An Asymptotically-Optimal Sampling-Based Algorithm for Bi-directional Motion Planning
Abstract
Bi-directional search is a widely used strategy to increase the success and convergence rates of sampling-based motion planning algorithms. Yet, few results are available that merge both bi-directional search and asymptotic optimality into existing optimal planners, such as PRM*, RRT*, and FMT*. The objective of this paper is to fill this gap. Specifically, this paper presents a bi-directional, sampling-based, asymptotically-optimal algorithm named Bi-directional FMT* (BFMT*) that extends the Fast Marching Tree (FMT*) algorithm to bi-directional search while preserving its key properties, chiefly lazy search and asymptotic optimality through convergence in probability. BFMT* performs a two-source, lazy dynamic programming recursion over a set of randomly-drawn samples, correspondingly generating two search trees: one in cost-to-come space from the initial configuration and another in cost-to-go space from the goal configuration. Numerical experiments illustrate the advantages of BFMT* over its unidirectional counterpart, as well as a number of other state-of-the-art planners.
Keywords
Cite
@article{arxiv.1507.07602,
title = {An Asymptotically-Optimal Sampling-Based Algorithm for Bi-directional Motion Planning},
author = {Joseph A. Starek and Javier V. Gomez and Edward Schmerling and Lucas Janson and Luis Moreno and Marco Pavone},
journal= {arXiv preprint arXiv:1507.07602},
year = {2016}
}
Comments
Accepted to the 2015 IEEE Intelligent Robotics and Systems Conference in Hamburg, Germany. This submission represents the long version of the conference manuscript, with additional proof details (Section IV) regarding the asymptotic optimality of the BFMT* algorithm