
Contributed to the Cornell-University-Combat-Robotics/Autonomous-24-25 repository by developing and refining autonomous robotics features over a two-month period. Delivered a Pygame-based simulation with real-world scaling, accurate collision detection, and improved state representation, enabling safer and faster hardware validation. Enhanced the Ram Ram navigation algorithm by fixing orientation bugs, refactoring angle logic, and expanding documentation to streamline onboarding and testing. Built a Python HTTP server for Raspberry Pi camera image capture and integrated data logging for end-to-end algorithm validation. Applied skills in Python, simulation, and embedded systems to improve navigation reliability, testing workflows, and the maintainability of robotics codebases.
January 2025 monthly summary focusing on key accomplishments for the Cornell-University-Combat-Robotics/Autonomous-24-25 project. The sprint delivered higher-fidelity Pygame simulation with real-world scaling, improved state representation, and more accurate rendering of the robot, alongside robust collision handling and performance improvements. The work aligned simulation behavior with real-world parameters to enable faster and safer iteration with real hardware validation.
January 2025 monthly summary focusing on key accomplishments for the Cornell-University-Combat-Robotics/Autonomous-24-25 project. The sprint delivered higher-fidelity Pygame simulation with real-world scaling, improved state representation, and more accurate rendering of the robot, alongside robust collision handling and performance improvements. The work aligned simulation behavior with real-world parameters to enable faster and safer iteration with real hardware validation.
November 2024 monthly summary for Cornell-University-Combat-Robotics/Autonomous-24-25. Delivered field-data capture tooling and improved testing workflows for the Ram Ram navigation algorithm, fixed a critical angle-determination bug, and enhanced documentation to accelerate validation and onboarding. These efforts improved navigation reliability, reduced debugging time, and strengthened the team's ability to validate autonomous behavior in real-world scenarios.
November 2024 monthly summary for Cornell-University-Combat-Robotics/Autonomous-24-25. Delivered field-data capture tooling and improved testing workflows for the Ram Ram navigation algorithm, fixed a critical angle-determination bug, and enhanced documentation to accelerate validation and onboarding. These efforts improved navigation reliability, reduced debugging time, and strengthened the team's ability to validate autonomous behavior in real-world scenarios.

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