Modeling and stochastic optimization of complete coverage under uncertainties in multi-robot base placements

Hassan, M; Liu, D; Paul, G

Modeling and stochastic optimization of complete coverage under uncertainties in multi-robot base placements

Hassan, M

Liu, D

Paul, G

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Publication Type:: Conference Proceeding
Citation:: IEEE International Conference on Intelligent Robots and Systems, 2016, 2016-November pp. 2978 - 2984
Issue Date:: 2016-11-28

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Field	Value	Language
dc.contributor.author	Hassan, M https://orcid.org/0000-0002-0824-3272	en_US
dc.contributor.author	Liu, D https://orcid.org/0000-0002-1581-5582	en_US
dc.contributor.author	Paul, G https://orcid.org/0000-0002-3478-0020	en_US
dc.date.available	2020-05-25T19:08:01Z
dc.date.issued	2016-11-28	en_US
dc.identifier.citation	IEEE International Conference on Intelligent Robots and Systems, 2016, 2016-November pp. 2978 - 2984	en_US
dc.identifier.isbn	9781509037629	en_US
dc.identifier.issn	2153-0858	en_US
dc.identifier.uri	http://hdl.handle.net/10453/56335
dc.description.abstract	© 2016 IEEE. Uncertainties in base placements of mobile, autonomous industrial robots can cause incomplete coverage in tasks such as grit-blasting and spray painting. Sensing and localization errors can cause such uncertainties in robot base placements. This paper addresses the problem of collaborative complete coverage under uncertainties through appropriate base placements of multiple mobile and autonomous industrial robots while aiming to optimize the performance of the robot team. A mathematical model for complete coverage under uncertainties is proposed and then solved using a stochastic multi-objective optimization algorithm. The approach aims to concurrently find an optimal number and sequence of base placements for each robot such that the robot team's objectives are optimized whilst uncertainties are accounted for. Several case studies based on a real-world application using a realworld object and a complex simulated object are provided to demonstrate the effectiveness of the approach for different conditions and scenarios, e.g. various levels of uncertainties, different numbers of robots, and robots with different capabilities.	en_US
dc.relation	http://purl.org/au-research/grants/arc/LP150100935
dc.relation.ispartof	IEEE International Conference on Intelligent Robots and Systems	en_US
dc.relation.isbasedon	10.1109/IROS.2016.7759461	en_US
dc.title	Modeling and stochastic optimization of complete coverage under uncertainties in multi-robot base placements	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2016-November	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/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	open_access
pubs.publication-status	Published	en_US
pubs.volume	2016-November	en_US

Abstract:

© 2016 IEEE. Uncertainties in base placements of mobile, autonomous industrial robots can cause incomplete coverage in tasks such as grit-blasting and spray painting. Sensing and localization errors can cause such uncertainties in robot base placements. This paper addresses the problem of collaborative complete coverage under uncertainties through appropriate base placements of multiple mobile and autonomous industrial robots while aiming to optimize the performance of the robot team. A mathematical model for complete coverage under uncertainties is proposed and then solved using a stochastic multi-objective optimization algorithm. The approach aims to concurrently find an optimal number and sequence of base placements for each robot such that the robot team's objectives are optimized whilst uncertainties are accounted for. Several case studies based on a real-world application using a realworld object and a complex simulated object are provided to demonstrate the effectiveness of the approach for different conditions and scenarios, e.g. various levels of uncertainties, different numbers of robots, and robots with different capabilities.

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http://hdl.handle.net/10453/56335