Learned stochastic mobility prediction for planning with control uncertainty on unstructured terrain

Peynot, Thierry, Lui, Sin-Ting, McAllister, Rowan, Fitch, Robert, & Sukkarieh, Salah (2014) Learned stochastic mobility prediction for planning with control uncertainty on unstructured terrain. Journal of Field Robotics, 31(6), pp. 969-995.

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Abstract

Motion planning for planetary rovers must consider control uncertainty in order to maintain the safety of the platform during navigation. Modelling such control uncertainty is difficult due to the complex interaction between the platform and its environment. In this paper, we propose a motion planning approach whereby the outcome of control actions is learned from experience and represented statistically using a Gaussian process regression model. This mobility prediction model is trained using sample executions of motion primitives on representative terrain, and predicts the future outcome of control actions on similar terrain. Using Gaussian process regression allows us to exploit its inherent measure of prediction uncertainty in planning. We integrate mobility prediction into a Markov decision process framework and use dynamic programming to construct a control policy for navigation to a goal region in a terrain map built using an on-board depth sensor. We consider both rigid terrain, consisting of uneven ground, small rocks, and non-traversable rocks, and also deformable terrain. We introduce two methods for training the mobility prediction model from either proprioceptive or exteroceptive observations, and report results from nearly 300 experimental trials using a planetary rover platform in a Mars-analogue environment. Our results validate the approach and demonstrate the value of planning under uncertainty for safe and reliable navigation.

Impact and interest:

3 citations in Scopus
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ID Code: 76269
Item Type: Journal Article
Refereed: Yes
Keywords: mobile robotics, planetary rover, mobility prediction, learning, motion planning
DOI: 10.1002/rob.21536
ISSN: 1556-4967
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100)
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Funding:
  • DIISR AUSTRALIAN GOV/Australian Space Research Program
  • AFRL/FA2386-10-1-4153
Copyright Owner: Copyright 2014 Wiley Periodicals, Inc.
Copyright Statement: This is the accepted version of the following article: [full citation], which has been published in final form at [Link to final article]
Deposited On: 22 Sep 2014 00:10
Last Modified: 04 Jan 2016 11:00

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