AIT* and EIT*: Asymmetric bidirectional sampling-based path planning

TL;DR

This paper introduces AIT* and EIT*, two asymptotically optimal bidirectional sampling-based path planning algorithms that improve efficiency by continuously exchanging problem-specific heuristics during search.

Contribution

The paper presents novel asymmetric bidirectional algorithms, AIT* and EIT*, which adaptively and effectively utilize heuristics for improved path planning performance.

Findings

Outperform other algorithms on obstacle clearance problems.

Maintain strong performance on path length minimization tasks.

Effectively exploit problem-specific heuristics in diverse domains.

Abstract

Optimal path planning is the problem of finding a valid sequence of states between a start and goal that optimizes an objective. Informed path planning algorithms order their search with problem-specific knowledge expressed as heuristics and can be orders of magnitude more efficient than uninformed algorithms. Heuristics are most effective when they are both accurate and computationally inexpensive to evaluate, but these are often conflicting characteristics. This makes the selection of appropriate heuristics difficult for many problems. This paper presents two almost-surely asymptotically optimal sampling-based path planning algorithms to address this challenge, Adaptively Informed Trees (AIT*) and Effort Informed Trees (EIT*). These algorithms use an asymmetric bidirectional search in which both searches continuously inform each other. This allows AIT* and EIT* to improve planning performance by simultaneously calculating and exploiting increasingly accurate, problem-specific heuristics. The benefits of AIT* and EIT* relative to other sampling-based algorithms are demonstrated on twelve problems in abstract, robotic, and biomedical domains optimizing path length and obstacle clearance. The experiments show that AIT* and EIT* outperform other algorithms on problems optimizing obstacle clearance, where a priori cost heuristics are often ineffective, and still perform well on problems minimizing path length, where such heuristics are often effective.