frc2399/2025-season
Jan 2025 – Apr 2025
4 Months active
Languages Used
BatchJSONJavaShellYAMLGradle
Technical Skills
Autonomous NavigationCode DocumentationCommand-Based FrameworkComputer VisionConfiguration ManagementControl Systems
Maya Houghton
PROFILE
Maya Houghton
Over four months, Michael Houghton developed and stabilized advanced autonomous navigation and control systems for the frc2399/2025-season repository. He engineered modular drive and perception subsystems in Java, integrating computer vision and AprilTags for robust pose estimation and trajectory planning. His work included refining swerve drive controls, implementing command-based routines, and tuning PID controllers to improve reliability and field performance. Michael enhanced subsystem integration, streamlined build management with Gradle, and expanded automated testing and deployment workflows. By consolidating autonomous routines and optimizing endgame logic, he delivered a maintainable, production-ready robotics codebase that improved autonomous reliability, safety, and competitive readiness.
Overall Statistics
Feature vs Bugs
65%Features
Repository Contributions
205Total
Bugs
38
Commits
205
Features
72
Lines of code
32,339
Activity Months4
Your Network
19 people
Work History
April 2025
16 Commits • 3 Features
Apr 1, 2025April 2025 highlights: Delivered key autonomous enhancements, robust climbing controls, and streamlined endgame logic, contributing to higher field reliability and scoring efficiency. Consolidated autonomous paths, trajectories, and tuning across BLUE/RED paths, L4 scoring, and noodling to improve trajectory reliability and auto success rates. Implemented POV-pad climbing controls with tuned soft limits and adjustments to servo usage, enabling safer, dual-climb operation. Optimized endgame logic to return booleans directly, reducing checks and edge-case risk. Algae wrist geometry updated to align with processor angle and refreshed version metadata for better traceability. Added reliability improvements including removal of odometry resets from autons and pre-comp tuning for Buckeye to stabilize performance in competitive conditions.
16 Commits • 3 Features
Apr 1, 2025April 2025 highlights: Delivered key autonomous enhancements, robust climbing controls, and streamlined endgame logic, contributing to higher field reliability and scoring efficiency. Consolidated autonomous paths, trajectories, and tuning across BLUE/RED paths, L4 scoring, and noodling to improve trajectory reliability and auto success rates. Implemented POV-pad climbing controls with tuned soft limits and adjustments to servo usage, enabling safer, dual-climb operation. Optimized endgame logic to return booleans directly, reducing checks and edge-case risk. Algae wrist geometry updated to align with processor angle and refreshed version metadata for better traceability. Added reliability improvements including removal of odometry resets from autons and pre-comp tuning for Buckeye to stabilize performance in competitive conditions.
April 2025
March 2025
90 Commits • 30 Features
Mar 1, 2025March 2025 (frc2399/2025-season) monthly summary: Focused on stabilizing core robot behavior, expanding autonomous capabilities, and improving testing, logging, and deployment readiness. Business value centers on reliable autonomous operation, safer teleop, and faster iteration cycles.
March 2025
90 Commits • 30 Features
Mar 1, 2025March 2025 (frc2399/2025-season) monthly summary: Focused on stabilizing core robot behavior, expanding autonomous capabilities, and improving testing, logging, and deployment readiness. Business value centers on reliable autonomous operation, safer teleop, and faster iteration cycles.
February 2025
73 Commits • 27 Features
Feb 1, 2025February 2025 monthly summary for frc2399/2025-season focusing on performance, reliability, and readiness for Betabot deployment. Delivered foundational stabilization and calibration work, major bug fixes, and several feature enhancements that improve robot reliability, control, and maintainability. Demonstrated strong collaboration with testing infrastructure, vendor updates, and code hygiene to support rapid iteration and production readiness.
73 Commits • 27 Features
Feb 1, 2025February 2025 monthly summary for frc2399/2025-season focusing on performance, reliability, and readiness for Betabot deployment. Delivered foundational stabilization and calibration work, major bug fixes, and several feature enhancements that improve robot reliability, control, and maintainability. Demonstrated strong collaboration with testing infrastructure, vendor updates, and code hygiene to support rapid iteration and production readiness.
February 2025
January 2025
26 Commits • 12 Features
Jan 1, 2025January 2025 delivered a robust drivetrain and perception foundation for frc2399/2025-season, enabling autonomous navigation and reliable robot operation. Implemented DriveSubsystem core with gyro and chassis drop-in, validated vision-based pose estimation, and established DriveToPose scaffolding with pose rotations. Completed motor control refinements, IO/system reliability fixes, and swerve/config updates, along with Alphabot filtering, static controller refactor, and vendor dependency updates for maintainability and stability. These changes provide tangible business value through more predictable autonomous performance, easier tuning, and a smaller maintenance surface.
January 2025
26 Commits • 12 Features
Jan 1, 2025January 2025 delivered a robust drivetrain and perception foundation for frc2399/2025-season, enabling autonomous navigation and reliable robot operation. Implemented DriveSubsystem core with gyro and chassis drop-in, validated vision-based pose estimation, and established DriveToPose scaffolding with pose rotations. Completed motor control refinements, IO/system reliability fixes, and swerve/config updates, along with Alphabot filtering, static controller refactor, and vendor dependency updates for maintainability and stability. These changes provide tangible business value through more predictable autonomous performance, easier tuning, and a smaller maintenance surface.
Activity
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Quality Metrics
Correctness83.0%
Maintainability83.4%
Architecture80.8%
Performance77.8%
AI Usage20.4%
Skills & Technologies
Programming Languages
BatchGradleJSONJavaShellYAML
Technical Skills
AprilTagsAutonomous NavigationAutonomous Path GenerationAutonomous Path PlanningAutonomous Path TuningAutonomous ProgrammingAutonomous Routine ConfigurationAutonomous Routine ProgrammingAutonomous RoutinesAutonomous TuningBuild ManagementBuild Tool ConfigurationCAN Bus OptimizationCode CleanupCode Documentation
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