{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:23:09Z","timestamp":1777702989274,"version":"3.51.4"},"reference-count":17,"publisher":"SAGE Publications","issue":"5","license":[{"start":{"date-parts":[[2019,1,29]],"date-time":"2019-01-29T00:00:00Z","timestamp":1548720000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2019,5,14]]},"abstract":"<jats:p>\u00a0Navigation of multiple robots is a challenging task, particularly for many robots, since individual gains may more often than not adversely affect global gain. This paper investigates the problem of multiple robots moving towards individual goals within a common workspace without colliding amongst themselves. Two solutions for coordination namely Fuzzy Logic Controller (FLC) and Genetic Algorithm based FLC (GA-FLC) have been employed and the efficacy of cooperation strategies have been compared with their non-cooperative counterparts as well as with the fundamental potential field method (PFM). Proposed coordination schemes are verified through simulations. A total of 100 scenarios are considered varying the number of robots (8, 12, 16 and 20). The obtained results show the efficacy of the proposed schemes.<\/jats:p>","DOI":"10.3233\/jifs-169996","type":"journal-article","created":{"date-parts":[[2019,1,29]],"date-time":"2019-01-29T12:11:17Z","timestamp":1548763877000},"page":"4413-4423","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":9,"title":["Intelligent navigation of multiple coordinated robots"],"prefix":"10.1177","volume":"36","author":[{"given":"Buddhadeb","family":"Pradhan","sequence":"first","affiliation":[{"name":"Department of Mechanical Engineering, National Institute of Technology Durgapur, Durgapur, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"V.","family":"Vijayakumar","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, VIT, Chennai, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nirmal Baran","family":"Hui","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, National Institute of Technology Durgapur, Durgapur, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Diptendu","family":"Sinha Roy","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, National Institute of Technology Meghalaya, Shillong, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2019,1,29]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2015.05.116"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.robot.2016.12.006"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11721-012-0075-2"},{"key":"e_1_3_1_5_2","first-page":"27","volume-title":"Coordinated motion planning of multiple mobile robots using potential field method, International Conference on Industrial Electronics","author":"Hui N.B.","year":"2010","unstructured":"HuiN.B.Coordinated motion planning of multiple mobile robots using potential field method, International Conference on Industrial Electronics, Control and Robotics (IECR 2010), NIT Rourkela, India, Dec. 27\u201329, 2010."},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0165-0114(02)00230-0"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/TETCI.2017.2741505"},{"issue":"3","key":"e_1_3_1_8_2","first-page":"219","article-title":"Automatic learning of Action Knowledge-Base for a mobile robot using genetic algorithms","volume":"15","author":"Watabe H.","year":"2004","unstructured":"WatabeH. and KawaokaT., Automatic learning of Action Knowledge-Base for a mobile robot using genetic algorithms, Journal of Intelligent and Fuzzy Systems15(3,4) (2004), 219\u2013223.","journal-title":"Journal of Intelligent and Fuzzy Systems"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.3233\/JIFS-17348"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1504\/IJCVR.2018.095002"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2015.10.011"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.fss.2006.04.004"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2011.05.001"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1504\/IJMA.2014.064098"},{"issue":"2","key":"e_1_3_1_15_2","first-page":"334","article-title":"Prototype optimization of reconfigurable mobile robots based on a modified Harmony Search method","volume":"34","author":"He Xu,","year":"2010","unstructured":"HeXu, X.Z.Gao, Gao-liangPeng, KaiXue and YulinMa,Prototype optimization of reconfigurable mobile robots based on a modified Harmony Search method, Transactions of the Institute of Measurement and Control34(2\u20133) (2010), 334\u2013360.","journal-title":"Transactions of the Institute of Measurement and Control"},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2015.2395073"},{"key":"e_1_3_1_17_2","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2016.2555315"},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-13-1595-4_63"}],"container-title":["Journal of Intelligent &amp; 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