Research on Industrial Robot Motion Planning Algorithm Based on Deep Learning

This research converges on the motion planning algorithm of industrial robots based on deep learning. During the research process, a dataset encompassing a vast number of industrial robot motion scenarios was initially constructed, covering diverse working environments, task requirements, and initial states of robots, et cetera. The deep reinforcement learning (DRL) algorithm was harnessed with the aim of maximizing the rewards for robots to complete tasks in complex environments, enabling robots to learn the optimal motion strategies through continuous trial-and-error. Concurrently, a convolutional neural network (CNN) was introduced to extract and process the environmental information, enhancing the robot's perception ability of the environment. Through experimental testing, this algorithm has remarkably elevated the efficiency and accuracy of industrial robot motion planning. Compared with traditional algorithms, in complex environments, the planning time has been averagely curtailed by 35%, the path-planning success rate has been augmented from 60% to 85%, and the collision risk of robots during the motion process has been effectively mitigated, with the number of collisions being approximately 40% less than that of traditional algorithms, demonstrating the formidable advantages and application potential of deep-learning algorithms in the field of industrial robot motion planning.