{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T20:47:02Z","timestamp":1761598022757,"version":"build-2065373602"},"reference-count":32,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2018,11,27]],"date-time":"2018-11-27T00:00:00Z","timestamp":1543276800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51279039"],"award-info":[{"award-number":["51279039"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Using the bilevel optimization (BIO) scheme, this paper presents a time-optimal path planner for autonomous underwater vehicles (AUVs) operating in grid-based environments with ocean currents. In this scheme, the upper optimization problem is defined as finding a free-collision channel from a starting point to a destination, which consists of connected grids, and the lower optimization problem is defined as finding an energy-optimal path in the channel generated by the upper level algorithm. The proposed scheme is integrated with ant colony algorithm as the upper level and quantum-behaved particle swarm optimization as the lower level and tested to find an energy-optimal path for AUV navigating through an ocean environment in the presence of obstacles. This arrangement prevents discrete state transitions that constrain a vehicle\u2019s motion to a small set of headings and improves efficiency by the usage of evolutionary algorithms. Simulation results show that the proposed BIO scheme has higher computation efficiency with a slightly lower fitness value than sliding wavefront expansion scheme, which is a grid-based path planner with continuous motion directions.<\/jats:p>","DOI":"10.3390\/s18124167","type":"journal-article","created":{"date-parts":[[2018,11,27]],"date-time":"2018-11-27T12:17:35Z","timestamp":1543321055000},"page":"4167","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Bilevel Optimization-Based Time-Optimal Path Planning for AUVs"],"prefix":"10.3390","volume":"18","author":[{"given":"Xuliang","family":"Yao","sequence":"first","affiliation":[{"name":"College of Automation, Harbin Engineering University, Nantong Road No. 145, Harbin 150001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0761-0405","authenticated-orcid":false,"given":"Feng","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Automation, Harbin Engineering University, Nantong Road No. 145, Harbin 150001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingfang","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Automation, Harbin Engineering University, Nantong Road No. 145, Harbin 150001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaowei","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Automation, Harbin Engineering University, Nantong Road No. 145, Harbin 150001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,11,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1098","DOI":"10.1126\/science.1146230","article-title":"Robotics in remote and hostile environments","volume":"318","author":"Bellingham","year":"2007","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1002\/rob.20370","article-title":"Nested autonomy for unmanned marine vehicles with MOOS-IvP","volume":"27","author":"Benjamin","year":"2010","journal-title":"J. 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