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Path Planning Method for UUV Homing and Docking in Movement Disorders Environment
Path planning method for unmanned underwater vehicles (UUV) homing and docking in movement disorders environment is proposed in this paper. Firstly, cost function is proposed for path planning. Then, a novel particle swarm optimization (NPSO) is proposed and applied to find the waypoint with minimum...
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Published in: | TheScientificWorld 2014-01, Vol.2014 (2014), p.1-13 |
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description | Path planning method for unmanned underwater vehicles (UUV) homing and docking in movement disorders environment is proposed in this paper. Firstly, cost function is proposed for path planning. Then, a novel particle swarm optimization (NPSO) is proposed and applied to find the waypoint with minimum value of cost function. Then, a strategy for UUV enters into the mother vessel with a fixed angle being proposed. Finally, the test function is introduced to analyze the performance of NPSO and compare with basic particle swarm optimization (BPSO), inertia weight particle swarm optimization (LWPSO, EPSO), and time-varying acceleration coefficient (TVAC). It has turned out that, for unimodal functions, NPSO performed better searching accuracy and stability than other algorithms, and, for multimodal functions, the performance of NPSO is similar to TVAC. Then, the simulation of UUV path planning is presented, and it showed that, with the strategy proposed in this paper, UUV can dodge obstacles and threats, and search for the efficiency path. |
doi_str_mv | 10.1155/2014/246469 |
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Firstly, cost function is proposed for path planning. Then, a novel particle swarm optimization (NPSO) is proposed and applied to find the waypoint with minimum value of cost function. Then, a strategy for UUV enters into the mother vessel with a fixed angle being proposed. Finally, the test function is introduced to analyze the performance of NPSO and compare with basic particle swarm optimization (BPSO), inertia weight particle swarm optimization (LWPSO, EPSO), and time-varying acceleration coefficient (TVAC). It has turned out that, for unimodal functions, NPSO performed better searching accuracy and stability than other algorithms, and, for multimodal functions, the performance of NPSO is similar to TVAC. Then, the simulation of UUV path planning is presented, and it showed that, with the strategy proposed in this paper, UUV can dodge obstacles and threats, and search for the efficiency path.</description><identifier>ISSN: 2356-6140</identifier><identifier>ISSN: 1537-744X</identifier><identifier>EISSN: 1537-744X</identifier><identifier>DOI: 10.1155/2014/246469</identifier><identifier>PMID: 25054169</identifier><language>eng</language><publisher>Cairo, Egypt: Hindawi Publishing Corporation</publisher><subject>Colleges & universities ; Computer Simulation ; Control ; Electronics in navigation ; Engineering research ; Hydrology ; Mathematical optimization ; Methods ; Models, Theoretical ; Motion ; Motion control ; Personal computers ; Remote submersibles ; Ships - methods ; Software packages ; Underwater vehicles</subject><ispartof>TheScientificWorld, 2014-01, Vol.2014 (2014), p.1-13</ispartof><rights>Copyright © 2014 Zheping Yan et al.</rights><rights>COPYRIGHT 2014 John Wiley & Sons, Inc.</rights><rights>Copyright © 2014 Zheping Yan et al. 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This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</rights><rights>Copyright © 2014 Zheping Yan et al. 2014</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c605t-28a3cbe780c81ce2c25b1b3813010c9f3e7a3c220da09d4d42d7262c68c426f53</citedby><cites>FETCH-LOGICAL-c605t-28a3cbe780c81ce2c25b1b3813010c9f3e7a3c220da09d4d42d7262c68c426f53</cites><orcidid>0000-0002-8702-339X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/1552838776/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/1552838776?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,25753,27924,27925,37012,37013,44590,53791,53793,74998</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/25054169$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Trunfio, Giuseppe A.</contributor><creatorcontrib>Chen, Tao</creatorcontrib><creatorcontrib>Chi, Dongnan</creatorcontrib><creatorcontrib>Deng, Chao</creatorcontrib><creatorcontrib>Yan, Zheping</creatorcontrib><creatorcontrib>Hou, Shuping</creatorcontrib><title>Path Planning Method for UUV Homing and Docking in Movement Disorders Environment</title><title>TheScientificWorld</title><addtitle>ScientificWorldJournal</addtitle><description>Path planning method for unmanned underwater vehicles (UUV) homing and docking in movement disorders environment is proposed in this paper. Firstly, cost function is proposed for path planning. Then, a novel particle swarm optimization (NPSO) is proposed and applied to find the waypoint with minimum value of cost function. Then, a strategy for UUV enters into the mother vessel with a fixed angle being proposed. Finally, the test function is introduced to analyze the performance of NPSO and compare with basic particle swarm optimization (BPSO), inertia weight particle swarm optimization (LWPSO, EPSO), and time-varying acceleration coefficient (TVAC). It has turned out that, for unimodal functions, NPSO performed better searching accuracy and stability than other algorithms, and, for multimodal functions, the performance of NPSO is similar to TVAC. 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Firstly, cost function is proposed for path planning. Then, a novel particle swarm optimization (NPSO) is proposed and applied to find the waypoint with minimum value of cost function. Then, a strategy for UUV enters into the mother vessel with a fixed angle being proposed. Finally, the test function is introduced to analyze the performance of NPSO and compare with basic particle swarm optimization (BPSO), inertia weight particle swarm optimization (LWPSO, EPSO), and time-varying acceleration coefficient (TVAC). It has turned out that, for unimodal functions, NPSO performed better searching accuracy and stability than other algorithms, and, for multimodal functions, the performance of NPSO is similar to TVAC. Then, the simulation of UUV path planning is presented, and it showed that, with the strategy proposed in this paper, UUV can dodge obstacles and threats, and search for the efficiency path.</abstract><cop>Cairo, Egypt</cop><pub>Hindawi Publishing Corporation</pub><pmid>25054169</pmid><doi>10.1155/2014/246469</doi><tpages>13</tpages><orcidid>https://orcid.org/0000-0002-8702-339X</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Colleges & universities Computer Simulation Control Electronics in navigation Engineering research Hydrology Mathematical optimization Methods Models, Theoretical Motion Motion control Personal computers Remote submersibles Ships - methods Software packages Underwater vehicles |
title | Path Planning Method for UUV Homing and Docking in Movement Disorders Environment |
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