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Cross-entropy motion planning

Kobilarov, Marin (2012) Cross-entropy motion planning. International Journal of Robotics Research, 31 (7). pp. 855-871. ISSN 0278-3649. doi:10.1177/0278364912444543.

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This paper is concerned with motion planning for non-linear robotic systems operating in constrained environments. A method for computing high-quality trajectories is proposed building upon recent developments in sampling-based motion planning and stochastic optimization. The idea is to equip sampling-based methods with a probabilistic model that serves as a sampling distribution and to incrementally update the model during planning using data collected by the algorithm. At the core of the approach lies the cross-entropy method for the estimation of rare-event probabilities. The cross-entropy method is combined with recent optimal motion planning methods such as the rapidly exploring random trees (RRT*) in order to handle complex environments. The main goal is to provide a framework for consistent adaptive sampling that correlates the spatial structure of trajectories and their computed costs in order to improve the performance of existing planning methods.

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Additional Information:© 2012 The Author(s). Published online May 30, 2012; OnlineFirst Version May 11, 2012. The author thanks the reviewers for the useful directions for improving the paper. The author was partially supported by the Keck Institute for Space Studies, Caltech.
Group:Keck Institute for Space Studies
Funding AgencyGrant Number
Keck Institute for Space Studies (KISS)UNSPECIFIED
Subject Keywords:motion planning; nonlinear control; importance sampling; cross-entropy; stochastic optimization; RRT
Issue or Number:7
Record Number:CaltechAUTHORS:20120711-115634623
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Official Citation:Marin Kobilarov Cross-entropy motion planning The International Journal of Robotics Research June 2012 31: 855-871, first published on May 11, 2012 doi:10.1177/0278364912444543
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:32353
Deposited On:11 Jul 2012 20:27
Last Modified:09 Nov 2021 21:26

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