Dynamic Obstacle Avoidance and Trajectory Planning Based on the Soft Actor-Critic Algorithm

In automated factories, dynamic obstacle avoidance and trajectory planning of robotic manipulators are critical to achieving safe and efficient operations. However, traditional obstacle avoidance methods, such as the artificial potential field, element decomposition, viewable, Voronoi diagram, and probabilistic road map, face many challenges in dealing with complex dynamic environments, such as target unreachable and local minimum problems. A new dynamic obstacle avoidance and trajectory planning framework based on the Soft Actor-Critic (SAC) algorithm is proposed in this paper to address these problems. The framework combines the fast-scaling random tree (RRT) algorithm for global path planning and the SAC algorithm to optimize the local path to adapt to the changes in the dynamic environment. Specifically, the simulation uses Python to construct a URDF (Unified Robot Description Format) model of an open-source robot arm. It applies the SAC algorithm to the model's dynamic obstacle avoidance trajectory planning. The simulation results show that the proposed framework combining RRT and SAC algorithms achieved a high success rate in reaching the target point. This method can effectively find the right trajectory in a complex dynamic environment.