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An Asymptotically-Optimal Sampling-Based Algorithm for Bi-directional Motion Planning.

Joseph A Starek1, Javier V Gomez2, Edward Schmerling3

  • 1Dept. of Aeronautics and Astronautics, Stanford University, Stanford, CA 94305.

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Summary
This summary is machine-generated.

This study introduces Bi-directional FMT* (BFMT*), a novel algorithm enhancing motion planning by combining bi-directional search with asymptotic optimality. BFMT* improves success and convergence rates for sampling-based planners.

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Area of Science:

  • Robotics and Artificial Intelligence
  • Computational Geometry

Background:

  • Bi-directional search is crucial for improving sampling-based motion planning.
  • Integrating bi-directional search with asymptotic optimality in planners like FMT* remains a challenge.

Purpose of the Study:

  • To present a novel algorithm that merges bi-directional search and asymptotic optimality.
  • To extend the Fast Marching Tree (FMT*) algorithm for enhanced motion planning.

Main Methods:

  • Developed Bi-directional FMT* (BFMT*), a sampling-based, asymptotically-optimal algorithm.
  • Implemented a two-source, lazy dynamic programming recursion to generate dual search trees.
  • Utilized convergence in probability to ensure asymptotic optimality.

Main Results:

  • BFMT* successfully merges bi-directional search with the lazy dynamic programming of FMT*.
  • Demonstrated superior performance of BFMT* compared to unidirectional counterparts and other state-of-the-art planners in numerical experiments.
  • Preserved key properties of FMT*, including lazy search and asymptotic optimality.

Conclusions:

  • BFMT* effectively addresses the gap in combining bi-directional search with asymptotic optimality in motion planning.
  • The proposed algorithm offers significant advantages in success and convergence rates.
  • BFMT* represents a valuable advancement for optimal motion planning algorithms.