FRC7902/Reefscape2025
Feb 2025 – Feb 2025
1 Month active
Languages Used
Java
Technical Skills
Command-Based FrameworkControl SystemsJavaRobot ControlRoboticsSwerve Drive
Arjun Bhatia
PROFILE
Arjun Bhatia
During February 2025, Argon Boron contributed to the Reefscape2025 repository by developing targeted control system enhancements for FRC7902’s robot. Focusing on Java and leveraging the Command-Based Framework, Argon refactored elevator control logic to improve setpoint accuracy, PID tuning, and unit conversions, addressing issues with meters-to-rotations calculations for the gear ratio. Additionally, Argon implemented driver control features, including strafing via trigger axes and an angle-snapping command, which improved teleoperation precision and maneuverability. These updates, grounded in control systems and robotics expertise, reduced control errors and streamlined future tuning, reflecting a thoughtful, detail-oriented approach to robot software engineering.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
2
Lines of code
249
Activity Months1
Your Network
6 people
Work History
February 2025
2 Commits • 2 Features
Feb 1, 2025February 2025 performance summary for Reefscape2025 (FRC7902): Delivered targeted control system refinements to enhance precision, reliability, and maneuverability. Elevator control tuning refactored constants and utilities to improve setpoint accuracy, PID gains, CAN IDs, and unit conversions, including fixing meters/rotations conversions for the gear ratio. Driver controls were enhanced with strafing via trigger axes and an angle-snapping command to improve handling and precision. These changes, supported by bug fixes addressing kV/kA/feedforward behavior, reduce control errors, increase reliability, and streamline tuning for future iterations and competition readiness.
2 Commits • 2 Features
Feb 1, 2025February 2025 performance summary for Reefscape2025 (FRC7902): Delivered targeted control system refinements to enhance precision, reliability, and maneuverability. Elevator control tuning refactored constants and utilities to improve setpoint accuracy, PID gains, CAN IDs, and unit conversions, including fixing meters/rotations conversions for the gear ratio. Driver controls were enhanced with strafing via trigger axes and an angle-snapping command to improve handling and precision. These changes, supported by bug fixes addressing kV/kA/feedforward behavior, reduce control errors, increase reliability, and streamline tuning for future iterations and competition readiness.
February 2025
Activity
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Quality Metrics
Correctness85.0%
Maintainability80.0%
Architecture80.0%
Performance70.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Java
Technical Skills
Command-Based FrameworkControl SystemsJavaRobot ControlRoboticsSwerve DriveTeleoperation Control
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