Loading…

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...

Full description

Saved in:
Bibliographic Details
Published in:Robotics and biomimetics 2017-11, Vol.4 (1), p.8-15, Article 8
Main Authors: Ayari, Asma, Bouamama, Sadok
Format: Article
Language:English
Subjects:
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c3816-7e72890ce62204df467fd135c72f437ad4bdb9392bf9275e13bc55716b3d24b83
cites cdi_FETCH-LOGICAL-c3816-7e72890ce62204df467fd135c72f437ad4bdb9392bf9275e13bc55716b3d24b83
container_end_page 15
container_issue 1
container_start_page 8
container_title Robotics and biomimetics
container_volume 4
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
fullrecord <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_5668356</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>1966443278</sourcerecordid><originalsourceid>FETCH-LOGICAL-c3816-7e72890ce62204df467fd135c72f437ad4bdb9392bf9275e13bc55716b3d24b83</originalsourceid><addsrcrecordid>eNp1kU1rFTEUhoMotrT9AW4k4MbN1HxNknEhlKJVKHRjlyXka-5NmUnGJGOpv95cbi1XwVUC5znvOe95AXiD0TnGkn8oDHEqO4RFhxAnHX8BjgkeREcFly8P_kfgrJR7hBCmjGImX4MjMuCeMDYcg7sLGP0DnNephmXyMCeTKlx03cJl0jGGuIF62qQc6nb-CN1j1HOw0IVSczBr9a7BuQbbesuDzjNMSw1z-KVrSPEUvBr1VPzZ03sCbr98_n75tbu-ufp2eXHdWSox74QXRA7Iek4IYm5kXIwO094KMjIqtGPGmYEOxIwDEb3H1Ni-F5gb6ggzkp6AT3vdZTWzd9bHmvWklhxmnR9V0kH9XYlhqzbpp-o5l7TnTeD9k0BOP1ZfqppDsX5qJ_BpLQoPnDNGidjNevcPep_WHJu9RsnmABNBG4X3lM2plOzH52UwUrv81D4_1fJTu_zUbom3hy6eO_6k1QCyB0orxY3PB6P_q_oboWmm6Q</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1986221273</pqid></control><display><type>article</type><title>A new multiple robot path planning algorithm: dynamic distributed particle swarm optimization</title><source>Springer Nature - SpringerLink Journals - Fully Open Access </source><creator>Ayari, Asma ; Bouamama, Sadok</creator><creatorcontrib>Ayari, Asma ; Bouamama, Sadok</creatorcontrib><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><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 &amp; Engineering Collection</collection><collection>ProQuest Central</collection><collection>Advanced Technologies &amp; 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 &amp; Aerospace Database</collection><collection>ProQuest Advanced Technologies &amp; 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>
fulltext fulltext
identifier ISSN: 2197-3768
ispartof Robotics and biomimetics, 2017-11, Vol.4 (1), p.8-15, Article 8
issn 2197-3768
2197-3768
language eng
recordid cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_5668356
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
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-09-22T01%3A33%3A01IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20new%20multiple%20robot%20path%20planning%20algorithm:%20dynamic%20distributed%20particle%20swarm%20optimization&rft.jtitle=Robotics%20and%20biomimetics&rft.au=Ayari,%20Asma&rft.date=2017-11-02&rft.volume=4&rft.issue=1&rft.spage=8&rft.epage=15&rft.pages=8-15&rft.artnum=8&rft.issn=2197-3768&rft.eissn=2197-3768&rft_id=info:doi/10.1186/s40638-017-0062-6&rft_dat=%3Cproquest_pubme%3E1966443278%3C/proquest_pubme%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c3816-7e72890ce62204df467fd135c72f437ad4bdb9392bf9275e13bc55716b3d24b83%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=1986221273&rft_id=info:pmid/29152449&rfr_iscdi=true