Distributed simultaneous task allocation and motion coordination of autonomous vehicles using a parallel computing cluster

Kulatunga, AK; Skinner, BT; Liu, DK; Nguyen, HT

Distributed simultaneous task allocation and motion coordination of autonomous vehicles using a parallel computing cluster

Kulatunga, AK Skinner, BT

Liu, DK

Nguyen, HT

Permalink

Publication Type:: Chapter
Citation:: 2007, 362 pp. 409 - 420
Issue Date:: 2007-09-24

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download full textAdobe PDF (204.49 kB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Kulatunga, AK	en_US
dc.contributor.author	Skinner, BT https://orcid.org/0000-0002-7869-4458	en_US
dc.contributor.author	Liu, DK https://orcid.org/0000-0002-1581-5582	en_US
dc.contributor.author	Nguyen, HT https://orcid.org/0000-0003-3373-8178	en_US
dc.date.issued	2007-09-24	en_US
dc.identifier.citation	2007, 362 pp. 409 - 420	en_US
dc.identifier.isbn	3540733736	en_US
dc.identifier.isbn	9783540733737	en_US
dc.identifier.uri	http://hdl.handle.net/10453/12741
dc.description.abstract	Task allocation and motion coordination are the main factors that should be consi-dered in the coordination of multiple autonomous vehicles in material handling systems. Presently, these factors are handled in different stages, leading to a reduction in optimality and efficiency of the overall coordination. However, if these issues are solved simultaneously we can gain near optimal results. But, the simultaneous approach contains additional algorithmic complexities which increase computation time in the simulation environment. This work aims to reduce the computation time by adopting a parallel and distributed computation strategy for Simultaneous Task Allocation and Motion Coordination (STAMC). In the simulation experiments, each cluster node executes the motion coordination algorithm for each autonomous vehicle. This arrangement enables parallel computation of the expensive STAMC algorithm. Parallel and distributed computation is performed directly within the interpretive MATLAB environment. Results show the parallel and distributed approach provides sub-linear speedup compared to a single centralised computing node. © 2007 Springer-Verlag Berlin Heidelberg.	en_US
dc.relation.isbasedon	10.1007/978-3-540-73374-4_49	en_US
dc.subject.classification	Industrial Engineering & Automation	en_US
dc.title	Distributed simultaneous task allocation and motion coordination of autonomous vehicles using a parallel computing cluster	en_US
dc.type	Chapter
utslib.citation.volume	362	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	0103 Numerical and Computational Mathematics	en_US
utslib.for	0102 Applied Mathematics	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
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US
pubs.volume	362	en_US

Abstract:

Task allocation and motion coordination are the main factors that should be consi-dered in the coordination of multiple autonomous vehicles in material handling systems. Presently, these factors are handled in different stages, leading to a reduction in optimality and efficiency of the overall coordination. However, if these issues are solved simultaneously we can gain near optimal results. But, the simultaneous approach contains additional algorithmic complexities which increase computation time in the simulation environment. This work aims to reduce the computation time by adopting a parallel and distributed computation strategy for Simultaneous Task Allocation and Motion Coordination (STAMC). In the simulation experiments, each cluster node executes the motion coordination algorithm for each autonomous vehicle. This arrangement enables parallel computation of the expensive STAMC algorithm. Parallel and distributed computation is performed directly within the interpretive MATLAB environment. Results show the parallel and distributed approach provides sub-linear speedup compared to a single centralised computing node. © 2007 Springer-Verlag Berlin Heidelberg.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/12741