Mapping Robot Paths in Robotics Competitions with Computer Vision

The article explores the application of object detection models, segmentation, and tracking in mapping robot paths during the First Robotics Competition (FRC), emphasizing the role of these technologies in enhancing strategy and analysis. It outlines a project that utilizes Node.js and Python, in conjunction with Roboflow's tools, to detect robots in video frames, map their movements onto a 2D field diagram, and store results in a JSON file for later analysis. The process involves building and training robot detection and field segmentation models, segmenting fields for coordinate mapping, and implementing a tracking algorithm to maintain consistent identification of robots across video frames. The approach allows for accurate estimation of robots' world positions by subdividing the field into regions and iteratively refining these estimates, with the ultimate goal of enabling detailed analysis of robot performance and strategies in competitions. The project also provides a practical demonstration of how such techniques can be adapted for various activities and sports, with full code available on GitHub for broader application and experimentation.