ROBOT OBSTACLE AVOIDANCE NAVIGATION BASED ON PATH PLANNING AND DEEP REINFORCEMENT LEARNING

Aimed at the long-distance obstacle avoidance navigation problem of mobile robots, an obstacle avoidance navigation algorithm combining deep reinforcement learning (DRL) and path planning (PL) is proposed. This method used the rapidly exploring random tree (RRT) algorithm to plan the long-distance path. According to the generated path nodes, the long-distance path was divided into several short distances. And for the navigation problem in the short distance, DRL algorithms were used to train an end-to-end obstacle avoidance navigation model with environmental perception and intelligent decision-making capabilities. Simulation experiments show that compared with obstacle avoidance navigation using only DRL, this method greatly improves the long-distance obstacle avoidance navigation performance of mobile robots, and solves the limitations of DRL in long-distance obstacle avoidance navigation tasks.