Obstacle avoidance path planning of mobile robot based on improved DQN

Aiming at the problems such as long learning time, poor exploration ability and sparse reward in obstacle avoidance path planning for robots under general reinforcement learning methods, an obstacle avoidance path planning for mobile robots based on improved Deep Q network (DQN) was proposed. Firstly, based on the traditional DQN algorithm, the obstacle learning rules are designed to remember and avoid obstacles, avoid repeated learning of the same obstacle, and improve the learning efficiency and success rate. Secondly, a reward optimization method is proposed, which uses the difference of access times between states to give rewards, balances the access times of state points, and avoids excessive access. At the same time, by calculating the Euclidean distance from the target point, it is inclined to choose the path close to the target, and cancel the penalty of staying away from the target, and realize the adaptive optimization of the reward mechanism. Finally, the dynamic exploration factor function is designed, and the reinforcement learning strategy is used to select action and learning in the later training to improve the performance and learning efficiency of the algorithm. The experimental simulation results show that compared with the traditional DQN algorithm, the improved algorithm can shorten the training time by 40.25%, the obstacle avoidance success rate by 79.8% and the path length by 2.25%, all of which show better performance. © 2024 Editorial Department of Journal of Chinese Inertial Technology. All rights reserved.