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

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Title: Distributed Simultaneous Task Allocation and Motion Coordination of Autonomous Vehicles Using a Parallel Computing Cluster.
Authors: Tzyh-Jong Tarn, Shan-Ben Chen, Changjiu Zhou, Kulatunga, A. K., Skinner, B. T., Liu, D. K., Nguyen, H. T.
Source: 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]
Copyright of Robotic Welding, Intelligence & Automation is the property of Springer eBooks and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="AR" term="%22Tzyh-Jong+Tarn%22">Tzyh-Jong Tarn</searchLink><br /><searchLink fieldCode="AR" term="%22Shan-Ben+Chen%22">Shan-Ben Chen</searchLink><br /><searchLink fieldCode="AR" term="%22Changjiu+Zhou%22">Changjiu Zhou</searchLink><br /><searchLink fieldCode="AR" term="%22Kulatunga%2C+A%2E+K%2E%22">Kulatunga, A. K.</searchLink><br /><searchLink fieldCode="AR" term="%22Skinner%2C+B%2E+T%2E%22">Skinner, B. T.</searchLink><br /><searchLink fieldCode="AR" term="%22Liu%2C+D%2E+K%2E%22">Liu, D. K.</searchLink><br /><searchLink fieldCode="AR" term="%22Nguyen%2C+H%2E+T%2E%22">Nguyen, H. T.</searchLink>
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  Data: Robotic Welding, Intelligence & Automation; 2007, p409-420, 12p
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  Data: 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]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Robotic Welding, Intelligence & Automation is the property of Springer eBooks and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/978-3-540-73374-4_49
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            NameFull: Shan-Ben Chen
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              Text: 2007
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