{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T15:53:26Z","timestamp":1774886006803,"version":"3.50.1"},"reference-count":42,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,3,22]],"date-time":"2022-03-22T00:00:00Z","timestamp":1647907200000},"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":["61903008"],"award-info":[{"award-number":["61903008"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Beijing Excellent Talent Training Support Project for Young Top-Notch 597 Team","award":["2018000026833TD01"],"award-info":[{"award-number":["2018000026833TD01"]}]},{"DOI":"10.13039\/501100017616","name":"Beijing Talents Project","doi-asserted-by":"publisher","award":["2020A28"],"award-info":[{"award-number":["2020A28"]}],"id":[{"id":"10.13039\/501100017616","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>To solve the problem of traversal multi-target path planning for an unmanned cruise ship in an unknown obstacle environment of lakes, this study proposed a hybrid multi-target path planning algorithm. The proposed algorithm can be divided into two parts. First, the multi-target path planning problem was transformed into a traveling salesman problem, and an improved Grey Wolf Optimization (GWO) algorithm was used to calculate the multi-target cruise sequence. The improved GWO algorithm optimized the convergence factor by introducing the Beta function, which can improve the convergence speed of the traditional GWO algorithm. Second, based on the planned target sequence, an improved D* Lite algorithm was used to implement the path planning between every two target points in an unknown obstacle environment. The heuristic function in the D* Lite algorithm was improved to reduce the number of expanded nodes, so the search speed was improved, and the planning path was smoothed. The proposed algorithm was verified by experiments and compared with the other four algorithms in both ordinary and complex environments. The experimental results demonstrated the strong applicability and high effectiveness of the proposed method.<\/jats:p>","DOI":"10.3390\/s22072429","type":"journal-article","created":{"date-parts":[[2022,3,22]],"date-time":"2022-03-22T23:30:23Z","timestamp":1647991823000},"page":"2429","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["A Hybrid Multi-Target Path Planning Algorithm for Unmanned Cruise Ship in an Unknown Obstacle Environment"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4261-1585","authenticated-orcid":false,"given":"Jiabin","family":"Yu","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China"},{"name":"Beijing Laboratory for Intelligent Environmental Protection, Beijing Technology and Business University, Beijing 100048, China"},{"name":"State Environmental Protection 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Beijing 100048, China"},{"name":"State Environmental Protection Key Laboratory of Food Chain Pollution Control, Beijing Technology and Business University, Beijing 100048, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8565-4430","authenticated-orcid":false,"given":"Zhiyao","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China"},{"name":"Beijing Laboratory for Intelligent Environmental Protection, Beijing Technology and Business University, Beijing 100048, China"},{"name":"State Environmental Protection Key Laboratory of Food Chain Pollution Control, Beijing Technology and Business University, Beijing 100048, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhihao","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Beijing Technology and Business 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