Earl-Of-March-FRC/2025-7476-Reefscape
Jan 2025 – Mar 2025
3 Months active
Languages Used
JavaPython
Technical Skills
Code OrganizationCommand-Based FrameworkControl SystemsEmbedded SystemsFRCGit
Angela Zheng
PROFILE
Angela Zheng
Angela Zheng contributed to the Earl-Of-March-FRC/2025-7476-Reefscape repository by developing and modernizing autonomous navigation, control, and sensing systems for a robotics platform. She implemented alliance-aware pathfinding, dynamic pose estimation, and profiled PID controllers to improve autonomous precision and reliability. Angela refactored subsystems such as the arm and shooter for SparkMax integration, centralized configuration management, and enhanced telemetry for real-time debugging. Using Java and Python, she applied command-based programming, CAN Bus communication, and sensor integration to streamline operator input and subsystem control. Her work enabled faster iteration, safer operation, and a scalable codebase, demonstrating strong depth in robotics engineering.
Overall Statistics
Feature vs Bugs
88%Features
Repository Contributions
59Total
Bugs
3
Commits
59
Features
22
Lines of code
4,934
Activity Months3
Your Network
12 people
Work History
March 2025
12 Commits • 4 Features
Mar 1, 2025March 2025 (Earl-Of-March-FRC/2025-7476-Reefscape) monthly performance review focused on delivering robust autonomous capabilities, improving launcher reliability, and enabling perceptive sensing. Key features delivered include alliance-aware pathfinding to the launch spot with a driver-controller A button trigger and new pathfinding constants; enhanced navigation to the barge launching zone with dynamic target poses and alliance-based target calculations; launcher telemetry improvements with velocity (rad/s) logging and PID/setpoint tuning; and arm color detection with a ColorHelpers utility. Major bugs fixed include corrected target rotation logic for barge zone navigation, PID-related issues in the launching path, a velocity setpoint typo, and enabling dynamic target poses via a Supplier for MoveToTargetPoseCmd to support real-time pose changes. Overall impact: the team reduced autonomous setup time, increased shot reliability and consistency, improved run-time telemetry for debugging, and expanded sensing capabilities. These changes enhance field readiness, scoring reliability, and debugging efficiency for rapid iteration. Technologies/skills demonstrated: autonomous navigation and pathfinding, alliance-aware pose logic, dynamic pose propulsion, PID tuning and telemetry, real-time target pose handling, and color sensing via a dedicated color-detection module and utilities.
12 Commits • 4 Features
Mar 1, 2025March 2025 (Earl-Of-March-FRC/2025-7476-Reefscape) monthly performance review focused on delivering robust autonomous capabilities, improving launcher reliability, and enabling perceptive sensing. Key features delivered include alliance-aware pathfinding to the launch spot with a driver-controller A button trigger and new pathfinding constants; enhanced navigation to the barge launching zone with dynamic target poses and alliance-based target calculations; launcher telemetry improvements with velocity (rad/s) logging and PID/setpoint tuning; and arm color detection with a ColorHelpers utility. Major bugs fixed include corrected target rotation logic for barge zone navigation, PID-related issues in the launching path, a velocity setpoint typo, and enabling dynamic target poses via a Supplier for MoveToTargetPoseCmd to support real-time pose changes. Overall impact: the team reduced autonomous setup time, increased shot reliability and consistency, improved run-time telemetry for debugging, and expanded sensing capabilities. These changes enhance field readiness, scoring reliability, and debugging efficiency for rapid iteration. Technologies/skills demonstrated: autonomous navigation and pathfinding, alliance-aware pose logic, dynamic pose propulsion, PID tuning and telemetry, real-time target pose handling, and color sensing via a dedicated color-detection module and utilities.
March 2025
February 2025
38 Commits • 14 Features
Feb 1, 2025February 2025 (2025-02) monthly summary for Earl-Of-March-FRC/2025-7476-Reefscape. The team delivered foundational configuration improvements, precision control enhancements, and stability-focused refactors that jointly increase reliability, tunability, and deployment confidence while reducing maintenance burdens and enabling faster iteration.
February 2025
38 Commits • 14 Features
Feb 1, 2025February 2025 (2025-02) monthly summary for Earl-Of-March-FRC/2025-7476-Reefscape. The team delivered foundational configuration improvements, precision control enhancements, and stability-focused refactors that jointly increase reliability, tunability, and deployment confidence while reducing maintenance burdens and enabling faster iteration.
January 2025
9 Commits • 4 Features
Jan 1, 2025January 2025 performance summary for Reefscape (repo: Earl-Of-March-FRC/2025-7476-Reefscape). Focused on delivering feature extensions, subsystem modernization, and input handling improvements to boost reliability and autonomous performance. Key features delivered: Shoulder Auto Movement Profiling (profiled PID for shoulder), Arm subsystem modernization with SparkMax control and command restructuring (ArmMoveAuto -> ArmAuto; ArmAuto -> ArmPID), Shooter subsystem SparkMax config with ShooterConfigs and SetShooterSpeed plus granular commands, Controller input modernization to CommandXboxController with merge-conflict cleanup. Major bugs fixed: no major defects; one merge-conflict resolution improving stability. Overall impact: higher autonomous precision and safety, easier maintenance, and finer-grained shooter control. Technologies demonstrated: SparkMax control, profiled PID, command-based architecture, modern input handling. Business value: more reliable autonomous ops, faster iteration, scalable codebase for future features.
9 Commits • 4 Features
Jan 1, 2025January 2025 performance summary for Reefscape (repo: Earl-Of-March-FRC/2025-7476-Reefscape). Focused on delivering feature extensions, subsystem modernization, and input handling improvements to boost reliability and autonomous performance. Key features delivered: Shoulder Auto Movement Profiling (profiled PID for shoulder), Arm subsystem modernization with SparkMax control and command restructuring (ArmMoveAuto -> ArmAuto; ArmAuto -> ArmPID), Shooter subsystem SparkMax config with ShooterConfigs and SetShooterSpeed plus granular commands, Controller input modernization to CommandXboxController with merge-conflict cleanup. Major bugs fixed: no major defects; one merge-conflict resolution improving stability. Overall impact: higher autonomous precision and safety, easier maintenance, and finer-grained shooter control. Technologies demonstrated: SparkMax control, profiled PID, command-based architecture, modern input handling. Business value: more reliable autonomous ops, faster iteration, scalable codebase for future features.
January 2025
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Quality Metrics
Correctness90.4%
Maintainability91.0%
Architecture87.6%
Performance85.8%
AI Usage20.0%
Skills & Technologies
Programming Languages
JavaPython
Technical Skills
Autonomous NavigationAutonomous ProgrammingCAN BusCode FormattingCode OrganizationCode RefactoringCommand-Based FrameworkCommand-Based ProgrammingConfigurationConstants ManagementControl SystemsDocumentationEmbedded SystemsFRCField Navigation
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