A Hamilton-Jacobi Reachability-Guided Search Framework for Efficient and Safe Indoor Planar Robot Navigation

TL;DR

This paper introduces a hybrid planning framework combining offline Hamilton-Jacobi reachability with online graph search to enable efficient and safe indoor robot navigation in complex, dynamic environments.

Contribution

It presents a novel integration of precomputed HJ value functions with online graph search, improving real-time planning and safety in indoor navigation tasks.

Findings

Outperforms baseline methods in simulation and real-world tests.

Achieves faster planning and safer navigation in dynamic environments.

Effectively incorporates environment knowledge into online planning.

Abstract

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.