一种基于哈密顿-雅可比可达性的搜索框架用于高效安全的室内平面机器人导航
A Hamilton-Jacobi Reachability-Guided Search Framework for Efficient and Safe Indoor Planar Robot Navigation
AI总结：
本文提出结合离线哈密顿-雅可比可达性与在线图搜索的方法，提升室内机器人导航的效率和安全性，通过预计算的HJ值函数作为启发式和安全约束，减少计算开销。
摘要：
自主导航需要在复杂且可能动态的环境中安全高效地规划路径。基于图搜索的算法因其通用性和理论保证而被广泛采用，但随着搜索空间维度增加，计算复杂性迅速上升，使动态环境中的实时规划变得不可行。本文结合离线哈密顿-雅可比（HJ）可达性与在线图搜索，利用两者互补的优势。预计算的HJ值函数用作信息启发式和主动安全约束，从而减少在线图搜索的计算成本。同时，图搜索使可达性推理能够融入在线规划，克服了HJ可达性需要完整环境知识的长期挑战。大量仿真研究和实际实验表明，所提出的方法在有和无人类存在环境中，均在规划效率和导航安全性方面优于基线方法。
英文摘要：
Autonomous navigation requires planning to reach a goal safely and efficiently in complex and potentially dynamic environments. Graph search-based algorithms are widely adopted due to their generality and theoretical guarantees when equipped with admissible heuristics. However, the computational complexity of graph search grows rapidly with the dimensionality of the search space, often making real-time planning in dynamic environments intractable. In this paper, we combine offline Hamilton-Jacobi (HJ) reachability with online graph search to leverage the complementary strengths of both. Precomputed HJ value functions, used as informative heuristics and proactive safety constraints, amortize online computation of the graph search process. At the same time, graph search enables reachability-based reasoning to be incorporated into online planning, overcoming the long-standing challenge of HJ reachability requiring full knowledge of the environment. Extensive simulation studies and real-world experiments demonstrate that the proposed approach consistently outperforms baseline methods in terms of planning efficiency and navigation safety, in environments with and without human presence.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
