用墙跟随机制解决多机器人导航中的局部极小陷阱问题
Escaping Local Minima: Hybrid Artificial Potential Field with Wall-Follower for Decentralized Multi-Robot Navigation
- 在APF基础上引入墙跟随行为，实现无地图下的自主避障
- 实验显示成功率显著高于现有方法，可应对非凸与动态障碍物
- 适合分布式多机器人系统，无需通信或全局信息
针对非凸障碍物环境中缺乏全局环境信息的去中心化多机器人导航挑战，传统反应式方法如人工势场（APF）虽高效但易陷入局部极小值。现有解决方案或依赖机器人间通信，或仅适用于单机场景，难以有效克服非凸障碍。本文提出一种仅利用局部传感器与状态信息的导航方法，通过在APF中融合墙跟随（WF）行为，使机器人在存在非凸及动态障碍（含其他机器人）时仍能摆脱局部极小。设计了基于规则和专家示范训练的编码器网络两种切换策略。实验表明，该方法相比最先进方法显著提升成功率，有效突破局部极小限制。
原文摘要 · Abstract (English)
We tackle the challenges of decentralized multi-robot navigation in environments with nonconvex obstacles, where complete environmental knowledge is unavailable. While reactive methods like Artificial Potential Field (APF) offer simplicity and efficiency, they suffer from local minima, causing robots to become trapped due to their lack of global environmental awareness. Other existing solutions either rely on inter-robot communication, are limited to single-robot scenarios, or struggle to overcome nonconvex obstacles effectively. Our proposed methods enable collision-free navigation using only local sensor and state information without a map. By incorporating a wall-following (WF) behavior into the APF approach, our method allows robots to escape local minima, even in the presence of nonconvex and dynamic obstacles including other robots. We introduce two algorithms for switching between APF and WF: a rule-based system and an encoder network trained on expert demonstrations. Experimental results show that our approach achieves substantially higher success rates compared to state-of-the-art methods, highlighting its ability to overcome the limitations of local minima in complex environments
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。