移动机器人导航与对抗控制的强化学习方法研究

The traditional robot navigation and decision-making methods rely heavily on the construction of highprecision 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 transferability 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 information 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 considered 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.