计算机工程与应用2024，Vol.60Issue(14)：1-13,13.DOI:10.3778/j.issn.1002-8331.2312-0256
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
Research Review on Deep Reinforcement Learning for Solving End-to-End Navigation Prob-lems of Mobile Robots
摘要
Abstract
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.关键词
端到端导航/深度强化学习/感知能力/学习效率/泛化能力Key words
end-to-end navigation/deep reinforcement learning/perception ability/learning efficiency/generalization ability分类
信息技术与安全科学引用本文复制引用
何丽,姚佳程,廖雨鑫,张文智,卢赵清,袁亮,肖文东..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[J].计算机工程与应用,2024,60(14):1-13,13.基金项目
新疆维吾尔自治区自然科学基金(2022D01C392) （2022D01C392）
国家自然科学基金(62063033) （62063033）
新疆维吾尔自治区重点研发计划项目(2022B01050-2). （2022B01050-2）
