Enhanced stochastic mobility prediction with multi-output Gaussian processes

Lui, Sin Ting, Peynot, Thierry, Fitch, Robert, & Sukkarieh, Salah (2016) Enhanced stochastic mobility prediction with multi-output Gaussian processes. In Intelligent Autonomous Systems 13: Proceedings of the 13th International Conference IAS-13 [Advances in Intelligent Systems and Computing, Volume 302], Springer, Padua, Italy, pp. 173-190.

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Abstract

Outdoor robots such as planetary rovers must be able to navigate safely and reliably in order to successfully perform missions in remote or hostile environments. Mobility prediction is critical to achieving this goal due to the inherent control uncertainty faced by robots traversing natural terrain. We propose a novel algorithm for stochastic mobility prediction based on multi-output Gaussian process regression. Our algorithm considers the correlation between heading and distance uncertainty and provides a predictive model that can easily be exploited by motion planning algorithms. We evaluate our method experimentally and report results from over 30 trials in a Mars-analogue environment that demonstrate the effectiveness of our method and illustrate the importance of mobility prediction in navigating challenging terrain.

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ID Code: 74587
Item Type: Conference Paper
Refereed: Yes
Additional URLs:
Keywords: mobile robotics, planetary rover, mobility prediction, learning, stochastic motion planning
DOI: 10.1007/978-3-319-08338-4_14
ISBN: 978-3-319-08337-7
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
Copyright Owner: Copyright 2014 Please consult the authors
Deposited On: 31 Jul 2014 22:48
Last Modified: 08 Jun 2016 01:14

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