This paper presents the design and implementation of an integrated robotic system capable of detecting objects through computer vision and making decisions based on logic strategies to perform physical tasks. For that, the system uses a robotic arm to play the Tic-Tac-Toe game utilizing a Q-learning algorithm to determine optimal moves. The system can be controlled using a graphical interface that enables real-time monitoring, facilitating seamless interaction between the user and the robotic arm. Three algorithms with different decision strategies were developed: a random decision algorithm, the MiniMax algorithm, and Q-learning, a reinforcement-learning algorithm. The results obtained highlight the control of the robotic arm using kinematic equations, the training of a robust YOLOv5 model, and the effective learning capability of a Q-learning algorithm. The proposed system presents practical implementation of the robotic system which can be used as a basis for further projects and for teaching robotics.
Timóteo et al. (Fri,) studied this question.