A deep reinforcement learning based method for real-time path planning and dynamic obstacle avoidance

Abstract

In a dynamic environment, the moving obstacle makes the path planning of the manipulator very difficult. Therefore, this paper proposes a path planning with dynamic obstacle avoidance method of the manipulator based on a deep reinforcement learning algorithm soft actor-critic (SAC). To avoid the moving obstacle in the environment and make real-time planning, we design a comprehensive reward function of dynamic obstacle avoidance and target approach. Aiming at the problem of low sample utilization caused by random sampling, in this paper, prioritized experience replay (PER) is employed to change the weight of samples, and then improve the sampling efficiency. In addition, we carry out the simulation experiment and give the results. The result shows that this method can effectively avoid moving obstacles in the environment, and complete the planning task with a high success rate.