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
Worked on the Reefscape2025 repository to deliver targeted control system enhancements for a robotics platform, focusing on elevator and driver control subsystems. Refactored elevator constants and utilities in Java to improve setpoint accuracy, PID tuning, CAN ID management, and unit conversions, addressing issues with meters-to-rotations calculations for the gear ratio. Enhanced driver controls by implementing strafing via controller trigger axes and introducing an angle-snapping command, both aimed at increasing maneuverability and precision during teleoperation. Leveraged the Command-Based Framework and control systems expertise to stabilize motor behavior, reduce control errors, and streamline future tuning for competition readiness.
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 peopleWork 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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