中图分类号：TP273
文献标识码：A
DOI:10.19358/j.issn.2097-1788.2023.03.011
引用格式：李恒，刘轻尘，马麒超.基于深度强化学习和社会力模型的移动机器人自主避障［J］.网络安全与数据治理，2023,42(3):68-73，79.

Autonomous obstacle avoidance for mobile robots based on deep reinforcement learning and social force model

Abstract： Deep reinforcement learning has been widely applied in the field of mobile robot autonomous obstacle avoidance Its basic principle is to simulate continuous trialanderror in the environment and improve the robot’s obstacle avoidance performance by combining reward mechanisms However, the training efficiency of the network varies significantly depending on the task scene, and in crowded scenes, the robot’s behavior may cause interference with humans To address the problems of low training efficiency and robots behaving inappropriately, this paper proposes a selfobstacle avoidance strategy that incorporates the social force model into deep reinforcement learning The strategy firstly considers the future trajectory of humans in the reward function to ensure that the robot understands human intentions and avoids entering the human comfort zone Secondly, during the training process, a priori traditional controller model is introduced and a probabilitybased switching method is designed to randomly switch controller outputs to improve the robot’s exploration efficiency The experimental results show that the proposed method can increase the safety distance between the robot and humans while achieving smooth navigation.

Key words : eep reinforcement learning; social force model; autonomous obstacle avoidance