Asymptotically Optimal Sampling-Based Path Planning Using Bidirectional Guidance Heuristic

TL;DR

This paper presents BIGIT*, a novel bidirectional heuristic sampling-based motion planning algorithm that improves efficiency and optimality in high-dimensional spaces, demonstrated through simulations and real drone path planning.

Contribution

Introduces BIGIT*, combining meet-in-the-middle bidirectional search with a lazy strategy and uniform-cost search for asymptotic optimality in motion planning.

Findings

Outperforms existing planners in speed and convergence to optimal solutions

Effective in high-dimensional spaces up to 16 dimensions

Validated on simulated problems and real drone flight paths

Abstract

This paper introduces Bidirectional Guidance Informed Trees (BIGIT*),~a new asymptotically optimal sampling-based motion planning algorithm. Capitalizing on the strengths of \emph{meet-in-the-middle} property in bidirectional heuristic search with a new lazy strategy, and uniform-cost search, BIGIT* constructs an implicitly bidirectional preliminary motion tree on an implicit random geometric graph (RGG). This efficiently tightens the informed search region, serving as an admissible and accurate bidirectional guidance heuristic. This heuristic is subsequently utilized to guide a bidirectional heuristic search in finding a valid path on the given RGG. Experiments show that BIGIT* outperforms the existing informed sampling-based motion planners both in faster finding an initial solution and converging to the optimum on simulated abstract problems in $\mathbb{R}^{16}$. Practical drone flight path planning tasks across a campus also verify our results.