Motion planning and stochastic control with experimental validation on a planetary rover

McAllister, Rowan, Peynot, Thierry, Fitch, Robert, & Sukkarieh, Salah (2012) Motion planning and stochastic control with experimental validation on a planetary rover. In 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, Hotel Tivoli Marina Vilamoura, Algarve, Portugal, pp. 4716-4723.

View at publisher


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 model is used to construct a control policy for navigation to a goal region in a terrain map built using an on-board RGB-D camera. The terrain includes flat ground, small rocks, and non-traversable rocks. We report the results of 200 simulated and 35 experimental trials that validate the approach and demonstrate the value of considering control uncertainty in maintaining platform safety.

Impact and interest:

1 citations in Scopus
Search Google Scholar™

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

Full-text downloads:

44 since deposited on 06 Mar 2014
10 in the past twelve months

Full-text downloads displays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.

ID Code: 67657
Item Type: Conference Paper
Refereed: Yes
Additional Information: 13195564
motion planning
stochastic control
experimental validation
planetary rover
control uncertainty
platform safety maintenance
complex interaction
control action learning
statistical representation
Gaussian process regression model
control policy
goal region
terrain map
on-board RGB-D camera
flat ground
small rocks
nontraversable rocks
Keywords: Gaussian processes, learning systems, mobile robots, path planning, planetary rovers, stochastic systems, uncertain systems
DOI: 10.1109/IROS.2012.6386229
ISBN: 9781467317375
ISSN: 2153-0858
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 2012 IEEE
Copyright Statement: Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
Deposited On: 06 Mar 2014 00:30
Last Modified: 05 Apr 2014 20:45

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page