中圖分類號：TP273
文獻標識碼：A
DOI:10.19358/j.issn.2097-1788.2023.03.011
引用格式：李恒，劉輕塵，馬麒超.基于深度強化學(xué)習和社會力模型的移動機器人自主避障［J］.網(wǎng)絡(luò)安全與數(shù)據(jù)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