Simultaneous planning and scheduling for multi-autonomous vehicles

Liu, DK; Kulatunga, AK

Simultaneous planning and scheduling for multi-autonomous vehicles

Liu, DK

Kulatunga, AK

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Publication Type:: Chapter
Citation:: 2007, 49 pp. 437 - 464
Issue Date:: 2007-05-09

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Field	Value	Language
dc.contributor.author	Liu, DK https://orcid.org/0000-0002-1581-5582	en_US
dc.contributor.author	Kulatunga, AK	en_US
dc.date.issued	2007-05-09	en_US
dc.identifier.citation	2007, 49 pp. 437 - 464	en_US
dc.identifier.isbn	3540485821	en_US
dc.identifier.isbn	9783540485827	en_US
dc.identifier.uri	http://hdl.handle.net/10453/7881
dc.description.abstract	With the increasing applications of autonomous vehicles in dynamic and strictly constrained environments such as automated container terminals, efficient task/resource allocation and motion coordination (i.e., path and speed planning) of multi-autonomous vehicles has become the critical problem and have therefore been recently recognized as the key research issues by both academics and industry. This chapter addresses a generic approach for integration of task allocation, path planning and collision avoidance, which has so far not attracted much attention in the academic literature. A Simultaneous Task Allocation and Motion Coordination (STAMC) approach is presented. Two metaheuristic algorithms, i.e. simulated annealing and ant colony, and an auction algorithm are investigated and applied. The proposed approach is able to solve the scheduling, planning and collision avoidance problems simultaneously; improve the usage of bottleneck areas; handle dynamic traffic conditions and avoid deadlock. Simulation results demonstrated the effectiveness and efficiency of this approach. © Springer-Verlag Berlin Heidelberg 2007.	en_US
dc.relation.isbasedon	10.1007/978-3-540-48584-1_16	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Simultaneous planning and scheduling for multi-autonomous vehicles	en_US
dc.type	Chapter
utslib.citation.volume	49	en_US
utslib.for	091303 Autonomous Vehicles	en_US
utslib.for	091007 Manufacturing Robotics and Mechatronics (excl. Automotive Mechatronics)	en_US
utslib.for	090602 Control Systems, Robotics and Automation	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
pubs.volume	49	en_US

Abstract:

With the increasing applications of autonomous vehicles in dynamic and strictly constrained environments such as automated container terminals, efficient task/resource allocation and motion coordination (i.e., path and speed planning) of multi-autonomous vehicles has become the critical problem and have therefore been recently recognized as the key research issues by both academics and industry. This chapter addresses a generic approach for integration of task allocation, path planning and collision avoidance, which has so far not attracted much attention in the academic literature. A Simultaneous Task Allocation and Motion Coordination (STAMC) approach is presented. Two metaheuristic algorithms, i.e. simulated annealing and ant colony, and an auction algorithm are investigated and applied. The proposed approach is able to solve the scheduling, planning and collision avoidance problems simultaneously; improve the usage of bottleneck areas; handle dynamic traffic conditions and avoid deadlock. Simulation results demonstrated the effectiveness and efficiency of this approach. © Springer-Verlag Berlin Heidelberg 2007.

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http://hdl.handle.net/10453/7881