Incremental Generalized Hybrid A*

TL;DR

This paper introduces IGHA*, an incremental hybrid A* algorithm that improves real-time kinodynamic planning by reducing expansions and dynamically organizing search without rigid grid-based pruning.

Contribution

IGHA* offers a novel anytime tree-search framework that outperforms traditional Hybrid A* by adaptively organizing search expansions, enabling faster and more robust planning for complex dynamics.

Findings

IGHA* uses 6x fewer expansions than HA* in planning tasks.

IGHA* achieves real-time performance in simulation and on a small vehicle.

IGHA* outperforms HA* in high-fidelity off-road experiments.

Abstract

We address the problem of efficiently organizing search over very large trees, which arises in many applications ranging from autonomous driving to aerial vehicles. Here, we are motivated by off-road autonomy, where real-time planning is essential. Classical approaches use graphs of motion primitives and exploit dominance to mitigate the curse of dimensionality and prune expansions efficiently. However, for complex dynamics, repeatedly solving two-point boundary-value problems makes graph construction too slow for fast kinodynamic planning. Hybrid A* (HA*) addressed this challenge by searching over a tree of motion primitives and introducing approximate pruning using a grid-based dominance check. However, choosing the grid resolution is difficult: too coarse risks failure, while too fine leads to excessive expansions and slow planning. We propose Incremental Generalized Hybrid A* (IGHA*), an anytime tree-search framework that dynamically organizes vertex expansions without rigid pruning. IGHA* provably matches or outperforms HA*. For both on-road kinematic and off-road kinodynamic planning queries for a car-like robot, variants of IGHA* use 6x fewer expansions to the best solution compared to an optimized version of HA* (HA*M, an internal baseline). In simulated off-road experiments in a high-fidelity simulator, IGHA* outperforms HA*M when both are used in the loop with a model predictive controller. We demonstrate real-time performance both in simulation and on a small-scale off-road vehicle, enabling fast, robust planning under complex dynamics. Website: https://personalrobotics.github.io/IGHAStar/