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A new multiple robot path planning algorithm: dynamic distributed particle swarm optimization
Multiple robot systems have become a major study concern in the field of robotic research. Their control becomes unreliable and even infeasible if the number of robots increases. In this paper, a new dynamic distributed particle swarm optimization (D 2 PSO) algorithm is proposed for trajectory path...
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Published in: | Robotics and biomimetics 2017-11, Vol.4 (1), p.8-15, Article 8 |
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creator | Ayari, Asma Bouamama, Sadok |
description | Multiple robot systems have become a major study concern in the field of robotic research. Their control becomes unreliable and even infeasible if the number of robots increases. In this paper, a new dynamic distributed particle swarm optimization (D
2
PSO) algorithm is proposed for trajectory path planning of multiple robots in order to find collision-free optimal path for each robot in the environment. The proposed approach consists in calculating two local optima detectors, LOD
pBest
and LOD
gBest
. Particles which are unable to improve their personal best and global best for predefined number of successive iterations would be replaced with restructured ones. Stagnation and local optima problems would be avoided by adding diversity to the population, without losing the fast convergence characteristic of PSO. Experiments with multiple robots are provided and proved effectiveness of such approach compared with the distributed PSO. |
doi_str_mv | 10.1186/s40638-017-0062-6 |
format | article |
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2
PSO) algorithm is proposed for trajectory path planning of multiple robots in order to find collision-free optimal path for each robot in the environment. The proposed approach consists in calculating two local optima detectors, LOD
pBest
and LOD
gBest
. Particles which are unable to improve their personal best and global best for predefined number of successive iterations would be replaced with restructured ones. Stagnation and local optima problems would be avoided by adding diversity to the population, without losing the fast convergence characteristic of PSO. Experiments with multiple robots are provided and proved effectiveness of such approach compared with the distributed PSO.</description><identifier>ISSN: 2197-3768</identifier><identifier>EISSN: 2197-3768</identifier><identifier>DOI: 10.1186/s40638-017-0062-6</identifier><identifier>PMID: 29152449</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Behavioral Sciences ; Collision avoidance ; Collision dynamics ; Engineering ; Multiple robots ; Particle swarm optimization ; Robotics and Automation ; Robots ; Stagnation ; Systems Biology ; Trajectory planning</subject><ispartof>Robotics and biomimetics, 2017-11, Vol.4 (1), p.8-15, Article 8</ispartof><rights>The Author(s) 2017</rights><rights>Robotics and Biomimetics is a copyright of Springer, (2017). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3816-7e72890ce62204df467fd135c72f437ad4bdb9392bf9275e13bc55716b3d24b83</citedby><cites>FETCH-LOGICAL-c3816-7e72890ce62204df467fd135c72f437ad4bdb9392bf9275e13bc55716b3d24b83</cites><orcidid>0000-0002-3805-6726</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,315,786,790,891,27957,27958</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/29152449$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Ayari, Asma</creatorcontrib><creatorcontrib>Bouamama, Sadok</creatorcontrib><title>A new multiple robot path planning algorithm: dynamic distributed particle swarm optimization</title><title>Robotics and biomimetics</title><addtitle>Robot. Biomim</addtitle><addtitle>Robotics Biomim</addtitle><description>Multiple robot systems have become a major study concern in the field of robotic research. Their control becomes unreliable and even infeasible if the number of robots increases. In this paper, a new dynamic distributed particle swarm optimization (D
2
PSO) algorithm is proposed for trajectory path planning of multiple robots in order to find collision-free optimal path for each robot in the environment. The proposed approach consists in calculating two local optima detectors, LOD
pBest
and LOD
gBest
. Particles which are unable to improve their personal best and global best for predefined number of successive iterations would be replaced with restructured ones. Stagnation and local optima problems would be avoided by adding diversity to the population, without losing the fast convergence characteristic of PSO. Experiments with multiple robots are provided and proved effectiveness of such approach compared with the distributed PSO.</description><subject>Behavioral Sciences</subject><subject>Collision avoidance</subject><subject>Collision dynamics</subject><subject>Engineering</subject><subject>Multiple robots</subject><subject>Particle swarm optimization</subject><subject>Robotics and Automation</subject><subject>Robots</subject><subject>Stagnation</subject><subject>Systems Biology</subject><subject>Trajectory planning</subject><issn>2197-3768</issn><issn>2197-3768</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><recordid>eNp1kU1rFTEUhoMotrT9AW4k4MbN1HxNknEhlKJVKHRjlyXka-5NmUnGJGOpv95cbi1XwVUC5znvOe95AXiD0TnGkn8oDHEqO4RFhxAnHX8BjgkeREcFly8P_kfgrJR7hBCmjGImX4MjMuCeMDYcg7sLGP0DnNephmXyMCeTKlx03cJl0jGGuIF62qQc6nb-CN1j1HOw0IVSczBr9a7BuQbbesuDzjNMSw1z-KVrSPEUvBr1VPzZ03sCbr98_n75tbu-ufp2eXHdWSox74QXRA7Iek4IYm5kXIwO094KMjIqtGPGmYEOxIwDEb3H1Ni-F5gb6ggzkp6AT3vdZTWzd9bHmvWklhxmnR9V0kH9XYlhqzbpp-o5l7TnTeD9k0BOP1ZfqppDsX5qJ_BpLQoPnDNGidjNevcPep_WHJu9RsnmABNBG4X3lM2plOzH52UwUrv81D4_1fJTu_zUbom3hy6eO_6k1QCyB0orxY3PB6P_q_oboWmm6Q</recordid><startdate>20171102</startdate><enddate>20171102</enddate><creator>Ayari, Asma</creator><creator>Bouamama, Sadok</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>C6C</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>ABJCF</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>L6V</scope><scope>LK8</scope><scope>M7P</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-3805-6726</orcidid></search><sort><creationdate>20171102</creationdate><title>A new multiple robot path planning algorithm: dynamic distributed particle swarm optimization</title><author>Ayari, Asma ; Bouamama, Sadok</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3816-7e72890ce62204df467fd135c72f437ad4bdb9392bf9275e13bc55716b3d24b83</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Behavioral Sciences</topic><topic>Collision avoidance</topic><topic>Collision dynamics</topic><topic>Engineering</topic><topic>Multiple robots</topic><topic>Particle swarm optimization</topic><topic>Robotics and Automation</topic><topic>Robots</topic><topic>Stagnation</topic><topic>Systems Biology</topic><topic>Trajectory planning</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ayari, Asma</creatorcontrib><creatorcontrib>Bouamama, Sadok</creatorcontrib><collection>Springer Open Access</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central</collection><collection>Advanced Technologies & Aerospace Database (1962 - current)</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>AUTh Library subscriptions: ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection (Proquest) (PQ_SDU_P3)</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>ProQuest Engineering Collection</collection><collection>Biological Sciences</collection><collection>Biological Science Database</collection><collection>ProQuest Engineering Database</collection><collection>ProQuest Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Robotics and biomimetics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ayari, Asma</au><au>Bouamama, Sadok</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A new multiple robot path planning algorithm: dynamic distributed particle swarm optimization</atitle><jtitle>Robotics and biomimetics</jtitle><stitle>Robot. Biomim</stitle><addtitle>Robotics Biomim</addtitle><date>2017-11-02</date><risdate>2017</risdate><volume>4</volume><issue>1</issue><spage>8</spage><epage>15</epage><pages>8-15</pages><artnum>8</artnum><issn>2197-3768</issn><eissn>2197-3768</eissn><notes>ObjectType-Article-1</notes><notes>SourceType-Scholarly Journals-1</notes><notes>ObjectType-Feature-2</notes><notes>content type line 23</notes><abstract>Multiple robot systems have become a major study concern in the field of robotic research. Their control becomes unreliable and even infeasible if the number of robots increases. In this paper, a new dynamic distributed particle swarm optimization (D
2
PSO) algorithm is proposed for trajectory path planning of multiple robots in order to find collision-free optimal path for each robot in the environment. The proposed approach consists in calculating two local optima detectors, LOD
pBest
and LOD
gBest
. Particles which are unable to improve their personal best and global best for predefined number of successive iterations would be replaced with restructured ones. Stagnation and local optima problems would be avoided by adding diversity to the population, without losing the fast convergence characteristic of PSO. Experiments with multiple robots are provided and proved effectiveness of such approach compared with the distributed PSO.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>29152449</pmid><doi>10.1186/s40638-017-0062-6</doi><tpages>15</tpages><orcidid>https://orcid.org/0000-0002-3805-6726</orcidid><oa>free_for_read</oa></addata></record> |
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source | Springer Nature - SpringerLink Journals - Fully Open Access |
subjects | Behavioral Sciences Collision avoidance Collision dynamics Engineering Multiple robots Particle swarm optimization Robotics and Automation Robots Stagnation Systems Biology Trajectory planning |
title | A new multiple robot path planning algorithm: dynamic distributed particle swarm optimization |
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