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Estimation, Search, and Planning (ESP) Research Group

Informed RRT*: Optimal sampling-based path planning focused via direct sampling of an admissible ellipsoidal heuristic

Authors
  1. Jonathan D. Gammell
  2. Siddhartha S. Srinivasa
  3. Timothy D. Barfoot
Publication Date
Abstract

Rapidly-exploring random trees (RRTs) are popular in motion planning because they find solutions efficiently to single-query problems. Optimal RRTs (RRT*s) extend RRTs to the problem of finding the optimal solution, but in doing so asymptotically find the optimal path from the initial state to every state in the planning domain. This behaviour is not only inefficient but also inconsistent with their single-query nature.

For problems seeking to minimize path length, the subset of states that can improve a solution can be described by a prolate hyperspheroid. We show that unless this subset is sampled directly, the probability of improving a solution becomes arbitrarily small in large worlds or high state dimensions. In this paper, we present an exact method to focus the search by directly sampling this subset.

The advantages of the presented sampling technique are demonstrated with a new algorithm, Informed RRT*. This method retains the same probabilistic guarantees on completeness and optimality as RRT* while improving the convergence rate and final solution quality. We present the algorithm as a simple modification to RRT* that could be further extended by more advanced path-planning algorithms. We show experimentally that it outperforms RRT* in rate of convergence, final solution cost, and ability to find difficult passages while demonstrating less dependence on the state dimension and range of the planning problem.

VideoVideo
PresentationPresentation
Publication Details
Type
Full-Paper-Refereed Conference Paper
Conference
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Location
Chicago, IL, USA
Pages
2997–3004
Digital Object Identifier DOI
10.1109/IROS.2014.6942976
arXiv Identifier arXiv
1404.2334 [cs.RO]
Manuscript
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BibTeX Entry
@inproceedings{gammell_iros14,
author = {Jonathan D Gammell and Siddhartha S Srinivasa and Timothy D Barfoot},
title = {Informed {RRT*}: {Optimal} sampling-based path planning focused via direct sampling of an admissible ellipsoidal heuristic},
booktitle = {Proceedings of the {IEEE/RSJ} International Conference on Intelligent Robots and Systems ({IROS})},
year = {2014},
pages = {2997--3004},
address = {Chicago, IL, USA},
month = {14--18 } # sep,
doi = {10.1109/IROS.2014.6942976},
}