioning and it clamped. However this was not consistent, as sometimes it wouldn’t clamp.

AUTON CHANGE REPORTS

10/01/2024

65

138

DRIVE PROGRAMMING�Intake Color Macro

TASK:

We wanted to create a macro for during the driver period where if a ring is intaken and it is the opposite team’s color, it would flick it off, but if it was our team it would score it.

Assuming we are on red team…

​

Intaking the red →

(it scored)

Intaking the blue →

(it flicked it off like we wanted)

Skills Routes - Rough Plan

10/6/2024

66

139

SCORE BREAKDOWN:

​

20 - Mogos in corner (5x4)

12 - Top ring on wall/alliance stakes (3x4)

16 - Full scored Mogos(8x2)

7 - 5 rings scored Mogo(7x1)

3 - Hang

__________________________________

= 58 total

Max = 116 combined skills

AUTON CHANGE REPORTS

10/08/2024

67

140

AUTON ROUTE PROGRAMMING�Autonomous route: Positive Red

TASK:

Auton Routes Positive Red

Route step: Clamp stake, score, next stake

clamp

We continued working on the autonomous route.

The robot clamped onto the stake then scored the preload:

← as well as the intaken ring

next, we need to work on the rest of the route: clamp 2nd stake, score, touch ladder.

AUTON CHANGE REPORTS

​

10/14/2024

68

141

AUTON+DRIVER MOVEMENT/ROUTE TESTING�Skills Route Initialization

What is the problem?�When first setting up the skills route, we encountered several challenges that affected the consistency and accuracy of the robot’s performance. The main issues included:

• Inconsistent Execution of Tasks:

• The robot’s movements were not always precise, causing it to miss objectives or complete them inefficiently.

• Some runs were successful, while others resulted in misalignment or failure to complete certain tasks.

• Unclear Pathing and Timing Between Actions:

• Without a structured sequence, the robot struggled to transition smoothly between different actions.

• Delays, misalignment, or unintended pauses resulted in wasted time and inefficiencies.

• Difficulty in Achieving Repeatable Runs:

• One of the key goals in a skills challenge is achieving consistent performance across multiple runs.

• The initial setup led to variability in execution, making it difficult to ensure reliability under competition conditions.

​

Since precision and efficiency are crucial in maximizing points and optimizing performance, addressing these issues was a top priority.

AUTON CHANGE REPORTS

​

10/14/2024

68

142

AUTON+DRIVER MOVEMENT/ROUTE TESTING�Skills Route Initialization

What we changed:�To improve the consistency of the skills route, we focused on structuring the robot’s movements using waypoints and predefined actions. The main changes included:

1. Programming Defined Waypoints:

• Instead of relying on manual adjustments or trial-and-error movements, we set up waypoints that mapped out the ideal route.

• These waypoints provided a structured guide for the robot to follow, improving path accuracy.

2. Creating a Sequence of Actions for Each Objective:

• We broke down the skills challenge into specific steps, ensuring that the robot executed tasks in a logical order.

• Actions were precisely timed to minimize delays and unnecessary movement corrections.

3. Testing and Refining Transitions Between Actions:

• We analyzed how the robot moved from one action to the next, adjusting timing and motion parameters to create smoother transitions.

• Speed and acceleration settings were fine-tuned to prevent jerky movements or loss of control during transitions.

AUTON CHANGE REPORTS

​

10/14/2024

68

143

AUTON+DRIVER MOVEMENT/ROUTE TESTING�Skills Route Initialization

Why would it work?�Implementing waypoints and structured action sequences allows the robot to complete the skills challenge more effectively by:

• Improving Task Completion Rate:

• The robot now has a clear and repeatable path to follow, reducing the chances of missed objectives.

• Enhancing Smoothness and Efficiency:

• With better-defined transitions, the robot can move between objectives more fluidly, saving time.

• Increasing Run-to-Run Consistency:

• By removing ambiguity in movement execution, we ensure that the robot performs more reliably across multiple runs.

AUTON CHANGE REPORTS

​

10/14/2024

68

144

AUTON+DRIVER MOVEMENT/ROUTE TESTING�Skills Route Initialization

Did it work (why or why not)?�Partially successful. While the introduction of waypoints and structured action sequences significantly improved the overall consistency and efficiency of the skills route, some challenges remain:

• Struggles with Transitions Between Actions:

• The robot still experiences minor hesitations or misalignments when switching between different objectives.

• Additional refinements in timing and motion profiling are needed to fully optimize transitions.

• Further Adjustments Needed for Faster Execution:

• While accuracy has improved, speed optimizations are still required to maximize performance in a competitive setting.

• Balancing precision and speed will be key in future refinements.

AUTON CHANGE REPORTS

​

10/14/2024

68

145

AUTON+DRIVER MOVEMENT/ROUTE TESTING�Skills Route Initialization

Overall: �By implementing defined waypoints and structured action sequences, we significantly improved consistency, accuracy, and efficiency in the skills route. While some challenges remain in optimizing transitions and execution speed, these changes have laid the foundation for a more reliable and effective performance. Further refinements will focus on minimizing transition delays, improving motion profiling, and ensuring repeatable high-scoring runs.

AUTON CHANGE REPORTS

10/17/2024

69

146

AUTON ROUTE PROGRAMMING�Autonomous route: Positive Red

TASK:

Auton Routes Positive Red

Route step: Clamp stake, score, next stake

We continued working on the autonomous route.

Continue from clamping, scoring preload and intake, dropping stake, and going to next stake…

On the second stake, the robot pushed it too far and didn’t clamp onto it. This happened very frequently, so we had to fine tune positions and speeds.

AUTON CHANGE REPORTS

10/23/2024

70

147

AUTON ROUTE PROGRAMMING�Autonomous route: Positive Red

TASK:

Auton Routes Positive Red

Route step: Clamp stake, score, next stake

We continued working on the autonomous route.

The second stake kept getting jammed on the conveyor belt.

AUTON CHANGE REPORTS

10/25/2024

71

148

AUTON ROUTE PROGRAMMING�Autonomous route: Positive Red

TASK:

Auton Routes Positive Red

Route step: Clamp stake, score, next stake

We fixed up positioning and the route was able to score 2 rings on the first stake, pick up the next stake and score on it.

PICK UP:

SCORE:

← Touch ladder

Routes complete!

AUTON CHANGE REPORTS

​

10/29/2024

72

149

AUTON+DRIVER MOVEMENT/ROUTE TESTING�Doinker Macro

What is the problem?�The robot had difficulty clearing rings that accumulated in the corner, causing obstruction and affecting movement.�⁠What we changed:�Implemented code for pneumatic system designed to sweep rings out of the corner efficiently.�Why would it work?�Automating the process of clearing rings allows for uninterrupted movement and prevents obstructions that could hinder the robot’s performance.�Did it work (why or why not)?�Yes, the pneumatic system effectively cleared rings, but additional testing is needed to ensure reliable operation during continuous use.

Sensor Change

​

10/29/2024

73

150

SENSORS�Replaced color sensor with distance sensor

To ensure quick detection of the ring for the wall stake mechanism, we swapped out the color sensor with a distance sensor.

​

This greatly increased the consistency of the redirect macro facilitating faster wall stake play and top-ring control which will be essential for gameplay.

​

This means our color macro will be modified to work with distance sensor.

Auton Done

11/01/2024

74

151

Auton done on Nov 1

​

Completed all 4 routes, but the blue negative route had problems.

​

We fixed the blue route the day of the competition.

​

However, due to firmware issues, the autonomous seemed not to work only when it was plugged into the official competition switch.

Auton Route Completion�Day before Ceres competition

Competition Reflection

11/02/2024

75

152

COMPETITION REVIEW�Central Valley High Stakes VEX V5 Robotics Competition #3 @ Ceres High School (1 win, 4 losses, 1 tie)

Things that went wrong at competition:

• Clamp:
• Clamp was easily dislodged by opposing alliance members hindering our scoring ability
• Intake 1st stage:
• The first stage chain was on the outside of the robot and very susceptible to breaking
• Wallstake Mechanism:
• The retake mechanism was quite slow and inconsistent.
• Autos
• Autos were not working consistently

Fixes:

• Make a more robust clamp with pistons almost vertical
• Have the intake with its own separate 5.5 watt motor that doesn’t have chain exposed on the outside
• Switch to a lady brown wall stake mechanism
• Use odometry to code consistent autos

Subassemblies List

76

153

Electronic Components For Subassemblies

Lady Brown

week of thanksgiving

77

154

Lady Brown�New Wall Stake Mechanism

The Lady Brown wall stake mechanism enables faster scoring while using fewer motors. This allows more torque to be allocated to the intake and mogo-scoring mechanisms.

Pros:

• Enables very fast scoring.
• Increases cycle time.
• Highly consistent performance.
• Requires fewer motors.

Cons:

• Vulnerable to being dislodged or defended.
• Limited to scoring one at a time.

​

project

week of thanksgiving

78

155

Vertical Piston Clamp�Mogo Mechanism

Problem Statement

The mogo clamp faced multiple issues, including interference from other bots, poor alignment, and failure to passively lock. These problems required higher PSI to function, leading to reduced efficiency with fewer uses per air tank refill.

Pros:

1. Improved Alignment: Repositioning the piston reduces misalignment issues.
2. More Durable: The 3-hole C-channel is sturdier than the old 2x2.
3. Better Locking: High-strength components help with passive locking.
4. Less Interference: Design changes minimize issues with other bots.

project

week of thanksgiving

79

156

Passive Polycarbonate Climb�Tier 1 Elevation Mechanism

To climb, we originally had an active climb mounted on our wallstake mechanism.

To address this, we switched to a passive climb that we drive up onto the truss:

• Two piston passive climb
• Uses custom polycarbonate pieces that are perfectly shaped for the truss

Pros:

• Increased speed and consistency.
• Doesn’t require a motor

Cons:

• Does not climb to third tier

project

week of thanksgiving

80

157

Split Intake/Hook System�Mogo Scoring Mechanism

The intake mechanism was weak and inconsistent, limiting performance.

To address this, we switched to a split first and second stage system:

• 5.5 Watt First Stage:
• geared for speed
• 400 RPM.
• 11 Watt Hooks:
• 1:1 sprocket.
• 600 RPM direct.

Pros:

• Increased speed and consistency.
• Improved scoring efficiency.

Cons:

• Creates spacing issues for odometer pods.

project

mentioned on slides, 2024

81

158

Code Updates�Autonomous Route

Post-November 2 Competition Updates & Refinements

​

General Match Route Adjustments:

• Tweaked values for all four match routes to improve consistency and performance.

• Began working on the programming skills route, but it remains untested and unfinished.

project

mentioned on slides, 2024

81

159

Code Updates�Autonomous Route

November 24 Updates:

​

Odometry & Tracking Wheel Enhancements:

• Added baseline configurations for odometry sensors, but ports and some settings still need adjustment.

• Updated configurations for tracking wheels, ensuring they are calibrated and ready for use.

​

Intake & Clamp Code Refinements:

• Intake code finalized and confirmed to be functioning correctly.

• Cleanup of old variables from earlier versions of the code is still required; for now, they have been commented out.

• Clamp mechanism code finalized and confirmed to be working properly.

​

Chassis & Temporary Scoring Features:

• Fixed an accidental issue in the chassis code that was affecting movement.

• Implemented a temporary wallstake scoring functionality, providing an initial framework for scoring on wall stakes.

project

mentioned on slides, 2024

81

160

Code Updates�Autonomous Route

November 26 Updates:

​

New Mechanism Implementations:

• Added “Doinker” Mechanism: Designed to scoop rings out of the corner to improve ring retrieval efficiency.

• Added Intake Lift: Functions as a conveyor belt to transport rings from intake for scoring.

• Added Wallstake Mechanism (“Ladybrown”) for improved ring placement on wall stakes.

​

Code Optimization & Cleanup:

• Refactored and cleaned up various sections of the code for better readability and efficiency.

• Removed outdated or unused code to ensure a streamlined and maintainable project.

project

mentioned on slides, 2024

82

161

Code Updates�Autonomous Route (cont.)

• November 29: PID Re-Tuning & Wallstake Macro Development

• Due to the robot being rebuilt, we had to re-tune our PID system’s constant variables to restore accurate autonomous movement.

• Updated C++ libraries in the project to ensure compatibility and performance improvements.

• Worked on the wallstake macro, which allows the driver to cycle through three different positions:

1. Rest – Keeps the mechanism out of the way.

2. Ready – Positions it to grab rings from the intake conveyor.

3. Score – Moves the mechanism up to place the ring on the wall stake.

• The macro successfully rotates and cycles, but it moves in the wrong direction, requiring further adjustments.

• Implemented PID control for the wallstake scorer to ensure precise movement between positions.

project

mentioned on slides, 2024

82

162

Code Updates�Autonomous Route (cont.)

• November 30: Wallstake Macro Fixes & Debugging
• • Fixed the “Lady Brown” (Wallstake) macro, ensuring correct movement across all positions.
• • Added print statements for debugging, but they caused the brain to crash, so they will need to be removed or optimized.

project

mentioned on slides, 2024

82

163

Code Updates�Autonomous Route (cont.)

• December 1: Programming Skills Autonomous Testing

• Ran initial tests on the programming skills autonomous route to evaluate performance and identify areas for improvement.

​

Next Steps & Pending Work:

• Remove or optimize print statements to prevent system crashes.

• Refine PID tuning for wallstake scorer to improve movement accuracy.

• Continue testing and optimizing the programming skills route to ensure consistent performance.

Route Visualizer for LemLib

12/1/2024

83

164

Route Visualization Tool�Created tool that works for our purposes

We made this as we wanted a tool where we could simply paste in our code and have the route positions pop up in front of us.

project

mentioned on slides, 2024

84

165

Code Updates�Autonomous Route (cont.)

• December 1 - December 6 Updates & Refinements

​

• December 1 (Continued): Odometry & Programming Skills Route Development

• Encountered an issue when using both vertical and horizontal odometry sensors simultaneously, causing conflicts in motion calculations.

• As a temporary fix, disabled horizontal odometry and continued tuning with vertical odometry only.

• Modified PID values to enhance motion accuracy and reduce errors.

• Began implementing movements for the first quadrant of the programming skills route.

• Currently only movement logic is implemented; additional subsystems (intake, clamp, etc.) need to be integrated.

• Delays & timing require adjustments to ensure smoother transitions.

• PID tuning needed to resolve jittery movements.

• Fixed odometry conflict, allowing both vertical and horizontal sensors to function simultaneously without compromise, ensuring higher motion accuracy.

project

mentioned on slides, 2024

84

166

Code Updates�Autonomous Route (cont.)

• December 6: PID Tuning & Programming Skills Route Progress

• Retuned PID to refine movement precision.

• The chassis still requires additional tuning, but its current performance is satisfactory.

• Developed movement sequences for most of the programming skills autonomous route.

• Further refinements needed to ensure smooth execution.

project

mentioned on slides, 2024

85

167

Code Updates�Autonomous Route (cont.)

December 7 - December 16 Updates & Refinements

​

December 7: Autonomous Skills Route Progress

• Integrated subassemblies into the autonomous skills route, enabling synchronized control of key mechanisms.

• Fine-tuned first quadrant movements, improving accuracy and efficiency.

• Still requires additional tweaking for consistency.

project

mentioned on slides, 2024

85

168

Code Updates�Autonomous Route (cont.)

December 10: Mechanical & Control Adjustments

• Reversed the front intake motor direction to accommodate a mechanical change.

• Modified controller button mapping for the wall stake macro, optimizing driver controls for smoother operation.

• Minor tweaks across various systems to enhance performance.

project

mentioned on slides, 2024

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Code Updates�Autonomous Route (cont.)

December 16: Advanced Autonomous & Background Processing

• Continued refining the autonomous skills route with additional adjustments.

• First quadrant is now mostly tuned, but some minor optimizations are still needed.

• Faced a conflict with the Ladybrown (wall stake scorer) macro during ring-scoring on stakes.

• Since Ladybrown’s PID needed continuous updates while executing the route, it interfered with scoring actions.

• Solution: Developed a background process that updates Ladybrown’s PID simultaneously with the autonomous route execution.

• This improves intaking efficiency and ensures that Ladybrown moves properly during autonomous runs.

project

mentioned on slides, 2024

86

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Code Updates�Autonomous Route (cont.)

December 17: Programming Skills Route Fine-Tuning

• Updated the programming skills route, focusing primarily on the first quadrant.

• Route is mostly fine-tuned, but still exhibits inconsistencies:

• Sometimes it successfully scores 5 rings, while other times it only scores 2-3 rings.

• The issue seems to stem from timing and positioning inaccuracies.

• Added face display to the brain screen for better real-time monitoring of the robot’s status and progress.

project

mentioned on slides, 2024

86

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Code Updates�Autonomous Route (cont.)

December 18: Redesigning the Autonomous Skills Route

• Due to the inconsistencies with the initial route, we decided to remake the autonomous programming skills route.

• The previous route was used as a guideline for restructuring the new route.

• As of now, the new route isn’t fully functional:

• There are still positioning problems that prevent the route from running smoothly.

project

mentioned on slides, 2024

86

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Code Updates�Autonomous Route (cont.)

December 19: Further Route Updates & Ladybrown Enhancements

• Updated the programming skills route further:

• Adjusted delays and rewrote parts of the route to address previous issues.

• Delays still need fine-tuning to ensure smooth transitions between tasks.

• Moved the autonomous Ladybrown update to the screen task to resolve a bug when switching from autonomous mode to driver control.

• Updated Ladybrown’s angles due to recent mechanical changes, ensuring correct positioning during ring scoring.

project

mentioned on slides, 2024

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Code Updates�Autonomous Route (cont.)

Dec 19 (cont.)

• With improvements to the mechanical systems (intake and wallstake scorer), fixes with positioning, and improvements in the code, we were able to score 5 rings on 2 stakes and put them in the corner.

Below are pictures of the corner placement of stakes for various runs of the route (the robot scored these rings

← BEST RUN

(camera didn’t capture the corner placement)

We got 5, but would’ve gotten all 6 rings hadn’t it jammed due to moving too fast (which we fixed)

5

4

4

5

Route Visualizer for LemLib

mentioned on slides, 2024

88

174

Route Visualization Tool�GitHub Commits

GitHub Commits (from newest to oldest):

December 20: UI & Animation Updates

• Made significant changes to the UI:

The UI underwent a major overhaul to improve overall user experience. This could include reorganizing layout elements, streamlining navigation, and making it easier for users to interact with the route visualizer or settings. The goal was to create a more intuitive interface that enhances usability and reduces clutter.

• Added more animation controls:

New controls were introduced to give users more flexibility and customization options for animation behavior. This likely includes sliders or buttons to adjust speed, ease, or other animation parameters. This change empowers users to fine-tune how animations play, ensuring they can match their preferences or debugging needs.

Route Visualizer for LemLib

mentioned on slides, 2024

88

175

Route Visualization Tool�GitHub Commits

December 16: Bug Fixes & New Features

• Added route command text display for animations:

A text display was added that shows the current route commands during the animation. This allows users to track what specific instructions are being executed at any given point during the animation. It could also help identify issues in real time or make debugging more transparent.

• Fixed bug with negative time per animation step:

A bug was identified where negative time values were being processed, causing issues with animation timing. This fix ensures that each animation step is timed correctly, leading to smoother and more predictable animations. The bug could have been causing visual glitches or timing inconsistencies during route visualization.

Route Visualizer for LemLib

mentioned on slides, 2024

88

176

Route Visualization Tool�GitHub Commits

December 8: UI Enhancements & Performance Updates

• Fixed bug preventing the route from showing on startup if not saved in localStorage:

A startup issue was preventing the route from being loaded if the route data hadn’t been saved in localStorage. The fix ensures that even without a saved route, the program behaves as expected, potentially showing a default route or an empty route to start with. This ensures smoother user experience when starting the application.

• Added version tag text and script to fetch:

A version tag was added to display the current version of the app. This can be important for users to know which version they’re using, especially if they’re experiencing issues. Additionally, a script to fetch the version dynamically could help track future updates and ensure compatibility with new features or bug fixes.

• Introduced tab title and bottom disclaimer:

A tab title was added to the browser’s tab, making it easier for users to identify the app when multiple tabs are open. A bottom disclaimer provides extra information, likely clarifying the app’s use, licensing, or providing credit to contributors. This improves professionalism and transparency.

• Refined mouse positioning to only show when the mouse is over the field:

The mouse positioning display feature was modified so that it only activates when the mouse hovers over the field area. This reduces unnecessary distractions and clutter when the user is interacting with other parts of the app, providing a cleaner interface.

• Added repo link and credit:

A repository link was included to direct users to the project’s GitHub page. It’s an easy way to encourage users to access the source code or report issues. The credit section acknowledges contributors or libraries used, ensuring proper attribution.

• Changed the field rendering to use canvas drawable objects instead of an image:

The method used to render the field changed from using a static image to canvas drawable objects. This is a more dynamic and flexible approach, allowing for better control over how the field is drawn and how field elements can be manipulated. However, the code needs some cleanup since it may not be as optimized or structured.

• Added favicon and removed unneeded images:

A favicon was added for branding purposes, improving the app’s overall visual appeal and helping users identify it in browser tabs. Unnecessary images were removed to streamline the app’s performance and reduce resource usage, enhancing efficiency.

​

Route Visualizer for LemLib

mentioned on slides, 2024

88

177

Route Visualization Tool�GitHub Commits

December 7: Animation & UI Enhancements

• Implemented animation easing out for each step:

Animation easing is a technique that smoothens transitions between animation steps. By adding ease-out, the animations slow down toward the end, creating a smoother and more natural flow. This improves visual appeal and makes interactions feel more polished.

• Added skills field image:

A skills field image was added to better represent the environment in which the robot will be operating. This visual element helps users to better understand how the robot is interacting with the field and may also serve as a reference for programming or testing.

• Significantly improved UI:

Significant UI improvements likely focused on making the interface more responsive, visually appealing, and user-friendly. This could involve refining layouts, improving readability, or simplifying navigation. Bug fixes from earlier UI updates would also be part of this change.

Route Visualizer for LemLib

mentioned on slides, 2024

88

178

Route Visualization Tool�GitHub Commits

December 6: Route Visualization & Interactive Features

• Added mouse click functionality to add moveToPoint at cursor:

An interactive feature was introduced, allowing users to add moveToPoint commands directly at the cursor’s position on the field. This makes it easier for users to create and edit routes on the fly, offering a more intuitive and hands-on method of programming the robot’s movements.

• Saved route code to localStorage:

Routes are now saved to localStorage, allowing users to persist their work across sessions. This ensures that users don’t lose their progress when they close or refresh the app, improving usability.

• Added button to change animation speed:

A button was added to allow users to adjust the animation speed. This gives users more control over how quickly or slowly the robot’s movements are visualized, which can be useful for debugging or just personal preference.

• Implemented route visualizer with rectangles for all bot positions and animation:

A route visualizer was added that uses rectangles to represent the robot’s positions at various stages of the route. This visual feedback makes it easier for users to track the robot’s movements and understand how it navigates the field. This also helps users identify where problems might occur in the route.

• Processed text with LemLib C++ route into a JavaScript object for display:

The application can now process LemLib C++ route text and convert it into a JavaScript object that can be visualized in the app. This allows for a seamless integration of C++ route code with the UI, providing a more interactive way to debug or visualize routes.

• Memory leak issue with long-term animation:

There is a memory leak when the animation feature is enabled for extended periods, which causes performance degradation. This issue needs to be addressed to ensure that the app runs smoothly over time without consuming too much memory.

​

Route Visualizer for LemLib

12/20/2024

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Route Visualization Tool�Latest Version as of Dec 20

There are several features, such as animating the route movement, and live previewing the route. The above image is the latest update of the visualization tool.

​

It can be found at:

the-akze.github.io/Route-Visualizer-for-LemLib/

Code - Programming Skills Routes

12/19/2024

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Code�Programming Skills Route Plan

Our latest route for programming skills is shown. This was generated by the route visualizer talked about on the past slide. (See next slide for path)

Code - Programming Skills Routes

12/19/2024

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Code�Programming Skills Route Plan

The path is shown in rainbow & number order

1.stake, score 3 rings

2.score 3 more, put in corner

3.move to next stake

4.score 2 rings

5.score 4 more, put in corner

6.next side, score 1 ring and get next stake

7.score 5 more rings, put in corner

Full CAD

12/20/24

92

182

Post Competition Reflection

12/21/24

91

183

POST COMPETITION�WINTERS FARMBOTS INVITATIONAL

Robot Overall Performance

• 1st Place in Qualification Matches: The team went undefeated with a 7-0 record, securing the top seed in qualification rounds.

• Innovate Award: Recognized for unique design and implementation of innovative mechanisms.

• Robot Skills Champion: Achieved the highest combined skills score, earning qualification for regionals.

• Skills Performance:

• 69 total points in skills:

• 41 points in driver skills

• 28 points in programming skills

• Tournament Finalists: Reached the finals but lost in the championship match.

​

​

Post Competition Reflection

12/21/24

91

184

POST COMPETITION�WINTERS FARMBOTS INVITATIONAL

Post Competition Reflection

12/21/24

91

185

POST COMPETITION�WINTERS FARMBOTS INVITATIONAL

Positives and Strengths

• Programming Skills Worked Well: The autonomous programming functioned as expected, contributing significantly to the skills score.

• Increased Consistency: The robot performed more reliably and consistently compared to previous events.

• Faster Intake: The intake mechanism operated at a much higher speed, allowing for quicker ring collection.

• More Reliable Clamp: The clamp mechanism for grabbing and securing objects was significantly more consistent.

• Improved Wall Stake Mechanism: The wall stake scoring system was much faster, improving cycle times.

Post Competition Reflection

12/21/24

91

186

POST COMPETITION�WINTERS FARMBOTS INVITATIONAL

Areas for Improvement

• Higher Scoring Autonomous Skills:

• The programming skills routine, while functional, needs to be optimized to achieve a significantly higher score.

• Driver skills also require improvement, with better route selection and execution to maximize efficiency.

• More Consistent Autonomous Routes: Some autonomous paths still lack consistency, leading to occasional failures that need to be addressed.

• Driver Practice: Additional driver training sessions would help improve match performance, reaction times, and overall execution under pressure.

​

Post Competition Reflection

12/21/24

91

187

POST COMPETITION�WINTERS FARMBOTS INVITATIONAL

Upgrade Chassis for Speed and Maneuverability

One of the key areas of improvement for our robot is upgrading the chassis to enhance speed and maneuverability. Currently, our drivetrain allows for reasonable movement, but we believe a transition to 450 RPM motors will significantly increase our speed, allowing for quicker navigation across the field. Faster movements are essential in competitive matches, where the ability to reach goals and defensive positions swiftly can make the difference between winning and losing.

Post Competition Reflection

12/21/24

91

188

POST COMPETITION�WINTERS FARMBOTS INVITATIONAL

Upgrade Chassis for Speed and Maneuverability

(cont)

Additionally, we plan to integrate 3.25-inch omni wheels to improve agility and control. These wheels provide excellent traction while allowing for smoother turns and lateral movement, which is crucial for precise driving strategies. By optimizing the drivetrain with better speed and control, our team will be in a stronger position to execute both offensive and defensive maneuvers more effectively. The combination of high-speed motors and omni wheels will ensure our robot can respond to in-game conditions dynamically, allowing for superior adaptability.

​

Post Competition Reflection

12/21/24

91

189

POST COMPETITION�WINTERS FARMBOTS INVITATIONAL

Implement a Descore Mechanism for Skills

To enhance our performance in skills competitions, we aim to develop a descore mechanism that can efficiently remove rings from opponents’ goals. In the current competition landscape, the ability to descore is a valuable strategic tool that can shift the balance of a match. A well-designed descore mechanism would increase our ability to control the field, reducing the opponent’s points while simultaneously allowing us to secure higher-scoring opportunities.

Post Competition Reflection

12/21/24

91

190

POST COMPETITION�WINTERS FARMBOTS INVITATIONAL

Implement a Descore Mechanism for Skills

This enhancement is particularly significant because it could raise our skills cap to 79 points. The additional points will place our team in a more competitive standing, increasing our ranking and performance at both local and national levels. Our engineers will focus on prototyping and testing various designs to ensure efficiency while maintaining the integrity of our existing mechanisms. The descore mechanism must be lightweight and minimally intrusive to avoid interfering with other robot functions while still providing maximum impact on the game strategy.

​

Post Competition Reflection

12/21/24

91

191

POST COMPETITION�WINTERS FARMBOTS INVITATIONAL

Research From Other Teams

Post Competition Reflection

12/21/24

91

192

POST COMPETITION�WINTERS FARMBOTS INVITATIONAL

Refine the “Lady Brown” Two-Ring Mechanism

Our current wall stake scorer, known as “Lady Brown,” has been a crucial component in our robot’s success. However, improvements are necessary to make it more efficient. One of our major goals is to modify the mechanism to handle two rings simultaneously. By refining the system to allow for dual-ring scoring, we can dramatically increase the number of points scored in a match.

Post Competition Reflection

12/21/24

91

193

POST COMPETITION�WINTERS FARMBOTS INVITATIONAL

Refine the “Lady Brown” Two-Ring Mechanism (cont)

Efficiency is key in high-level competition, and the ability to handle two rings at once means that we can spend less time aligning and placing individual rings, leading to faster cycle times. This improvement will also enhance consistency, as the ability to score multiple rings in a single motion reduces the risk of errors and allows for smoother execution of match strategies. Prototyping and extensive testing will be essential to ensure the upgraded mechanism can handle rings reliably without jamming or misalignment.

​

Post Competition Reflection

12/21/24

91

194

POST COMPETITION�WINTERS FARMBOTS INVITATIONAL

Optimize Autonomous Programming

Autonomous programming plays a vital role in maximizing our robot’s potential. The current programming skills run effectively but lacks the level of consistency required for elite performance. Our goal is to refine our autonomous routines to be smoother and more reliable, minimizing error margins and increasing point-scoring efficiency.

Post Competition Reflection

12/21/24

91

195

POST COMPETITION�WINTERS FARMBOTS INVITATIONAL

Optimize Autonomous Programming (cont)

To achieve this, we will focus on implementing better error handling and fine-tuning sensor feedback systems. By improving how the robot detects and reacts to real-time conditions, we can reduce inconsistencies in movement paths. Furthermore, we will work on identifying higher-scoring routes that maximize the number of points scored within the available time. A well-optimized autonomous period can provide a significant competitive edge, allowing our team to gain early leads in matches and skills challenges.

Post Competition Reflection

12/21/24

91

196

POST COMPETITION�WINTERS FARMBOTS INVITATIONAL

Optimize Autonomous Programming (cont)

Another key aspect of this refinement process is leveraging simulation software to test different autonomous paths before implementation. This will allow us to predict potential issues and adjust programming logic before applying changes to the physical robot. This data-driven approach will ensure our autonomous program is robust and capable of handling a variety of in-game conditions.

​

Post Competition Reflection

12/21/24

91

197

POST COMPETITION�WINTERS FARMBOTS INVITATIONAL

Evaluate a Tier 3 (T3) Climb

Climbing mechanisms are an important strategic aspect of competitive robotics, often serving as a game-deciding factor. We are currently evaluating the feasibility of designing a Tier 3 (T3) climb. A successful T3 climb would significantly increase our endgame score, providing a major advantage in close matches. However, before committing to this addition, extensive testing is required to determine whether the additional points gained justify the resources and complexity involved in implementing such a system.

Post Competition Reflection

12/21/24

91

198

POST COMPETITION�WINTERS FARMBOTS INVITATIONAL

Evaluate a Tier 3 (T3) Climb (cont)

A major challenge of the T3 climb is designing a mechanism that integrates seamlessly with our existing robot framework without compromising other functionalities. We need to ensure that weight distribution remains balanced and that the climb can be executed reliably under competition conditions. If testing proves that the T3 climb offers a substantial strategic advantage, we will move forward with development, focusing on achieving consistent and secure climbs in every match.

​

Post Competition Reflection

12/21/24

91

199

POST COMPETITION�WINTERS FARMBOTS INVITATIONAL

Develop a Tier 1 (T1) Passive Hang

In addition to evaluating the T3 climb, we plan to implement a Tier 1 (T1) passive hang. Unlike active climbing mechanisms, a passive T1 hang does not require additional motorized components, making it a simpler yet effective way to gain extra points. The goal is to design a passive system that allows our robot to latch onto the hanging structure at the end of the match without the need for active input.

Post Competition Reflection

12/21/24

91

200

POST COMPETITION�WINTERS FARMBOTS INVITATIONAL

Develop a Tier 1 (T1) Passive Hang (cont)

This addition is particularly valuable because it provides a reliable way to earn extra points without overcomplicating our robot’s design. A well-executed passive hang will complement our overall strategy, ensuring that we capitalize on every scoring opportunity available. Testing will be conducted to ensure that the mechanism consistently engages with the hanging structure while maintaining structural integrity throughout the match.

Autonomous

12/21/24

92

201

Throughout previous matches, we encountered a persistent and frustrating issue where our autonomous routine would not run properly. While the subassemblies (intake, clamp, wall stake mechanism, etc.) moved as expected, the chassis remained stationary, preventing the robot from executing its full sequence. The most confusing part was that when we manually ran the autonomous code outside of competition matches, everything worked perfectly.

​

Since autonomous is a critical component for scoring and match consistency, we needed to diagnose and resolve this issue quickly.

​

Code Problem�Fixed after a long time

Autonomous

12/21/24

92

202

Code Problem�Fixed after a long time

Debugging the Issue

​

To pinpoint the cause, we consulted a LemLib project lead—LemLib being the C++ library we use for motion control and autonomous navigation. After carefully reviewing our code and testing different scenarios, we identified the root cause:

• The chassis calibration was being performed inside the autonomous function itself.

• This meant that every time autonomous was called, the chassis recalibrated before executing movement commands.

• However, in a competition setting, this recalibration interfered with movement execution, likely causing the chassis to freeze while subassemblies continued running.

​

This explained why manual tests worked fine—when run separately, the autonomous sequence had enough time to calibrate and execute correctly. However, in matches, the robot never completed calibration in time to begin movement.

Autonomous

12/21/24

92

203

Solution and Implementation

​

To fix the problem, we moved the chassis calibration from the autonomous function to the initialize function. By performing calibration during robot startup instead of during autonomous execution, we ensured that:

1. The chassis was fully calibrated before autonomous began.

2. Autonomous movement commands could execute immediately without delay or interference.

Code Problem�Fixed after a long time

Autonomous

12/21/24

92

204

Code Problem�Fixed after a long time

Results and Impact

​

With this fix in place, our autonomous route now runs consistently in competition. This was a major breakthrough, as it allowed us to confidently execute our programmed autonomous strategies without worrying about unexpected failures.

​

This debugging process also reinforced the importance of thorough testing in match conditions and ensuring that all subsystems initialize correctly before execution. Moving forward, we will continue refining our autonomous routes for better accuracy and scoring potential.

Build & Mech

date

93

205

Switched chassis to 450 RPM with 3.25 inch omni wheels.

Build & Mech�Updates

• Switch Chassis to 450 RPM on 3.25 inch omni wheels
• Design a descore mechanism for skills runs to increase skills cap to 79 points
• Tune a two ring lady brown mechanism
• Have much more refined, consistent and powerful programming skills
• Maybe design a t3 climb if it is worth
• Make a t1 passive hang first

New Clamp

03

206

Build & Mech �Chassis

Switched from a 2.75 inch omni wheel chassis to a 3.25 inch omni wheel chassis.

​

Needed custom Mcmaster Carr spacers for the spacings.

​

Used screw joints to reduce friction. Utilized 4 omni wheels for optimized turning speed when driving.

​

Used half-cut C-Channels for bracing to reduce weight.

Screw joints

3.25 inch omni wheels

New Clamp

93

207

Implementation of a Consistent and Omni-Directional Locking Clamp

A locking clamp mechanism is designed to provide secure and reliable fastening for various applications. In this case, the focus is on creating a clamp that is both consistent in performance and capable of omni-directional operation.

Build & Mech �Clamp

New Clamp

93

208

Build & Mech �Clamp

Key Features:

• Consistency: The clamp mechanism ensures that each operation results in the same secure hold, reducing variability and potential failures.
• Omni-Directionality: The design allows for gripping from multiple angles without compromising the strength or reliability of the system.
• Adaptability: The locking mechanism can be adjusted to accommodate different sizes or shapes of mobile goals, ensuring versatility in its application.

​

New Clamp

93

209

Utilization of Bent Standoffs for Main Force Transfer

The use of bent standoffs plays a critical role in effectively transferring force from the pistons to the mobile goal while ensuring structural integrity.

Build & Mech �Clamp

New Clamp

93

210

Build & Mech �Clamp

Advantages of Bent Standoffs:

• Enhanced Force Distribution: The bent standoffs provide a direct and robust path for force transmission, reducing stress on weaker components.
• Structural Reinforcement: By integrating standoffs with the chassis, the overall rigidity of the system is improved, preventing deformation or displacement.
• Improved Durability: The use of bent standoffs minimizes wear and tear on the pistons, extending their operational lifespan.

​

New Clamp

93

211

Shock and Collision Absorption through Structural Bracing

A major concern in robotic applications is the potential damage from impacts or sudden forces. Bent standoffs help mitigate these issues by transferring loads effectively.

Build & Mech �Clamp

New Clamp

93

212

Build & Mech �Clamp

Benefits:

• Protecting the Pistons: Rather than the pistons bearing the full impact, forces are distributed through the chassis structure.
• Increased System Longevity: By reducing direct stress on actuators, the lifespan of moving parts is prolonged.
• Resilience to External Forces: In case of sudden collisions or shocks, the structure absorbs and disperses energy, preventing catastrophic failures.

New Clamp

93

213

Auto-Align Feature for Enhanced Consistency

The auto-align mechanism is crucial for ensuring the clamp consistently secures the mobile goal correctly every time.

Key Functionalities:

• Self-Correcting Mechanism: If the clamp is slightly misaligned, the system automatically adjusts to ensure proper engagement.

Build & Mech �Clamp

New Clamp

93

214

Build & Mech �Clamp

Key Functionalities (cont)

• Repeatable Accuracy: Ensures that every clamping action results in the same positioning, improving reliability.
• Operational Efficiency: Reduces the need for manual adjustments, allowing for seamless integration into automated systems.

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215

Build & Mech �First Stage (Floating Intake)

Used a floating flex-wheel intake for fast ring scoring.

​

Ensured a 2:1 gear ratio on the 5.5 watt motor to maximize speed.

​

Ensured structural integrity for ramming.

​

Can move up and down with relative ease to accommodate stacked rings

​

Mounted with a piston to lift and drop to grab stacked rings in corners and on the field.

​

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216

Build & Mech �Second Stage (Hooks)

Used a hook intake for effective ring scoring.

​

Utilized a full 11 Watt motor running at 600 RPM Directly to maximize speed.

​

Incorporated a chain tensioner to keep tension consistent.

Placed Standoffs strategically to ensure we can score wall stakes even with a full mobile goal.

​

Build the hook structure using half-cut C-Channels to save weight.

​

93

217

Build & Mech �Wall Stake Mechanism (Double Ring Lady brown)

Used a two stage lady brown mechanism to score on the wall-stakes

​

Ensures consistent scoring on the wall stakes

​

Uses a rotation sensor and a custom PID system to go to previously defined heights.

Scores on Alliance stakes, other mobile goals as well as wall stakes

​

Can tip and untip mobile goals which plays into last second negative plays

​

1:5 gear ratio for torque to ensure consistent scoring

93

218

Build & Mech �Doinker/Goal Rush Arm

Using Delrin, we designed a goal rush arm that doubles a doinker to clear corners.

​

Using this mechanism, we can not only grab the neutral mobile goal in autonomous, we can also steal mobile goals from positive and negative corners.

​

This can also tip and until mobile goals if we cannot use the wall stake mechanism

This multifunctional arm is essential for our driver strategy and has come in useful in many circumstances.

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219

Code & Autonomous

12/21/24 - 2/2/25

94

220

February 2: Chassis Tuning & Intake Color Sort

• Chassis PID Tuning: Angular PID has been successfully tuned to improve rotational movement. Lateral tuning is still a work in progress to fine-tune the robot’s side-to-side movements for smoother and more accurate positioning. This will help improve overall stability and control during rapid maneuvers.

Code�Updates

Code & Autonomous

12/21/24 - 2/2/25

94

221

Code�Updates

February 2 (cont): Intake Color Sort: Work on the color sorting mechanism started, which ensures the robot only picks up and scores rings of the team’s color. This feature helps avoid scoring rings of the opposing team’s color and can automatically reject the wrong rings, improving the overall accuracy during gameplay.

​

Code & Autonomous

12/21/24 - 2/2/25

94

222

February 1: Wallstake Movement & Code Cleanup

• Wallstake (Ladybrown) Movement: The wallstake mechanism has been updated to improve its movement and efficiency. The code controlling it has been cleaned up for better readability and performance. While it runs smoother, the angles still need to be adjusted for greater precision, and error correction hasn’t been fully implemented yet.

Code�Updates

Code & Autonomous

12/21/24 - 2/2/25

94

223

Code�Updates

February 1 (cont) Post-Competition Code Cleanup: After the competition, we focused on refining the code, using the lessons learned to optimize its efficiency and functionality. This involved removing redundant or outdated code and restructuring some parts for better maintainability.

Code & Autonomous

12/21/24 - 2/2/25

94

224

December 21: Competition Route Updates

• Red+ and Blue+ Routes: Adjusted the red+ and blue+ autonomous routes for competition, ensuring that the timings are now correct. A previous issue where the autonomous didn’t run at all in competition was fixed. The updated red+ route now has improved timing, and the blue+ route has been reflected accordingly. The blue+ route is yet to be tested.

Code�Updates

Code & Autonomous

12/21/24 - 2/2/25

94

225

December 21 (cont) Programming Skills Route: The programming skills route was updated to fix an issue that caused the robot to score one less ring due to a jam. While the issue was identified and resolved, it didn’t have a significant impact on the final score, but the system is now functioning as expected.

Code�Updates

Code & Autonomous

2/3/25 - 2/5/25

94

226

February 5: Autonomous Route Testing and Adjustment

• Autonomous Route Testing: The team ran the autonomous routes multiple times to ensure consistency and accuracy. The values for the routes were tweaked to refine the performance, ensuring smoother and more precise movements. After several iterations and adjustments, the autonomous routes were deemed successful, achieving the desired results for consistent task completion during matches.

Code�Updates

Code & Autonomous

2/3/25 - 2/5/25

94

227

February 4: Odometer Pods, Chassis Tuning, and Improved Ladybrown Controls

• Odometer Pods Implementation: The team implemented the odometer pods, which track the robot’s position more accurately during autonomous routines. Ports for these sensors were adjusted to ensure the proper setup, helping improve localization on the field.

• Chassis PID Tuning: Both angular and lateral PID (Proportional-Integral-Derivative) controls for the chassis were fine-tuned. This ensures that the robot maintains stable and controlled movement in both rotational and lateral directions, improving overall precision during autonomous routines.

Code�Updates

Code & Autonomous

2/3/25 - 2/5/25

94

228

Code�Updates

February 4 (cont) Ladybrown (Wallstake Scorer) 4 Arrows Position Control: The control scheme for the wallstake scorer was enhanced by implementing four arrow buttons for toggling between the different positions. This makes controlling the wallstake positions more intuitive and reduces the number of button presses required for cycling through positions:

• Arrow Down: Position 1 (Rest position).

• Arrow Right: Toggle between Position 2 and 3 (Ready to grab rings from the top and bottom holding rings).

• Arrow Up: Toggle between Position 4 and 5 (Score rings, and score with extra force).

• Arrow Left: Position 5 (Fully extended for tipping over mobile goals and creating space for the climb).

This new control setup makes the driver’s task more intuitive, reducing the complexity of cycling through five positions with a single button press.

Code & Autonomous

2/3/25 - 2/5/25

94

229

February 3: Ladybrown PID Update and Port Adjustments

• Ladybrown PID Update: The PID control for the ladybrown wallstake was updated by adding a kD component (derivative). The kD helps slow down the movement when it is approaching the target position too quickly, improving accuracy and preventing overshooting. This tweak makes the mechanism more responsive and smoother during transitions.

Code�Updates

Code & Autonomous

2/3/25 - 2/5/25

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230

February 3 (cont)

• Port Updates: The ports for various components were changed from the previous setup to ensure the correct hardware was used for optimal performance.

• Intake Color Sort Disabled: The intake color sort feature was temporarily disabled as it was still a work in progress. Instead, the runintake function was used to allow the robot to practice without the obstruction of the sorting process, ensuring that the driver could operate the robot effectively during training.

Code�Updates

Code & Autonomous

Feb 14, 2025

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Code�Autonomous PID Tuning

We are re-tuning PID.

• Undershoot? increase kP
• Overshoot? increase kD
• or decrease kP if appropriate

​

Next we tune anti-windup range. We measure the error for all angle movements from 10 to 180, then get the average and multiply by 1.5.

• This is our data:

Code & Autonomous

2/3 - 2/14/2025

96

232

February 14, 2025: Tuning, Route Adjustments, and Fixes

• PID Tuning for Chassis Movement:

• The team worked on refining the angular and lateral PID controllers to ensure smoother and more precise movement of the robot.

• While the angular PID tuning was successful, lateral movement (side-to-side motion) still requires some additional fine-tuning to eliminate inconsistencies. The team plans to revisit this aspect for more precise movement control during autonomous tasks.

• Variable Name Consistency:

• In an effort to maintain consistent and readable code, the team fixed variable names to match the previously established naming conventions. This ensures that the code is clean, easier to understand, and free of confusion when revisiting it for further adjustments.

Code�Updates - Bottom to Top

Code & Autonomous

2/3 - 2/14/2025

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233

February 14, 2025 (cont)

• Tentative Autonomous Skills Route:

• A short tentative first part of the autonomous skills route was created. This marks an early stage of development for a larger, more complex autonomous routine, which will be expanded and refined as testing progresses.

• Red Positive Route Changes:

• The red positive autonomous route underwent some changes to improve its overall functionality and consistency. This route was tested, but the team noted that it might have some consistency problems due to minor offsets in positioning and timing.

• New Blue Negative Route:

• A new blue negative autonomous route was developed, with the intention of scoring three rings. However, the route was found to score only two rings reliably, with issues arising when attempting to score the third ring. The team is still investigating the cause of these problems, which seem to worsen when any attempts are made to fix them.

Code�Updates - Bottom to Top

Code & Autonomous

2/3 - 2/14/2025

96

234

February 8, 2025: Route Adjustments and Function Enhancements

• Middle Goal Position Adjustment:

• For the new autonomous route, the team experimented with pulling the middle goal further back. This adjustment did not work as expected, and further testing will be required to identify the optimal position for the goal.

• Red Positive Route Completion:

• The red positive route was finalized and successfully tested, though there were some consistency issues due to offsets in movement and positioning. Despite this, the route successfully scored two rings across two mobile goals, after which the robot proceeds to rush towards the ladder touch.

Code�Updates - Bottom to Top

Code & Autonomous

2/3 - 2/14/2025

96

235

Code�Updates - Bottom to Top

February 8, 2025 (cont)

• Red Positive Corner Route Progress:

• The team worked on the red positive corner route and made significant progress. The route is almost complete, but the ladybrown (wall stake scorer) mechanism is cutting off due to time limitations.

• To address this issue, the team created a new function to allow the ladybrown to execute in a loop with a specified timeout, meaning the wall stake scorer will loop every millisecond for the duration of the timeout, ensuring better control and more consistent scoring performance.

• Ports Update:

• A few ports were updated to reflect changes in the hardware setup, ensuring that all components are correctly connected and functioning as intended for optimal performance.

Code & Autonomous

2/3 - 2/14/2025

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• … Continuation of the previous explanation…
• Our plan for the red positive route was the following:

Code�Updates - Bottom to Top

1. rush to goal and grab

2. score preload

3. score bottom ring

4. clamp next stake

5. score top

6. touch ladder

Code & Autonomous

2/3 - 2/14/2025

96

237

February 7, 2025: Updates on Red Positive Autonomous Route

• Red Positive Autonomous Route Revamp

The team revamped the red positive autonomous route, improving both efficiency and reliability. While progress was made, there are still some issues to address.

• Goal Rush Efficiency Needs Improvement

The goal rush maneuver is effective, but there is a need to speed up the process. Currently, the turn-around action when reaching for the mobile goal is not smooth. It moves to the right in a way that knocks over rings, making their placement unpredictable. This inconsistency could affect the success of the autonomous routine, and further adjustments are needed to streamline the goal rush and prevent interference with rings.

Code�Updates - Bottom to Top

Code & Autonomous

2/3 - 2/14/2025

96

238

February 5, 2025: New Autonomous Route and Goal Rush

• PID Retuning for Chassis

The team completed retuning of the PID for the chassis, aiming for better control and more consistent movement across the field. The fine-tuning will help improve accuracy, especially during complex autonomous maneuvers.

• Development of Red Positive Corner Autonomous Route

The red positive corner autonomous route is in progress. While the bot’s positioning is close to ideal, some timing issues are still present. Adjustments will be made to optimize the timing and ensure smooth operation throughout the route.

​

Code�Updates - Bottom to Top

Code & Autonomous

2/3 - 2/14/2025

96

239

Code�Updates - Bottom to Top

Goal Rush Implementation

The goal rush was implemented to ensure that the mobile goal is successfully secured without the risk of disqualification due to crossing the line. Previously, the team attempted to clamp onto the mobile goal and drag it, which was risky. To overcome this, the team introduced a new mechanism—referred to as the “doinker”—to grab the goal from a distance and drag it in safely.

This change mitigates the risk of going past the line, ensuring that the team secures the goal and remains within the scoring boundaries, leading to a solo Autonomous Win Point (AWP). The goal rush strategy is now more reliable, with the doinker acting as a key component for securing points in the autonomous phase.

February 5, 2025 (cont)

Code & Autonomous

2/15 - 2/20/2025

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240

February 3, 2025

• Implemented odometry in the code, which allows for more accurate tracking of the robot’s position on the field.

• Fixed port assignments to ensure all components were correctly mapped and functional.

• Tuned chassis PID control—angular tuning was completed, while lateral tuning was still in progress.

• Updated Ladybrown (wall stake mechanism) control scheme by integrating 4 arrow-based toggling to make position selection more intuitive for the driver.

Code�Updates - Bottom to Top

Code & Autonomous

2/15 - 2/20/2025

96

241

February 15, 2025

• Continued refining the skills route, making adjustments to ensure smoother transitions between different scoring actions.

• Focused on optimizing movement sequences, particularly improving the timing and positioning of the robot to align properly with game elements.

• Identified inconsistencies in the positioning of mechanisms, such as the Ladybrown and intake, which caused some misalignment issues during scoring.

• Began testing different PID tuning values and adjustments to improve accuracy and reliability, ensuring that each movement lands precisely where intended.

• Noted that some positions still required manual corrections, suggesting that further calibration would be needed in the coming days.

Code�Updates - Bottom to Top

Code & Autonomous

2/15 - 2/20/2025

96

242

February 16, 2025

• Fixed a small timing issue with Ladybrown, although additional adjustments were still needed.

• Improved the skills route, making it more efficient and consistent.

• Addressed a recurring inaccuracy problem that led to inconsistent scoring. While some improvements were made, Ladybrown still required further fixing.

Code�Updates - Bottom to Top

Code & Autonomous

2/15 - 2/20/2025

96

243

February 18, 2025

• Made significant progress on the skills route—the currently coded section was fine-tuned and nearly complete.

• Identified potential overheating issues that may be causing inconsistencies.

• Adjusted red positive (red+) autonomous route, tweaked Ladybrown PID control and position settings to improve scoring efficiency.

• Changes were primarily made by Yashmit and others.

Code�Updates - Bottom to Top

Code & Autonomous

2/15 - 2/20/2025

96

244

February 19, 2025

• Optimized timing delays in the currently coded portion of the skills route, reducing unnecessary pauses by a few seconds to improve overall speed and efficiency.

• Focused on making the robot’s movements smoother and more fluid, ensuring that each action transitions seamlessly without excessive waiting times.

• Tested different timing values to find the best balance between consistency and speed, making sure that reducing delays did not negatively impact accuracy.

• Noted that while the route was faster, some inconsistencies remained, especially in cases where mechanical components did not reset quickly enough between actions.

• Prepared for additional tuning in the next iteration to further refine movement precision and minimize wasted motion.

Code�Updates - Bottom to Top

Code & Autonomous

2/15 - 2/20/2025

96

245

February 20, 2025

• Updated the autonomous skills route, fine-tuning it so that the currently coded portion is now nearly perfect.

• Added runIntake -0.1 power for 300ms when pressing Up, Right, or Left, instead of requiring the button to be held down. This allows for better control over the intake mechanism.

• Removed an alternate ending that was previously coded but no longer necessary.

• Added comments to the code for clarity and better organization.

Code�Updates - Bottom to Top

Code & Autonomous

2/23 - 2/26/2025

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246

February 26, 2025

• Worked on refining the right-side portion of the autonomous skills route, focusing on timing adjustments and positional accuracy to ensure more consistent performance.

• Noted that some tweaks were still needed to fine-tune movement sequences and eliminate minor misalignments.

• Implemented changes from Yashmit, Yash, and Likhith, which primarily affected match autonomous routes by optimizing movement paths and refining execution speed.

• Performed additional code cleanup to improve readability and maintainability, making it easier to make further adjustments in the future.

Code�Updates - Bottom to Top

Code & Autonomous

2/23 - 2/26/2025

96

247

February 25, 2025

• Updated the opposite side of the skills route, ensuring that the full routine runs from start to finish in approximately 41-43 seconds.

• Identified positioning issues with Ladybrown during the second quadrant, which need adjustments to ensure reliable scoring.

• Optimized the first quadrant of the skills route, reducing the execution time to 18 seconds while maintaining accuracy.

• Incorporated Ladybrown PID tuning, color sort adjustments, and match autonomous route refinements from Yashmit, Likhith, and Yash, enhancing overall stability and performance.

• Prepared for further testing to validate improvements and adjust any remaining inconsistencies.

Code�Updates - Bottom to Top

Code & Autonomous

2/23 - 2/26/2025

96

248

February 24, 2025

• Refined the first quadrant of the skills route, successfully reducing execution time from 23-25 seconds to around 18 seconds, significantly improving efficiency.

• Final ring scoring and Ladybrown movements still need fine-tuning, as some inconsistencies remain.

• Tuned PID for the updated Ladybrown mechanism, ensuring smoother and more controlled movements.

• Fixed positional errors, particularly in transitions between scoring actions.

• Added a new driver button for the claw, allowing more precise manual control during matches.

Code�Updates - Bottom to Top

Code & Autonomous

2/23 - 2/26/2025

96

249

February 23, 2025

• Addressed issues in the first quadrant, ensuring it now completes the full scoring sequence correctly—though some mechanical inconsistencies still need investigation.

• Mirrored and updated reset positions for the second quadrant, laying the groundwork for a smoother transition between phases.

• Added the second quadrant to the skills routine, but it requires additional testing and refinement to verify its accuracy and efficiency.

• Identified areas for further tuning, including small mechanical adjustments that may be necessary for optimal performance.

Code�Updates - Bottom to Top

Post Competition Reflection

2/8/2025

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POST COMPETITION�Ceres #6th Competition (Feb 8th)

Competition Results:

• 7th in Qualification Matches (5 wins, 1 loss)

The team performed well in the qualification rounds, securing 5 wins out of 6 matches. The 1 loss was attributed to minor issues that were addressed post-match, showcasing the team’s ability to quickly adapt.

• Reached Semi-Finals

The robot advanced to the semi-finals but did not progress to the finals. Despite a strong showing, minor inconsistencies in autonomous behavior and driver operation impacted the performance.

Post Competition Reflection

2/8/2025

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251

Positives:

• Rebuild performed well

After rebuilding and fine-tuning, the robot showed remarkable improvement. The adjustments led to better overall consistency and functionality, enhancing the team’s performance across both autonomous and driver-controlled tasks.

• Faster chassis speed

With a tuned chassis, the robot exhibited increased speed, allowing for faster movement across the field, better positioning, and more effective scoring. This improvement gave the robot an edge in fast-paced matches.

POST COMPETITION�Ceres #6th Competition (Feb 8th)

Post Competition Reflection

2/8/2025

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Positives (cont):

• Intake speed was improved

The intake mechanism was optimized, resulting in faster and more reliable intake of rings. This change allowed the team to execute tasks quicker, contributing to the robot’s overall efficiency.

• Clamp was more consistent

The clamp mechanism was adjusted for better precision. It now holds and releases game elements more consistently, reducing errors when scoring or manipulating mobile goals, thus improving the team’s overall reliability.

• Double-ring Wallstake mechanism worked effectively

The wallstake mechanism, which was rebuilt to handle double rings, functioned better than before. It provided the team with more flexibility and scoring options, which was crucial for securing high scores in the skills challenge.

POST COMPETITION�Ceres #6th Competition (Feb 8th)

Post Competition Reflection

2/8/2025

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253

POST COMPETITION�Ceres #6th Competition (Feb 8th)

Improvement Areas:

• Programming and Driver Skills routes need significant improvement

The current programming and driver skills routes were not up to the level required for state-level competitions. More testing and optimization are necessary to increase point scoring and efficiency in both skills challenges.

• Autonomous routes need to be more consistent

Some autonomous routines showed inconsistencies during matches, with certain maneuvers not executing as expected. Improving the reliability of autonomous movements will be key to performing better in future competitions.

• Driver practice is essential

While the robot was mechanically solid, driver skill was an area for improvement. More practice is needed to ensure that the driver can respond quickly to the field and maintain optimal control during high-pressure situations.

Post Competition Reflection

2/8/2025

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Future Changes & Development Plans:

• Develop stronger autonomous routines

The team plans to enhance autonomous programming, particularly for critical tasks like scoring and positioning. By fine-tuning timing, PID controllers, and sensor feedback, the autonomous performance will be more consistent and reliable.

• Implement a Solo AWP (Autonomous Win Point) strategy

The AWP strategy would aim to maximize points early in the match, potentially allowing for a faster game finish and giving the team an advantage going into driver-controlled periods. This will be key in gaining momentum in matches.

POST COMPETITION�Ceres #6th Competition (Feb 8th)

Post Competition Reflection

2/8/2025

98

255

POST COMPETITION�Ceres #6th Competition (Feb 8th)

Future Changes & Development Plans (cont):

• Refine a Goal Rush strategy

The team plans to improve the Goal Rush strategy to quickly secure mobile goals early in the match. This strategy requires fast, precise movements and better control of the robot during the initial rush, giving the team a solid foundation for the rest of the match.

• Tune and perfect the T1 Climb

A reliable T1 Climb is essential for securing extra points in the competition. The team plans to fine-tune the climb mechanism, ensuring it is consistent and executes flawlessly during matches. The goal is to perform the climb smoothly under various conditions, with minimal risk of failure.

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Our autonomous routine follows a precise sequence designed to maximize scoring efficiency, secure key field elements, and ensure optimal positioning for the driver-controlled period. We begin by moving toward the alliance stake to grab the top ring, ensuring that the alliance stake is properly scored.

Code �Autonomous Route #1 (Negative Side Auton)

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Code �Autonomous Route #1 (Negative Side Auton)

Immediately after, we navigate to the open mobile goal, aligning precisely using onboard sensors to secure it quickly and efficiently. With the mobile goal in our possession, we proceed to the second ring stack, using our intake mechanism to collect rings and score them onto the mobile goal, maximizing early-game points.

(11 point route)

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258

Next, we transition to the wall stake, where we use our specialized wall stake mechanism to score our preload ring accurately. With that task completed, we move swiftly to the autonomous line to collect the two available rings, ensuring no scoring opportunities are wasted.

Code �Autonomous Route #1 (Negative Side Auton)

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Code �Autonomous Route #1 (Negative Side Auton)

Finally, if our alliance partner does not reach the bar touch, we maneuver into position to make physical contact before the autonomous period ends, securing the additional points associated with the bar touch. This carefully planned sequence ensures efficiency and adaptability, allowing us to optimize our scoring potential and positioning for the remainder of the match.

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Code �Autonomous Route #2 (Negative Alternate Ending)

Same as the previous route, except we head to the positive corner during the driver controlled portion to secure one positive corner ASAP.

(11 point route)

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Code �Autonomous Route #3 (Negative Alternate Ending)

Same as the previous route, except we end in the negative corner to grab the rings in the corner to maximize points incase our opponents has a very strong autonomous routine.

(13 point route)

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Our autonomous routine is designed to maximize scoring efficiency while securing key field elements and blocking opponent access. We begin by using our arm, also known as the "doinker," to grab the mobile goal positioned on the autonomous line.

Code �Autonomous Route #4 (Goal Rush)

Chokepoint

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Code �Autonomous Route #4 (Goal Rush)

Chokepoint

This ensures we can quickly gain control and maximize scoring potential for top rings during the autonomous period. After securing the mobile goal, we immediately transition to the wall stake, where we accurately score our preload ring, capitalizing on every available scoring opportunity.

(9 point autonomous)

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With the first mobile goal secured and the preload scored, we swiftly move to grab the second mobile goal, ensuring that we control as many scoring elements as possible early in the match. Simultaneously, we use our doinker to clear our positive corner, removing any obstacles that could hinder our alliance's movement and scoring potential.

Code �Autonomous Route #4 (Goal Rush)