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Off-road Path Planning Based on Improved Ant Colony Algorithm.

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Τίτλος: Off-road Path Planning Based on Improved Ant Colony Algorithm.
Συγγραφείς: Wang, Han, Zhang, Hongjun, Wang, Kun, Zhang, Chen, Yin, Chengxiang, Kang, Xingdang
Πηγή: Wireless Personal Communications; Sep2018, Vol. 102 Issue 2, p1705-1721, 17p
Θεματικοί όροι: Ant algorithms, Mathematical optimization, Off-road vehicle trails, Robotic path planning, Slopes (Physical geography), Trafficability
Περίληψη: Optimal vehicle off-road path planning problem must consider surface physical properties of terrain and soil. In this paper, we firstly analyse the comprehensive influence of terrain slope and soil strength to vehicle’s off-road trafficability. Given off-road area, the GO or NO-GO tabu table of terrain gird is determined by slope angle and soil remolding cone index (RCI). By applying tabu table and grid weight table, the influence of terrain slope and soil RCI are coordinated to reduce the search scope of algorithm and improve search efficiency. Simulation results based on tracked vehicle M1A1 in off-road environment show that, improved ant colony path planning algorithm not only considers the influence of actual terrain and soil, but also improves computation efficiency. The time cost of optimal routing computation is much lower which is essential for real time off-road path planning scenarios. [ABSTRACT FROM AUTHOR]
Copyright of Wireless Personal Communications is the property of Springer Nature 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.)
Βάση Δεδομένων: Complementary Index
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  – Url: https://dx.doi.org/doi:10.1007/s11277-017-5229-5
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  Data: Off-road Path Planning Based on Improved Ant Colony Algorithm.
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  Data: <searchLink fieldCode="AR" term="%22Wang%2C+Han%22">Wang, Han</searchLink><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Hongjun%22">Zhang, Hongjun</searchLink><br /><searchLink fieldCode="AR" term="%22Wang%2C+Kun%22">Wang, Kun</searchLink><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Chen%22">Zhang, Chen</searchLink><br /><searchLink fieldCode="AR" term="%22Yin%2C+Chengxiang%22">Yin, Chengxiang</searchLink><br /><searchLink fieldCode="AR" term="%22Kang%2C+Xingdang%22">Kang, Xingdang</searchLink>
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  Data: Wireless Personal Communications; Sep2018, Vol. 102 Issue 2, p1705-1721, 17p
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  Data: <searchLink fieldCode="DE" term="%22Ant+algorithms%22">Ant algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Off-road+vehicle+trails%22">Off-road vehicle trails</searchLink><br /><searchLink fieldCode="DE" term="%22Robotic+path+planning%22">Robotic path planning</searchLink><br /><searchLink fieldCode="DE" term="%22Slopes+%28Physical+geography%29%22">Slopes (Physical geography)</searchLink><br /><searchLink fieldCode="DE" term="%22Trafficability%22">Trafficability</searchLink>
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  Label: Abstract
  Group: Ab
  Data: Optimal vehicle off-road path planning problem must consider surface physical properties of terrain and soil. In this paper, we firstly analyse the comprehensive influence of terrain slope and soil strength to vehicle’s off-road trafficability. Given off-road area, the GO or NO-GO tabu table of terrain gird is determined by slope angle and soil remolding cone index (RCI). By applying tabu table and grid weight table, the influence of terrain slope and soil RCI are coordinated to reduce the search scope of algorithm and improve search efficiency. Simulation results based on tracked vehicle M1A1 in off-road environment show that, improved ant colony path planning algorithm not only considers the influence of actual terrain and soil, but also improves computation efficiency. The time cost of optimal routing computation is much lower which is essential for real time off-road path planning scenarios. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Wireless Personal Communications is the property of Springer Nature 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/s11277-017-5229-5
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      – Code: eng
        Text: English
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      – SubjectFull: Ant algorithms
        Type: general
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      – SubjectFull: Off-road vehicle trails
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              Text: Sep2018
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