{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,28]],"date-time":"2026-10-28T16:49:34Z","timestamp":1793206174565,"version":"4.5.0"},"reference-count":90,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,11,3]],"date-time":"2023-11-03T00:00:00Z","timestamp":1698969600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurorobot."],"abstract":"<jats:p>Path planning is an essential part of robot intelligence. In this paper, we summarize the characteristics of path planning of industrial robots. And owing to the probabilistic completeness, we review the rapidly-exploring random tree (RRT) algorithm which is widely used in the path planning of industrial robots. Aiming at the shortcomings of the RRT algorithm, this paper investigates the RRT algorithm for path planning of industrial robots in order to improve its intelligence. Finally, the future development direction of the RRT algorithm for path planning of industrial robots is proposed. The study results have particularly guided significance for the development of the path planning of industrial robots and the applicability and practicability of the RRT algorithm.<\/jats:p>","DOI":"10.3389\/fnbot.2023.1268447","type":"journal-article","created":{"date-parts":[[2023,11,3]],"date-time":"2023-11-03T04:54:51Z","timestamp":1698987291000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":49,"title":["A survey of path planning of industrial robots based on rapidly exploring random trees"],"prefix":"10.3389","volume":"17","author":[{"given":"Sha","family":"Luo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingyue","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongbo","family":"Zhuang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cheng","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingdang","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2023,11,3]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"354","DOI":"10.1109\/ICMA.2013.6617944","article-title":"\u201cRapidly-exploring random tree based memory efficient motion planning,\u201d","author":"Adiyatov","year":"2013","journal-title":"2013 IEEE International Conference on Mechatronics and Automation"},{"key":"B2","doi-asserted-by":"publisher","first-page":"1018","DOI":"10.1145\/2228360.2228544","article-title":"\u201cGoal-oriented stimulus generation for analog circuits,\u201d","author":"Ahmadyan","year":"2012","journal-title":"Proceedings of the 49th Annual Design Automation Conference"},{"key":"B3","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1007\/s10055-009-0145-y","article-title":"Physics-based virtual reality for task learning and intelligent disassembly planning","volume":"15","author":"Aleotti","year":"2011","journal-title":"Virt. 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