BiAIT*: Symmetrical Bidirectional Optimal Path Planning with Adaptive Heuristic

TL;DR

BiAIT* introduces a symmetrical bidirectional search algorithm that enhances path planning efficiency by reducing heuristic computation and accelerating initial solution finding, outperforming existing methods.

Contribution

It extends AIT* with symmetrical bidirectional search, improving speed and heuristic updates in path planning.

Findings

BiAIT* finds solutions faster than state-of-the-art methods.

Symmetrical bidirectional search improves heuristic efficiency.

BiAIT* reduces computation during collision updates.

Abstract

Adaptively Informed Trees (AIT*) is an algorithm that uses the problem-specific heuristic to avoid unnecessary searches, which significantly improves its performance, especially when collision checking is expensive. However, the heuristic estimation in AIT* consumes lots of computational resources, and its asymmetric bidirectional searching strategy cannot fully exploit the potential of the bidirectional method. In this article, we propose an extension of AIT* called BiAIT*. Unlike AIT*, BiAIT* uses symmetrical bidirectional search for both the heuristic and space searching. The proposed method allows BiAIT* to find the initial solution faster than AIT*, and update the heuristic with less computation when a collision occurs. We evaluated the performance of BiAIT* through simulations and experiments, and the results show that BiAIT* can find the solution faster than state-of-the-art methods. We also analyze the reasons for the different performances between BiAIT* and AIT*. Furthermore, we discuss two simple but effective modifications to fully exploit the potential of the adaptively heuristic method.