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An Empirical Study of Optimal Motion Planning

Authors: Jingru Luo, Kris K. Hauser

Published: 2014 (Conference Paper)

Source: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

DOI: 10.1109/IROS.2014.6942793

Summary

Benchmarks several categories of optimal motion planners (sampling-based, grid-based, and trajectory optimization) on synthetic problems varying in dimensionality, number of homotopy classes, and passage geometry, providing empirical guidance on planner selection.

Abstract

This paper presents a systematic benchmarking comparison between optimal motion planners. Six planners representing the categories of sampling-based, grid-based, and trajectory optimization methods are compared on synthetic problems of varying dimensionality, number of homotopy classes, and width and length of narrow passages. Performance statistics are gathered on success and convergence rates, and performance variations with respect to geometric characteristics are analyzed. Based on this analysis, we recommend planners that are likely to perform well for certain problem classes, and make recommendations for future planning research.

Primary

Paper motion.cs.illinois.edu

Standard

DOI 10.1109/IROS.2014.6942793

Alternate

ieeexplore.ieee.org ieeexplore.ieee.org

Tags

  • Survey

  • Benchmarking

  • Motion planning

  • Robot motion planning

  • Sampling-based planning

  • Optimal motion planning