
Roxana Andreea Popa contributed to the FRCTeam360/RainMaker25 repository by developing and refining autonomous navigation and control systems for competitive robotics. Over two months, she implemented robust autonomous routines, overhauled subsystems such as the AlgaeShooter, and centralized drivetrain constants to improve maintainability and cross-robot consistency. Her work included integrating PathPlanner for autonomous path following, tuning elevator control parameters, and introducing USB-based logging for enhanced data capture. Using Java and embedded systems expertise, Roxana addressed hardware interface challenges, encoder integration, and command-based framework improvements, demonstrating depth in both software engineering and robotics while delivering features that improved reliability and field performance.

February 2025 monthly summary for FRCTeam360/RainMaker25: Delivered core subsystem overhauls and reliability improvements, including algae subsystem overhaul, elevator tuning, robust autonomous routines, and USB logging. These work items improve accuracy, control stability, autonomous reliability, and data capture for post-run analysis.
February 2025 monthly summary for FRCTeam360/RainMaker25: Delivered core subsystem overhauls and reliability improvements, including algae subsystem overhaul, elevator tuning, robust autonomous routines, and USB logging. These work items improve accuracy, control stability, autonomous reliability, and data capture for post-run analysis.
January 2025 recap for FRCTeam360 RainMaker25: Focused on delivering autonomous capabilities, stabilizing Woodbot hardware configuration, and reducing runtime dependencies to accelerate field-ready performance. Key initiatives included establishing PathPlanner-driven autonomy, centralizing drivetrain constants, and introducing auto-balancing sequences to improve scoring reliability.
January 2025 recap for FRCTeam360 RainMaker25: Focused on delivering autonomous capabilities, stabilizing Woodbot hardware configuration, and reducing runtime dependencies to accelerate field-ready performance. Key initiatives included establishing PathPlanner-driven autonomy, centralizing drivetrain constants, and introducing auto-balancing sequences to improve scoring reliability.
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