{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T15:27:06Z","timestamp":1772119626669,"version":"3.50.1"},"reference-count":20,"publisher":"Maximum Academic Press","license":[{"start":{"date-parts":[[2016,9,9]],"date-time":"2016-09-09T00:00:00Z","timestamp":1473379200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/www.cambridge.org\/core\/terms"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["The Knowledge Engineering Review"],"published-print":{"date-parts":[[2017]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Obstacle avoidance is an important issue in robotics. In this paper, the particle\n                    swarm optimization (PSO) algorithm, which is inspired by the collective\n                    behaviors of birds, has been designed for solving the obstacle avoidance\n                    problem. Some animals that travel to the different places at a specific time of\n                    the year are called migrants. The migrants also represent the particles of PSO\n                    for defining the walking paths in this work. Migrants consider not only the\n                    collective behaviors, but also geomagnetic fields during their migration in\n                    nature. Therefore, in order to improve the performance and the convergence speed\n                    of the PSO algorithm, concepts from the migrant navigation method have been\n                    adopted for use in the proposed hybrid particle swarm optimization (H-PSO)\n                    algorithm. Moreover, the potential field navigation method and the designed\n                    fuzzy logic controller have been combined in H-PSO, which provided a good\n                    performance in the simulation and the experimental results. Finally, the\n                    Federation of International Robot-soccer Association (FIRA) HuroCup Obstacle Run\n                    Event has been chosen for validating the feasibility and the practicability of\n                    the proposed method in real time. The designed adult-sized humanoid robot also\n                    performed well in the 2015 FIRA HuroCup Obstacle Run Event through utilizing the\n                    proposed H-PSO.<\/jats:p>","DOI":"10.1017\/s0269888916000151","type":"journal-article","created":{"date-parts":[[2016,9,9]],"date-time":"2016-09-09T02:01:19Z","timestamp":1473386479000},"source":"Crossref","is-referenced-by-count":14,"title":["A migrant-inspired path planning algorithm for obstacle run using\n                    particle swarm optimization, potential field navigation, and fuzzy logic\n                    controller"],"prefix":"10.48130","volume":"32","author":[{"given":"Ping-Huan","family":"Kuo","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tzuu-Hseng S.","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guan-Yu","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ya-Fang","family":"Ho","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chih-Jui","family":"Lin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"27968","published-online":{"date-parts":[[2016,9,9]]},"reference":[{"key":"S0269888916000151_ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TMECH.2013.2252076"},{"key":"S0269888916000151_ref17","doi-asserted-by":"publisher","DOI":"10.1038\/383158a0"},{"key":"S0269888916000151_ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2013.2279802"},{"key":"S0269888916000151_ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TSMCA.2012.2227719"},{"key":"S0269888916000151_ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.robot.2006.04.001"},{"key":"S0269888916000151_ref5","doi-asserted-by":"publisher","DOI":"10.1038\/scientificamerican0792-66"},{"key":"S0269888916000151_ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TRO.2014.2312812"},{"key":"S0269888916000151_ref10","unstructured":"Kuo P.-H. & Li T.-H. S. 2011. Development of simulator for AndroSot in FIRA. In Proceedings of the FIRA 2011, CCIS 212, 233\u2013240."},{"key":"S0269888916000151_ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijepes.2011.08.012"},{"key":"S0269888916000151_ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2009.03.004"},{"key":"S0269888916000151_ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2013.2250955"},{"key":"S0269888916000151_ref7","unstructured":"Kennedy J. & Eberhart R. 1992. Particle swarm optimization. In Proceedings of the IEEE International Conference on Neural Networks, 66\u201372."},{"key":"S0269888916000151_ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.enconman.2012.02.024"},{"key":"S0269888916000151_ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.2012.2234943"},{"key":"S0269888916000151_ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2013.2290223"},{"key":"S0269888916000151_ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2013.2253605"},{"key":"S0269888916000151_ref4","unstructured":"FIRA Homepage 2014. FIRA HuroCup Rules. http:\/\/www.fira.net\/contents\/sub03\/sub03_1.asp."},{"key":"S0269888916000151_ref9","unstructured":"Koren Y. & Borenstein J. 1991. Potential field methods and their inherent limitations for mobile robot navigation. In Proceedings of the 1991 IEEE International Conference on Robotics and Automation, 1398\u20131404."},{"key":"S0269888916000151_ref14","unstructured":"Shimoda S. , Kuroda Y. & Iagnemma K. 2005. Potential field navigation of high speed unmanned ground vehicles on uneven terrain. In Proceedings of the 2005 IEEE International Conference on Robotics and Automation, 2839\u20132844. http:\/\/ieeexplore.ieee.org\/document\/1570542\/."},{"key":"S0269888916000151_ref15","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1109\/TSMCB.2012.2214382","article-title":"A new surrogate-assisted interactive genetic\n                        algorithm with weighted semisupervised learning","volume":"43","author":"Sun","year":"2013","journal-title":"IEEE Transactions on Cybernetics"}],"container-title":["The Knowledge Engineering Review"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.cambridge.org\/core\/services\/aop-cambridge-core\/content\/view\/S0269888916000151","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T14:42:08Z","timestamp":1767624128000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.cambridge.org\/core\/product\/identifier\/S0269888916000151\/type\/journal_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,9,9]]},"references-count":20,"alternative-id":["S0269888916000151"],"URL":"https:\/\/doi.org\/10.1017\/s0269888916000151","relation":{},"ISSN":["0269-8889","1469-8005"],"issn-type":[{"value":"0269-8889","type":"print"},{"value":"1469-8005","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,9,9]]},"article-number":"e5"}}