Generalizing Informed Sampling for Asymptotically-Optimal Sampling-Based Kinodynamic Planning via Markov Chain Monte Carlo

Daqing Yi, Rohan Thakker, Cole Gulino, Oren Salzman, Siddhartha Srinivasa

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Asymptotically-optimal motion planners such as RRT∗ have been shown to incrementally approximate the shortest path between start and goal states. Once an initial solution is found, their performance can be dramatically improved by restricting subsequent samples to regions of the state space that can potentially improve the current solution. When the motion-planning problem lies in a Euclidean space, this region Xinf, called the informed set, can be sampled directly. However, when planning with differential constraints in non-Euclidean state spaces, no analytic solutions exists to sampling Xinf directly. State-of-the-art approaches to sampling Xinf in such domains such as Hierarchical Rejection Sampling (HRS) may still be slow in high -dimensional state space. This may cause the planning algorithm to spend most of its time trying to produces samples in Xinf rather than explore it. In this paper, we suggest an alternative approach to produce samples in the informed set Xinf for a wide range of settings. Our main insight is to recast this problem as one of sampling uniformly within the sub-level-set of an implicit non-convex function. This recasting enables us to apply Monte Carlo sampling methods, used very effectively in the Machine Learning and Optimization communities, to solve our problem. We show for a wide range of scenarios that using our sampler can accelerate the convergence rate to high-quality solutions in high-dimensional problems.

Original languageEnglish
Title of host publication2018 IEEE International Conference on Robotics and Automation, ICRA 2018
Pages7063-7070
Number of pages8
ISBN (Electronic)9781538630815
DOIs
StatePublished - 10 Sep 2018
Externally publishedYes
Event2018 IEEE International Conference on Robotics and Automation, ICRA 2018 - Brisbane, Australia
Duration: 21 May 201825 May 2018

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation

Conference

Conference2018 IEEE International Conference on Robotics and Automation, ICRA 2018
Country/TerritoryAustralia
CityBrisbane
Period21/05/1825/05/18

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering

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