计算机工程与应用2025，Vol.61Issue(4)：90-98,9.DOI:10.3778/j.issn.1002-8331.2405-0088
动态环境下共融机器人深度强化学习导航算法
Deep Reinforcement Learning Navigation Algorithm for Coexisting-Cooperative-Cognitive Robots in Dynamic Environment
摘要
Abstract
In the past few decades,navigation algorithms for mobile service robots have been extensively studied,but intelli-gent agents still lack the complexity and cooperation exhibited by humans in crowded environments.With the continuous expansion of human-machine integration applications,collaboration between robots and humans in shared workspaces will become increasingly important.Therefore,the next generation of mobile service robots needs to meet social require-ments in order to be accepted by humans.In order to enhance the autonomous navigation ability of multi-agent systems in dynamic scenarios,a deep reinforcement learning obstacle avoidance algorithm for coexisting-cooperative-cognitive robots in dynamic environments is proposed to address the issues of low social adaptability and finding the optimal value func-tion in multi-agent navigation.A motion model that is more closely related to human behavior is established and added to the deep reinforcement learning framework to improve the cooperation of coexisting-cooperative-cognitive robots.In order to enhance the perceived safety of pedestrians based on their physical safety,a reward function is redefined.Non-linear deep neural networks are used instead of traditional value functions to solve the problem of finding the optimal value function.Simulation experiments show that compared to the latest deep reinforcement learning navigation methods,the proposed method achieves a 100%navigation success rate without increasing navigation time and without any collisions.The results indicate that this method maximizes the satisfaction of human social principles for the fusion robot,while effectively moving towards the goal and improving the perceived safety of pedestrians.关键词
服务机器人/避障算法/深度强化学习/最优值函数/奖励函数Key words
service robot/obstacle avoidance/deep reinforcement learning/optimal value function/reward function分类
信息技术与安全科学引用本文复制引用
顾金浩,况立群,韩慧妍,曹亚明,焦世超..动态环境下共融机器人深度强化学习导航算法[J].计算机工程与应用,2025,61(4):90-98,9.基金项目
国家自然科学基金(62272426) （62272426）
山西省科技重大专项计划"揭榜挂帅"项目(202201150401021) （202201150401021）
山西省自然科学基金(202303021211153,202203021222027,202303021212189,202203021212138) （202303021211153,202203021222027,202303021212189,202203021212138）
山西省科技成果转化引导专项(202104021301055). （202104021301055）
