基于改进强化学习的移动机器人动态避障方法

Aiming to solve the problems of long planning trajectory, slow travel speed and poor robustness of mobile robot dynamic obstacle avoidance in unknown environment, a mobile robot dynamic obstacle avoidance method based on improved reinforcement learning is proposed. According to its own speed, target position and laser radar information, the mobile robot can directly obtain the action signal to achieve end-to-end control. Based on distance gradient guidance and angle gradient guidance, the mobile robot is optimized towards the end point and the convergence speed of the algorithm is accelerated. Combined with convolution neural network, high-quality features are extracted from multi-dimensional observation data to improve the effect of strategy training. The simulation results show that the training speed of the proposed method is increased by 40%, the track length is reduced by more than 2.69%, and the average line speed is increased by more than 11.87% in the multi-dynamic obstacle environment. Compared with the existing mainstream obstacle avoidance methods, the proposed method has the advantages of short planning trajectory, fast travel speed, stable performance and so on. It can realize the smooth obstacle avoidance of mobile robots in the multi-obstacles environment.