Learned Stochastic Mobility Prediction for Planning with Control Uncertainty on Unstructured Terrain

Peynot, T; Lui, ST; McAllister, R; Fitch, R; Sukkarieh, S

Learned Stochastic Mobility Prediction for Planning with Control Uncertainty on Unstructured Terrain

Peynot, T Lui, ST McAllister, R Fitch, R

Sukkarieh, S

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Publication Type:: Journal Article
Citation:: Journal of Field Robotics, 2014, 31 (6), pp. 969 - 995
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Peynot, T	en_US
dc.contributor.author	Lui, ST	en_US
dc.contributor.author	McAllister, R	en_US
dc.contributor.author	Fitch, R https://orcid.org/0000-0003-2215-5188	en_US
dc.contributor.author	Sukkarieh, S	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	Journal of Field Robotics, 2014, 31 (6), pp. 969 - 995	en_US
dc.identifier.issn	1556-4959	en_US
dc.identifier.uri	http://hdl.handle.net/10453/118742
dc.description.abstract	© 2014 Wiley Periodicals, Inc. Motion planning for planetary rovers must consider control uncertainty in order to maintain the safety of the platform during navigation. Modeling 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 it 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 onboard depth sensor. We consider both rigid terrain, consisting of uneven ground, small rocks, and nontraversable rocks, and also deformable terrain. We introduce two methods for training the mobility prediction model from either proprioceptive or exteroceptive observations, and we 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.	en_US
dc.relation.ispartof	Journal of Field Robotics	en_US
dc.relation.isbasedon	10.1002/rob.21536	en_US
dc.subject.classification	Industrial Engineering & Automation	en_US
dc.title	Learned Stochastic Mobility Prediction for Planning with Control Uncertainty on Unstructured Terrain	en_US
dc.type	Journal Article
utslib.citation.volume	6	en_US
utslib.citation.volume	31	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	0913 Mechanical Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAS - Centre for Autonomous Systems
utslib.copyright.status	closed_access
pubs.issue	6	en_US
pubs.publication-status	Published	en_US
pubs.volume	31	en_US

Abstract:

© 2014 Wiley Periodicals, Inc. Motion planning for planetary rovers must consider control uncertainty in order to maintain the safety of the platform during navigation. Modeling 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 it 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 onboard depth sensor. We consider both rigid terrain, consisting of uneven ground, small rocks, and nontraversable rocks, and also deformable terrain. We introduce two methods for training the mobility prediction model from either proprioceptive or exteroceptive observations, and we 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.

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http://hdl.handle.net/10453/118742