用哈密顿-雅可比方法提升机器人室内导航的效率与安全性
A Hamilton-Jacobi Reachability-Guided Search Framework for Efficient and Safe Indoor Planar Robot Navigation
- 结合预计算的哈密顿-雅可比值函数作为启发式和安全约束
- 在动态环境中实现更快的实时规划，且导航更安全
- 适合需要高安全性和实时响应的室内机器人应用
自主导航需在复杂甚至动态环境中高效且安全地到达目标。基于图搜索的算法因具备通用性及在使用可接受启发式时的理论保障而被广泛采用。然而，图搜索的计算复杂度随搜索空间维度增加而急剧上升，常导致动态环境中实时规划不可行。本文将离线计算的哈密顿-雅可比（HJ）可达性分析与在线图搜索相结合，发挥两者优势。预先计算的HJ值函数作为信息性启发式和主动安全约束，分摊了在线搜索的计算负担。同时，图搜索使基于可达性的推理可融入在线规划，克服了传统HJ可达性分析需环境全知这一长期难题。大量仿真与真实世界实验表明，该方法在有无人类存在的环境中，均显著优于基线方法，在规划效率和导航安全性方面表现更优。
原文摘要 · Abstract (English)
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.
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