Path Planning and Motion Control of Robotic Arm Based on Neural Network

This paper studies the path planning and motion control method of the robot arm based on neural network, aiming to improve the path planning efficiency and motion control accuracy of the robot arm in complex environments. By introducing the deep reinforcement learning (DRL) method, especially the proximal policy optimization (PPO), this paper proposes a framework for integrated path planning and motion control. Experimental results show that the path generated by PPO in the path planning task has the highest smoothness, the shortest path length and the strongest obstacle avoidance ability. In the motion control task, PPO exhibits the smallest trajectory error, the highest motion accuracy and the best stability. Comprehensive experiments further verify the superior performance of PPO in the combination of path planning and motion control, which can generate smooth, short and safe paths, and accurately control the motion trajectory of the robot arm to ensure the high-quality completion of the task.