
Contributed to the Cornell-University-Combat-Robotics/Autonomous-24-25 repository by delivering four core platform enhancements focused on control reliability, testing infrastructure, and data management. Developed and expanded RamRamTest with comprehensive CSV-based test data, improving simulation validation for robot positions, velocities, and sensor data. Overhauled the robot movement and control algorithm in Python, consolidating turn and speed predictions and extending return values for easier debugging and more robust control. Integrated a pre-trained greyscale model to enable grayscale image processing without code changes. Improved repository hygiene by updating .gitignore to exclude generated CSV files, streamlining data engineering workflows and reducing accidental data tracking.
January 2025 monthly performance for Cornell-University-Combat-Robotics/Autonomous-24-25: Delivered core platform enhancements with a focus on testing, control reliability, model integration, and repository hygiene. Expanded RamRamTest data coverage and testing infrastructure; overhauled robot movement and data handling for more robust control; integrated a pre-trained greyscale model; and improved data hygiene by excluding generated CSV data from version control. These changes reduce risk, enable faster debugging, and broaden sensing/decision capabilities in simulation and field tests.
January 2025 monthly performance for Cornell-University-Combat-Robotics/Autonomous-24-25: Delivered core platform enhancements with a focus on testing, control reliability, model integration, and repository hygiene. Expanded RamRamTest data coverage and testing infrastructure; overhauled robot movement and data handling for more robust control; integrated a pre-trained greyscale model; and improved data hygiene by excluding generated CSV data from version control. These changes reduce risk, enable faster debugging, and broaden sensing/decision capabilities in simulation and field tests.

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