Bidirectional Incremental Generalized Hybrid A*

2026-05-28

AI Summary

This work addresses the challenges of planning for high-dimensional dynamical systems in unstructured environments, where the curse of dimensionality and the infeasibility of precomputed motion primitives hinder effective control. To overcome these issues, the paper proposes the Bidirectional Incremental Generalized Hybrid A* (Bi-IGHA*) algorithm, which introduces bidirectional search into the IGHAs* framework for the first time. By integrating multi-resolution state space discretization, bidirectional tree expansion, and a node freezing mechanism, Bi-IGHA* enables efficient trajectory planning under arbitrary time horizons. The approach substantially reduces search depth, mitigates the obscuring of feasible solutions caused by frozen nodes in unidirectional search, and guarantees monotonic cost improvement and termination. Experimental results demonstrate that Bi-IGHA* significantly decreases the number of expanded nodes in R³, R⁴, and R⁶ planning tasks and achieves closed-loop control performance comparable to existing methods in high-speed off-road autonomous driving with lower computational overhead.

Abstract

We focus on the problem of efficient anytime kinodynamic planning for systems with complex dynamics in unstructured environments that make precomputing motion primitives infeasible. Directly applying A* to such problems is computationally infeasible due to the curse of dimensionality. Methods such as Hybrid A* addressed this burden by discretizing the state space, but in turn creating a coupling between tree discovery and the discretization resolution. The Incremental Generalized Hybrid A* (IGHA*) performs search over a hierarchy of resolutions in an anytime fashion to break this coupling, by freezing vertices to use in later search iterations rather than pruning them. However, the frozen vertices can hide solution-supporting vertices from the search at a particular iteration. While classical bidirectional search is motivated by the reduction of search depth, extending IGHA* into the bidirectional setting (termed Bi-IGHA*) obtains additional benefit by fundamentally mitigating the behaviour induced by frozen vertices hiding solutions. We show that Bi-IGHA* preserves IGHA*'s guarantees on monotonic cost improvement and termination. We empirically show that Bi-IGHA* substantially reduces expansions on R3, R4, and R6 planning problems, and achieves equivalent closed-loop performance with kinodynamic planning for high-speed off-road autonomy while requiring significantly fewer expansions. Website: https://personalrobotics.github.io/IGHAStar/biighastar.html

Problem

• kinodynamic planning
• anytime planning
• Hybrid A*
• state space discretization
• bidirectional search

Innovation

• Bidirectional Search
• Incremental Generalized Hybrid A*
• Kinodynamic Planning
• Anytime Algorithm
• State Space Discretization