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Distributed Simultaneous Task Allocation and Motion Coordination of Autonomous Vehicles Using a Parallel Computing Cluster.

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Titel:	Distributed Simultaneous Task Allocation and Motion Coordination of Autonomous Vehicles Using a Parallel Computing Cluster.
Autoren:	Tzyh-Jong Tarn, Shan-Ben Chen, Changjiu Zhou, Kulatunga, A. K., Skinner, B. T., Liu, D. K., Nguyen, H. T.
Quelle:	Robotic Welding, Intelligence & Automation; 2007, p409-420, 12p
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. [ABSTRACT FROM AUTHOR]
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Datenbank:	Complementary Index

Beschreibung
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. [ABSTRACT FROM AUTHOR]
ISBN:	9783540733737
DOI:	10.1007/978-3-540-73374-4_49