Robotic Arm Assistance System Based on Simple Stereo Matching and Q-Learning Optimization

This study presents a stereo vision robotic arm assistance system, in which five degrees of catching can be performed by the robot arm in a single instance. The algorithm of the control system is built for population-based optimization and specifically aimed to assist people with disabilities. The proposed stereo vision-based robot arm system enables users to manipulate objects based on the robot's ability to aim at objects by using computer vision. The stereo vision system counts the parameters by focusing on the real-world position of the instance in the coordinate system. A trained deep fully connected network is then adopted to compensate the location measurement errors incurred by the inaccurate parameters measured from the deep learning procedure. Subsequently, the proposed Q-learning-based swarm optimization algorithm is adopted to solve the forward kinematics problem and count the angles of each servo. The performance of the robot arm is compared with several real-life experiments to test its ability to grip a target object in different positions.