Experimental study on controlling suspended cable-driven parallel robots for autonomous video-capturing of football games and obtaining the statistics of the games

This research explores the practical application of suspended cable-driven parallel robots for autonomous video-capturing of football games, as well as obtaining the statistics of the games using machine vision and deep learning techniques. On this regard, an algorithm for autonomous video-capturing of football games and extracting the statistics of the games is presented, which is practically implemented on a suspended cable-driven parallel robot available at the Human and Robot Interaction Laboratory, University of Tehran. To achieve this, a computer network is established, comprising a Server and a Client connected via Wi-Fi. The Client computer employs YOLOv5s and machine vision techniques to localize the ball and players on the football field, which facilitates the real-time calculation required for obtaining the statistics of the games. These statistics encompass various aspects such as goal count, outs, corners, percentage of ball presence in each field half, and heatmaps of the players of each team and the ball. Furthermore, the algorithm calculates the optimal camera position for capturing game footage and transmits this information to the Server system, which is responsible for controlling the robot. On the Server system, the current and desired positions of the robot are taken into consideration, and a smooth trajectory is planned between these coordinates using a septic polynomial, ensuring precise and fluid movements. To facilitate this, a digital controller optimized by the Simulated Annealing algorithm is designed. Through practical experimentation, this study demonstrates the viability of introducing autonomy to suspended cable-driven parallel robots for video-capturing of football games and extracting the statistics of the games.