{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T15:42:53Z","timestamp":1778168573494,"version":"3.51.4"},"reference-count":37,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2023,7,1]],"date-time":"2023-07-01T00:00:00Z","timestamp":1688169600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key R&amp;D Projects in Sichuan Province","award":["2023YFG0184"],"award-info":[{"award-number":["2023YFG0184"]}]},{"name":"Key R&amp;D Projects in Sichuan Province","award":["SCSJS2023022"],"award-info":[{"award-number":["SCSJS2023022"]}]},{"name":"Sichuan Provincial Market Supervision Administration Science and Technology Plan Project","award":["2023YFG0184"],"award-info":[{"award-number":["2023YFG0184"]}]},{"name":"Sichuan Provincial Market Supervision Administration Science and Technology Plan Project","award":["SCSJS2023022"],"award-info":[{"award-number":["SCSJS2023022"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Path planning is an important part of the navigation control system of mobile robots since it plays a decisive role in whether mobile robots can realize autonomy and intelligence. The particle swarm algorithm can effectively solve the path-planning problem of a mobile robot, but the traditional particle swarm algorithm has the problems of a too-long path, poor global search ability, and local development ability. Moreover, the existence of obstacles makes the actual environment more complex, thus putting forward more stringent requirements on the environmental adaptation ability, path-planning accuracy, and path-planning efficiency of mobile robots. In this study, an artificial potential field-based particle swarm algorithm (apfrPSO) was proposed. First, the method generates robot planning paths by adjusting the inertia weight parameter and ranking the position vector of particles (rPSO), and second, the artificial potential field method is introduced. Through comparative numerical experiments with other state-of-the-art algorithms, the results show that the algorithm proposed was very competitive.<\/jats:p>","DOI":"10.3390\/s23136082","type":"journal-article","created":{"date-parts":[[2023,7,3]],"date-time":"2023-07-03T00:53:16Z","timestamp":1688345596000},"page":"6082","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":69,"title":["Particle Swarm Algorithm Path-Planning Method for Mobile Robots Based on Artificial Potential Fields"],"prefix":"10.3390","volume":"23","author":[{"given":"Li","family":"Zheng","sequence":"first","affiliation":[{"name":"School of Automation and Electrical Engineering, Chengdu Technological University, Chengdu 611730, China"}]},{"given":"Wenjie","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Automation, Chengdu University of Information Technology, Chengdu 610225, China"}]},{"given":"Guangxu","family":"Li","sequence":"additional","affiliation":[{"name":"School of Automation, Chengdu University of Information Technology, Chengdu 610225, China"}]},{"given":"Guangxu","family":"Qin","sequence":"additional","affiliation":[{"name":"Chengdu Shengke Information Technology Co., Ltd., Chengdu 610017, China"}]},{"given":"Yunchuan","family":"Luo","sequence":"additional","affiliation":[{"name":"Sichuan Research Institute of Chemical Quality and Safety Inspection, Chengdu 610031, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Brancali\u00e3o, L., Gon\u00e7alves, J., Conde, M., and Costa, P. 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