Hierarchical detecting points path planning algorithm for climbing robot in spatial trusses

To solve climbing robot path planning in spatial trusses with detecting points, a hierarchical algorithm is proposed according to the divide-and-conquer strategy. Path planning problem with large-scale detecting points is decomposed into a number of path planning sub-problems with small-scale detect...

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Veröffentlicht in:2017 International Conference on Robotics and Automation Sciences (ICRAS) S. 25 - 29
Hauptverfasser: Erchong Liao, Changan Liu, Limei Jiang
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.08.2017
Schlagworte:
Algorithm design and analysis
ant colony optimization algorithm
Classification algorithms
climbing robot
Climbing robots
Clustering algorithms
density peaks clustering algorithm
Path planning
Runtime
traveling salesman problem
ISBN:1538639947, 9781538639948
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Abstract To solve climbing robot path planning in spatial trusses with detecting points, a hierarchical algorithm is proposed according to the divide-and-conquer strategy. Path planning problem with large-scale detecting points is decomposed into a number of path planning sub-problems with small-scale detecting points by Density Peaks Clustering algorithm(DPC), and then all sub-problems with small-scale detecting points are resolved by Ant Colony Optimization algorithm(ACO). At last, all the paths obtained from each subproblem are merged, and local path is optimized by 2-Opt and 3-Opt algorithms. The hierarchical algorithm is tested on 5 different instances. Experimental results show that the proposed algorithm has the same accuracy and computational complexity can be decreased n squared compared with ACO algorithm. The efficiency has been significantly improved.
AbstractList To solve climbing robot path planning in spatial trusses with detecting points, a hierarchical algorithm is proposed according to the divide-and-conquer strategy. Path planning problem with large-scale detecting points is decomposed into a number of path planning sub-problems with small-scale detecting points by Density Peaks Clustering algorithm(DPC), and then all sub-problems with small-scale detecting points are resolved by Ant Colony Optimization algorithm(ACO). At last, all the paths obtained from each subproblem are merged, and local path is optimized by 2-Opt and 3-Opt algorithms. The hierarchical algorithm is tested on 5 different instances. Experimental results show that the proposed algorithm has the same accuracy and computational complexity can be decreased n squared compared with ACO algorithm. The efficiency has been significantly improved.
Author Limei Jiang
Erchong Liao
Changan Liu
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  surname: Changan Liu
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  email: liuchangan@ncpeu.edu.cn
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  surname: Limei Jiang
  fullname: Limei Jiang
  email: qhdllmh@163.com
  organization: Sch. of Control & Comput. Eng., North China Electr. Power Univ., Baoding, China
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Snippet To solve climbing robot path planning in spatial trusses with detecting points, a hierarchical algorithm is proposed according to the divide-and-conquer...
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SubjectTerms Algorithm design and analysis
ant colony optimization algorithm
Classification algorithms
climbing robot
Climbing robots
Clustering algorithms
density peaks clustering algorithm
Path planning
Runtime
traveling salesman problem
Title Hierarchical detecting points path planning algorithm for climbing robot in spatial trusses
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