深度强化学习求解移动机器人端到端导航问题的研究综述OA北大核心CSTPCD

Autonomous navigation is the prerequisite and foundation for mobile robots to accomplish complex tasks.Traditional autonomous navigation systems rely on the accuracy of maps and cannot adapt to highly complex industrial and service scenarios.End-to-end navigation methods for mobile robots that do not rely on a priori map information and are able to make autonomous decisions through deep reinforcement learning,and environment interaction learning have become a new research hotspot.Most existing classifications cannot comprehensively summarize the challenges and opportunities of end-to-end navigation problems.Based on the characteristics of end-to-end navigation systems,the challenges of the navigation problem are attributed to the key issues of poor perception ability of navigation agents,ineffective learning and poor generalization ability of navigation strategies.The research status and development trends of end-to-end navigation systems are described.Representative research results in recent years addressing these key issues are detailed respectively,and their advantages and shortcomings are summarized.Finally,the future development trends of end-to-end navigation for mobile robots are prospectively envisioned in aspects such as visual language navigation,multi-agents collaborative navigation,end-to-end navigation for fusion super-resolution reconstructed images and interpretable end-to-end navigation,providing certain insights for the research and application of end-to-end navigation for mobile robots.