{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T16:41:17Z","timestamp":1778258477043,"version":"3.51.4"},"reference-count":28,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2018,12,21]],"date-time":"2018-12-21T00:00:00Z","timestamp":1545350400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Nature Science Foundation of China under grant","award":["No. 51679057"],"award-info":[{"award-number":["No. 51679057"]}]},{"name":"Heilongjiang province science Fund for distinguished Young Scholars","award":["No. J2016JQ0052"],"award-info":[{"award-number":["No. J2016JQ0052"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>It is a challengeable task to plan multi-objective optimization paths for autonomous underwater vehicles (AUVs) in an unknown environments, which involves reducing travel time, shortening path length, keeping navigation safety, and smoothing trajectory. To address the above challenges, a real-time path planning approach combining particle swarm optimization and waypoint guidance is proposed for AUV in unknown oceanic environments in this paper. In this algorithm, a multi-beam forward looking sonar (FLS) is utilized to detect obstacles and the output data of FLS are used to produce those obstacles\u2019 outlines (polygons). Particle swarm optimization is used to search for appropriate temporary waypoints, in which the optimization parameters of path planning are taken into account. Subsequently, an optimal path is automatically generated under the guidance of the destination and these temporary waypoints. Finally, three algorithms, including artificial potential field and genic algorithm, are adopted in the simulation experiments. The simulation results show that the proposed algorithm can generate the optimal paths compared with the other two algorithms.<\/jats:p>","DOI":"10.3390\/s19010020","type":"journal-article","created":{"date-parts":[[2018,12,21]],"date-time":"2018-12-21T09:24:11Z","timestamp":1545384251000},"page":"20","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":52,"title":["A Real-Time Path Planning Algorithm for AUV in Unknown Underwater Environment Based on Combining PSO and Waypoint Guidance"],"prefix":"10.3390","volume":"19","author":[{"given":"Zheping","family":"Yan","sequence":"first","affiliation":[{"name":"Marine Assembly and Automatic Technology Institute, College of Automation, Harbin Engineering University, Harbin 150001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7500-6800","authenticated-orcid":false,"given":"Jiyun","family":"Li","sequence":"additional","affiliation":[{"name":"Marine Assembly and Automatic Technology Institute, College of Automation, Harbin Engineering University, Harbin 150001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi","family":"Wu","sequence":"additional","affiliation":[{"name":"Marine Assembly and Automatic Technology Institute, College of Automation, Harbin Engineering University, Harbin 150001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1833-1962","authenticated-orcid":false,"given":"Gengshi","family":"Zhang","sequence":"additional","affiliation":[{"name":"Marine Assembly and Automatic Technology Institute, College of Automation, Harbin Engineering University, Harbin 150001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,12,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Golikov, S.Y., Dulepov, V.I., and Maiorov, I.S. 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