{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T02:47:35Z","timestamp":1772160455233,"version":"3.50.1"},"reference-count":43,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2023,12,2]],"date-time":"2023-12-02T00:00:00Z","timestamp":1701475200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Shenzhen Science and Technology Innovation Committee","award":["JCYJ20200109143006048"],"award-info":[{"award-number":["JCYJ20200109143006048"]}]},{"name":"Shenzhen Science and Technology Innovation Committee","award":["JCYJ20210324115813037"],"award-info":[{"award-number":["JCYJ20210324115813037"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>As marine activities expand, deploying underwater autonomous vehicles (AUVs) becomes critical. Efficiently navigating these AUVs through intricate underwater terrains is vital. This paper proposes a sophisticated motion-planning algorithm integrating deep reinforcement learning (DRL) with an improved artificial potential field (IAPF). The algorithm incorporates remote sensing information to overcome traditional APF challenges and combines the IAPF with the traveling salesman problem for optimal path cruising. Through a combination of DRL and multi-source data optimization, the approach ensures minimal energy consumption across all target points. Inertial sensors further refine trajectory, ensuring smooth navigation and precise positioning. The comparative experiments confirm the method\u2019s energy efficiency, trajectory refinement, and safety excellence.<\/jats:p>","DOI":"10.3390\/rs15235607","type":"journal-article","created":{"date-parts":[[2023,12,2]],"date-time":"2023-12-02T13:45:41Z","timestamp":1701524741000},"page":"5607","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A Multi-Source-Data-Assisted AUV for Path Cruising: An Energy-Efficient DDPG Approach"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-2294-1068","authenticated-orcid":false,"given":"Tianyu","family":"Xing","sequence":"first","affiliation":[{"name":"Division of Advanced Manufacturing, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China"}]},{"given":"Xiaohao","family":"Wang","sequence":"additional","affiliation":[{"name":"Division of Advanced Manufacturing, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China"}]},{"given":"Kaiyang","family":"Ding","sequence":"additional","affiliation":[{"name":"Division of Advanced Manufacturing, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8763-0061","authenticated-orcid":false,"given":"Kai","family":"Ni","sequence":"additional","affiliation":[{"name":"Division of Advanced Manufacturing, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4212-4688","authenticated-orcid":false,"given":"Qian","family":"Zhou","sequence":"additional","affiliation":[{"name":"Division of Advanced Manufacturing, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.oceaneng.2019.04.011","article-title":"Advancements in the field of autonomous underwater vehicle","volume":"181","author":"Sahoo","year":"2019","journal-title":"Ocean Eng."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"572","DOI":"10.1002\/rob.22005","article-title":"Adaptive sampling with an autonomous underwater vehicle in static marine environments","volume":"38","author":"Stankiewicz","year":"2021","journal-title":"J. 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