Optimal Multirobot Coverage Path Planning: Ideal-Shaped Spanning Tree
The present paper attempts to find the optimal coverage path for multiple robots in a given area including obstacles. For single robot coverage path planning (CPP) problem, an improved ant colony optimization (ACO) algorithm is proposed to construct the best spanning tree and then obtain the optimal...
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| Veröffentlicht in: | Mathematical problems in engineering 2018-01, Vol.2018 (2018), p.1-10 |
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| container_title | Mathematical problems in engineering |
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| creator | Li, You Xu, An Li, Zhanwu Kou, Yingxin Gao, Chunqing Chang, Yizhe |
| description | The present paper attempts to find the optimal coverage path for multiple robots in a given area including obstacles. For single robot coverage path planning (CPP) problem, an improved ant colony optimization (ACO) algorithm is proposed to construct the best spanning tree and then obtain the optimal path, which contributes to minimizing the energy/time consumption. For the multirobot case, first the DARP (Divide Areas based on Robots Initial Positions) algorithm is utilized to divide the area into separate equal subareas, so much so that it transforms the mCPP problem into several CPP problems, degrading the computation complexity. During the second phase, spanning tree in each subarea is constructed by the aforementioned algorithm. In the last phase, the specific end nodes are exchanged among subareas to achieve ideal-shaped spanning trees, which can also decrease the number of turns in coverage path. And the complete algorithms are proven to be approximately polynomial algorithms. Finally, the simulation confirms the complete algorithms’ advantages: complete coverage, nonbacktracks, minimum length, zero preparation time, and the least number of turns. |
| doi_str_mv | 10.1155/2018/3436429 |
| format | Article |
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For single robot coverage path planning (CPP) problem, an improved ant colony optimization (ACO) algorithm is proposed to construct the best spanning tree and then obtain the optimal path, which contributes to minimizing the energy/time consumption. For the multirobot case, first the DARP (Divide Areas based on Robots Initial Positions) algorithm is utilized to divide the area into separate equal subareas, so much so that it transforms the mCPP problem into several CPP problems, degrading the computation complexity. During the second phase, spanning tree in each subarea is constructed by the aforementioned algorithm. In the last phase, the specific end nodes are exchanged among subareas to achieve ideal-shaped spanning trees, which can also decrease the number of turns in coverage path. And the complete algorithms are proven to be approximately polynomial algorithms. Finally, the simulation confirms the complete algorithms’ advantages: complete coverage, nonbacktracks, minimum length, zero preparation time, and the least number of turns.</description><identifier>ISSN: 1024-123X</identifier><identifier>EISSN: 1563-5147</identifier><identifier>DOI: 10.1155/2018/3436429</identifier><language>eng</language><publisher>Cairo, Egypt: Hindawi Publishing Corporation</publisher><subject>Algorithms ; Ant colony optimization ; Automation ; Computer simulation ; Decomposition ; Graph theory ; Intelligence ; International conferences ; Methods ; Multiple robots ; Path planning ; Robotics ; Robots</subject><ispartof>Mathematical problems in engineering, 2018-01, Vol.2018 (2018), p.1-10</ispartof><rights>Copyright © 2018 Chunqing Gao et al.</rights><rights>Copyright © 2018 Chunqing Gao et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c426t-a602836d354b86eeb7da8c0fa8bbfb78da398ad55db319817c170e589cfee0f83</citedby><cites>FETCH-LOGICAL-c426t-a602836d354b86eeb7da8c0fa8bbfb78da398ad55db319817c170e589cfee0f83</cites><orcidid>0000-0001-8205-4501 ; 0000-0001-8241-7018 ; 0000-0001-7093-5862 ; 0000-0002-3293-4655</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27923,27924</link.rule.ids></links><search><contributor>Naddeo, Alessandro</contributor><contributor>Alessandro Naddeo</contributor><creatorcontrib>Li, You</creatorcontrib><creatorcontrib>Xu, An</creatorcontrib><creatorcontrib>Li, Zhanwu</creatorcontrib><creatorcontrib>Kou, Yingxin</creatorcontrib><creatorcontrib>Gao, Chunqing</creatorcontrib><creatorcontrib>Chang, Yizhe</creatorcontrib><title>Optimal Multirobot Coverage Path Planning: Ideal-Shaped Spanning Tree</title><title>Mathematical problems in engineering</title><description>The present paper attempts to find the optimal coverage path for multiple robots in a given area including obstacles. 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| subjects | Algorithms Ant colony optimization Automation Computer simulation Decomposition Graph theory Intelligence International conferences Methods Multiple robots Path planning Robotics Robots |
| title | Optimal Multirobot Coverage Path Planning: Ideal-Shaped Spanning Tree |
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