Provably-correct stochastic motion planning with safety constraints

Yoo, C; Fitch, R; Sukkarieh, S

Provably-correct stochastic motion planning with safety constraints

Yoo, C

Fitch, R

Sukkarieh, S

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Publication Type:: Conference Proceeding
Citation:: Proceedings - IEEE International Conference on Robotics and Automation, 2013, pp. 981 - 986
Issue Date:: 2013-11-14

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Field	Value	Language
dc.contributor.author	Yoo, C https://orcid.org/0000-0002-2773-0527	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	2013-11-14	en_US
dc.identifier.citation	Proceedings - IEEE International Conference on Robotics and Automation, 2013, pp. 981 - 986	en_US
dc.identifier.isbn	9781467356411	en_US
dc.identifier.issn	1050-4729	en_US
dc.identifier.uri	http://hdl.handle.net/10453/119869
dc.description.abstract	Formal methods based on the Markov decision process formalism, such as probabilistic computation tree logic (PCTL), can be used to analyse and synthesise control policies that maximise the probability of mission success. In this paper, we consider a different objective. We wish to minimise time-to-completion while satisfying a given probabilistic threshold of success. This important problem naturally arises in motion planning for outdoor robots, where high quality mobility prediction methods are available but stochastic path planning typically relies on an arbitrary weighted cost function that attempts to balance the opposing goals of finding safe paths (minimising risk) while making progress towards the goal (maximising reward). We propose novel algorithms for model checking and policy synthesis in PCTL that 1) provide a quantitative measure of safety and completion time for a given policy, and 2) synthesise policies that minimise completion time with respect to a given safety threshold. We provide simulation results in a stochastic outdoor navigation domain that illustrate policies with varying levels of risk. © 2013 IEEE.	en_US
dc.relation.ispartof	Proceedings - IEEE International Conference on Robotics and Automation	en_US
dc.relation.isbasedon	10.1109/ICRA.2013.6630692	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Provably-correct stochastic motion planning with safety constraints	en_US
dc.type	Conference Proceeding
utslib.for	080101 Adaptive Agents and Intelligent Robotics	en_US
utslib.for	091303 Autonomous Vehicles	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/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CAS - Centre for Autonomous Systems
utslib.copyright.status	closed_access	*
pubs.publication-status	Published	en_US

Abstract:

Formal methods based on the Markov decision process formalism, such as probabilistic computation tree logic (PCTL), can be used to analyse and synthesise control policies that maximise the probability of mission success. In this paper, we consider a different objective. We wish to minimise time-to-completion while satisfying a given probabilistic threshold of success. This important problem naturally arises in motion planning for outdoor robots, where high quality mobility prediction methods are available but stochastic path planning typically relies on an arbitrary weighted cost function that attempts to balance the opposing goals of finding safe paths (minimising risk) while making progress towards the goal (maximising reward). We propose novel algorithms for model checking and policy synthesis in PCTL that 1) provide a quantitative measure of safety and completion time for a given policy, and 2) synthesise policies that minimise completion time with respect to a given safety threshold. We provide simulation results in a stochastic outdoor navigation domain that illustrate policies with varying levels of risk. © 2013 IEEE.

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http://hdl.handle.net/10453/119869