Incremental Generalized Hybrid A*

2025-08-18

AI Summary

In complex dynamical environments, large-scale tree search suffers from an inherent trade-off in grid resolution—coarse grids lead to planning failures, while fine grids severely degrade computational efficiency. Method: This paper proposes an incremental tree search framework that eliminates the need for fixed-grid pruning. Its core innovations include a dynamic vertex expansion mechanism and approximate dominance checking, integrated with motion primitive modeling, incremental expansion strategies, and an anytime algorithmic framework. Model predictive control (MPC) is further incorporated to enable closed-loop trajectory optimization. Contribution/Results: The method guarantees solution optimality at any intermediate stage, significantly enhancing both robustness and real-time performance. Simulation and real-vehicle experiments demonstrate that, compared to an optimized Hybrid A*, the approach reduces node expansions by 6× while substantially improving planning speed and stability. It is particularly suitable for high-frequency applications such as autonomous driving and off-road navigation.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Planning, Routing, and Scheduling: Mixed Discrete/Continuous Planning
• Multiagent Systems: Multiagent Planning

Application Category

• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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*. 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. Code: https://github.com/personalrobotics/IGHAStar

Problem

• Efficiently organizing search over very large trees for real-time planning
• Mitigating dimensionality curse in kinodynamic planning with complex dynamics
• Balancing grid resolution to avoid excessive expansions or planning failures

Innovation

• Dynamic vertex expansions without rigid pruning
• Anytime tree-search framework for complex dynamics
• Grid-free dominance checks for efficient planning