提升机器人在复杂环境中的搜寻效率，实现快速精准定位目标。
HEATS: A Hierarchical Framework for Efficient Autonomous Target Search with Mobile Manipulators
- 设计针对移动机械臂特点的视角规划策略，提升覆盖效率。
- 融合全局路径与局部优化，实现安全敏捷的视角访问。
- 实测显示搜索时间更短、成功率更高，适合救援场景应用。
在复杂未知环境中利用机器人执行自主目标搜索，可显著提升搜救任务效率。然而，现有方法受限于硬件平台性能、视角选择策略低效及运动规划保守，表现不佳。本文提出HEATS框架，增强移动机械臂在复杂未知环境中的搜寻能力。设计了适配移动机械臂优势的目标视角规划器，确保高效全面的视角布局。在此基础上，整体式运动规划器结合全局路径搜索与局部IPC优化，使机械臂能安全且灵活地访问目标视角，显著提升搜索性能。通过大量仿真与真实世界测试验证，本方法相较经典及前沿方法，实现了更短搜索时间、更高目标搜寻完成率及更低移动成本。相关代码将开源，供社区使用。
原文摘要 · Abstract (English)
Utilizing robots for autonomous target search in complex and unknown environments can greatly improve the efficiency of search and rescue missions. However, existing methods have shown inadequate performance due to hardware platform limitations, inefficient viewpoint selection strategies, and conservative motion planning. In this work, we propose HEATS, which enhances the search capability of mobile manipulators in complex and unknown environments. We design a target viewpoint planner tailored to the strengths of mobile manipulators, ensuring efficient and comprehensive viewpoint planning. Supported by this, a whole-body motion planner integrates global path search with local IPC optimization, enabling the mobile manipulator to safely and agilely visit target viewpoints, significantly improving search performance. We present extensive simulated and real-world tests, in which our method demonstrates reduced search time, higher target search completeness, and lower movement cost compared to classic and state-of-the-art approaches. Our method will be open-sourced for community benefit.
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。