控制理论与应用2025，Vol.42Issue(9)：1757-1765,9.DOI:10.7641/CTA.2024.30164
移动机器人导航与对抗控制的强化学习方法研究
Research on reinforcement learning methods for navigation and adversarial control in mobile robots
摘要
Abstract
The traditional robot navigation and decision-making methods rely heavily on the construction of high-precision maps,and are difficult to adapt to dynamic and complex application scenarios.In addition,the existing navigation and control methods based on machine learning algorithms have the defects of unsatisfactory generalization and transfer-ability in real systems.To solve the above problems,a mobile robot navigation and real-time confrontation method based on multimodal information fusion and reinforcement learning framework is proposed in this paper.First of all,various information preprocessing modules are used to preprocess and fuse the RGB images,LiDAR data and other vector infor-mation collected by the robot,so as to realize the robot's comprehensive perception of the environment.Then,the system directly outputs the motion control commands of the robot based on the action network,allowing for the end-to-end control of the mobile robot without a model.Furthermore,the noise and dynamic factors in the real environment are fully consid-ered in the simulation system,and the model is fine-tuned and corrected by using the test data migrated to the real robot.Finally,experiments on navigation and real-time confrontation tasks of different difficulties are carried out in the simulation environment and the real environment,and the effectiveness of the proposed robot navigation and real-time confrontation method based on reinforcement learning is verified.关键词
强化学习/移动机器人/导航避障/对抗策略Key words
reinforcement learning/mobile robot/navigation and obstacle avoidance/confrontation policy引用本文复制引用
蒋坤,操菁瑜,柳文章,孙长银,董璐..移动机器人导航与对抗控制的强化学习方法研究[J].控制理论与应用,2025,42(9):1757-1765,9.基金项目
国家自然科学基金项目(62236002,61921004,62173251)资助.Supported by the National Natural Science Foundation of China(62236002,61921004,62173251). （62236002,61921004,62173251）
