Maximizing the Coverage of Roadmap Graph for Optimal Motion Planning
Automated motion-planning technologies for industrial robots are critical for their application to Industry 4.0. Various sampling-based methods have been studied to generate the collision-free motion of articulated industrial robots. Such sampling-based methods provide efficient solutions to complex...
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2018/9104720 |
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| _version_ | 1832556575262244864 |
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| author | Jae-Han Park Tae-Woong Yoon |
| author_facet | Jae-Han Park Tae-Woong Yoon |
| author_sort | Jae-Han Park |
| collection | DOAJ |
| description | Automated motion-planning technologies for industrial robots are critical for their application to Industry 4.0. Various sampling-based methods have been studied to generate the collision-free motion of articulated industrial robots. Such sampling-based methods provide efficient solutions to complex planning problems, but their limitations hinder the attainment of optimal results. This paper considers a method to obtain the optimal results in the roadmap algorithm that is representative of the sampling-based method. We define the coverage of a graph as a performance index of its optimality as constructed by a sampling-based algorithm and propose an optimization algorithm that can maximize graph coverage in the configuration space. The proposed method was applied to the model of an industrial robot, and the results of the simulation confirm that the roadmap graph obtained by the proposed algorithm can generate results of satisfactory quality in path-finding tests under various conditions. |
| format | Article |
| id | doaj-art-da1f0ae1a2d54fc8ae22f702e778521d |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-da1f0ae1a2d54fc8ae22f702e778521d2025-02-03T05:45:06ZengWileyComplexity1076-27871099-05262018-01-01201810.1155/2018/91047209104720Maximizing the Coverage of Roadmap Graph for Optimal Motion PlanningJae-Han Park0Tae-Woong Yoon1Robotics R&D Group, Korea Institute of Industrial Technology (KITECH), Ansan, Republic of KoreaSchool of Electrical Engineering, Korea University, Seoul, Republic of KoreaAutomated motion-planning technologies for industrial robots are critical for their application to Industry 4.0. Various sampling-based methods have been studied to generate the collision-free motion of articulated industrial robots. Such sampling-based methods provide efficient solutions to complex planning problems, but their limitations hinder the attainment of optimal results. This paper considers a method to obtain the optimal results in the roadmap algorithm that is representative of the sampling-based method. We define the coverage of a graph as a performance index of its optimality as constructed by a sampling-based algorithm and propose an optimization algorithm that can maximize graph coverage in the configuration space. The proposed method was applied to the model of an industrial robot, and the results of the simulation confirm that the roadmap graph obtained by the proposed algorithm can generate results of satisfactory quality in path-finding tests under various conditions.http://dx.doi.org/10.1155/2018/9104720 |
| spellingShingle | Jae-Han Park Tae-Woong Yoon Maximizing the Coverage of Roadmap Graph for Optimal Motion Planning Complexity |
| title | Maximizing the Coverage of Roadmap Graph for Optimal Motion Planning |
| title_full | Maximizing the Coverage of Roadmap Graph for Optimal Motion Planning |
| title_fullStr | Maximizing the Coverage of Roadmap Graph for Optimal Motion Planning |
| title_full_unstemmed | Maximizing the Coverage of Roadmap Graph for Optimal Motion Planning |
| title_short | Maximizing the Coverage of Roadmap Graph for Optimal Motion Planning |
| title_sort | maximizing the coverage of roadmap graph for optimal motion planning |
| url | http://dx.doi.org/10.1155/2018/9104720 |
| work_keys_str_mv | AT jaehanpark maximizingthecoverageofroadmapgraphforoptimalmotionplanning AT taewoongyoon maximizingthecoverageofroadmapgraphforoptimalmotionplanning |