Development of an autonomous chess robot system using computer vision and deep learning

• Developed a completed autonomous chess robot system which encompasses a robot, hardwares, and softwares and computer vision. • Developed a deep learning model for chess piece recognition and chess position detection with high accuracy chess piece recognition. • The computation time for a chess move is about 2 s for each chess position, and the average time for the robot to complete chess piece movement is about 60 s for one chess position. • The developed chess robot system operates stably and accurately. It can autonomously play a completed chess game against people or identify chess position for a pre-arranged chess setup. In this research, a low-cost autonomous chess robot system is developed using computer vision, deep learning, and robot control. The system comprises a chessboard, a camera system, and a 4-DOF SCARA robot. The entire system is managed by software running on a computer. Additionally, a deep learning model has been created for chess piece recognition and position detection. The calculation of chess moves is performed using the minimax algorithm within the Stockfish chess engine. Results indicate that the computation time for a chess move is approximately 2 s per chess position, while the average time for the robot to execute a chess piece movement is from 20 to 90 s for one position, depending on the type of chess move. The developed chess robot system operates stably and accurately, capable of autonomously playing a complete chess game against humans or identifying chess positions for a pre-arranged setup. Moreover, the fabrication cost of the robotic arm and its control system is approximately $100, making it both affordable and suitable for training and entertainment-focused chess robot systems. The results demonstrated that the autonomous chess robot system developed in this study is feasible for real-world applications for chess playing or chess training systems.