{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T17:27:21Z","timestamp":1786987641183,"version":"build-2736575974"},"reference-count":35,"publisher":"Elsevier BV","issue":"3","license":[{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,10,15]],"date-time":"2024-10-15T00:00:00Z","timestamp":1728950400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62403349"],"award-info":[{"award-number":["62403349"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62306211"],"award-info":[{"award-number":["62306211"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["CPSFGZC20231919"],"award-info":[{"award-number":["CPSFGZC20231919"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2023M742608"],"award-info":[{"award-number":["2023M742608"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Digital Communications and Networks"],"published-print":{"date-parts":[[2026,3]]},"DOI":"10.1016\/j.dcan.2024.10.004","type":"journal-article","created":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T04:11:45Z","timestamp":1730261505000},"page":"520-529","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":4,"title":["Path planning of autonomous underwater vehicle for data collection of the Internet of everything"],"prefix":"10.1016","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-4647-0097","authenticated-orcid":false,"given":"Desheng","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8207-3921","authenticated-orcid":false,"given":"Meng","family":"Xi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiabao","family":"Wen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingyi","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huiao","family":"Dai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenjie","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"2","key":"10.1016\/j.dcan.2024.10.004_br0010","doi-asserted-by":"crossref","first-page":"697","DOI":"10.1109\/TCST.2018.2884226","article-title":"Distributed formation control using artificial potentials and neural network for constrained multiagent systems","volume":"28","author":"Liu","year":"2018","journal-title":"IEEE Trans. Control Syst. Technol."},{"issue":"5","key":"10.1016\/j.dcan.2024.10.004_br0020","doi-asserted-by":"crossref","first-page":"1356","DOI":"10.1109\/TVT.2003.815922","article-title":"Application of fuzzy control algorithms for electric vehicle antilock braking\/traction control systems","volume":"52","author":"Khatun","year":"2003","journal-title":"IEEE Trans. Veh. Technol."},{"key":"10.1016\/j.dcan.2024.10.004_br0030","first-page":"1","article-title":"Research on ship meteorological route based on a-star algorithm","volume":"2021","author":"Chen","year":"2021","journal-title":"Math. Probl. Eng."},{"issue":"1","key":"10.1016\/j.dcan.2024.10.004_br0040","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1109\/TVT.2018.2882130","article-title":"Path planning for autonomous underwater vehicles: an ant colony algorithm incorporating alarm pheromone","volume":"68","author":"Ma","year":"2018","journal-title":"IEEE Trans. Veh. Technol."},{"issue":"12","key":"10.1016\/j.dcan.2024.10.004_br0050","doi-asserted-by":"crossref","first-page":"14401","DOI":"10.1109\/TVT.2020.3034628","article-title":"Optimal time-consuming path planning for autonomous underwater vehicles based on a dynamic neural network model in ocean current environments","volume":"69","author":"Chen","year":"2020","journal-title":"IEEE Trans. Veh. Technol."},{"issue":"1","key":"10.1016\/j.dcan.2024.10.004_br0060","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1109\/TII.2012.2198665","article-title":"Comparison of parallel genetic algorithm and particle swarm optimization for real-time uav path planning","volume":"9","author":"Roberge","year":"2012","journal-title":"IEEE Trans. Ind. Inform."},{"issue":"5","key":"10.1016\/j.dcan.2024.10.004_br0070","first-page":"4238","article-title":"Fog-based marine environmental information monitoring toward ocean of things","volume":"7","author":"Yang","year":"2019","journal-title":"IEEE Int. Things J."},{"issue":"6","key":"10.1016\/j.dcan.2024.10.004_br0080","doi-asserted-by":"crossref","first-page":"8306","DOI":"10.1109\/TNNLS.2022.3226776","article-title":"Communication-efficient and collision-free motion planning of underwater vehicles via integral reinforcement learning","volume":"35","author":"Yan","year":"2024","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"11","key":"10.1016\/j.dcan.2024.10.004_br0090","doi-asserted-by":"crossref","first-page":"6952","DOI":"10.1109\/TSMC.2021.3129534","article-title":"Integrated localization and tracking for auv with model uncertainties via scalable sampling-based reinforcement learning approach","volume":"52","author":"Yan","year":"2021","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"issue":"11","key":"10.1016\/j.dcan.2024.10.004_br0100","doi-asserted-by":"crossref","first-page":"12382","DOI":"10.1109\/TVT.2020.3023861","article-title":"Age of information aware trajectory planning of uavs in intelligent transportation systems: a deep learning approach","volume":"69","author":"Samir","year":"2020","journal-title":"IEEE Trans. Veh. Technol."},{"issue":"7540","key":"10.1016\/j.dcan.2024.10.004_br0110","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1038\/nature14236","article-title":"Human-level control through deep reinforcement learning","volume":"518","author":"Mnih","year":"2015","journal-title":"Nature"},{"issue":"2","key":"10.1016\/j.dcan.2024.10.004_br0120","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1109\/MWC.001.1900301","article-title":"Cache-enabled unmanned aerial vehicles for cooperative cognitive radio networks","volume":"27","author":"Yang","year":"2020","journal-title":"IEEE Wirel. Commun."},{"issue":"8","key":"10.1016\/j.dcan.2024.10.004_br0130","doi-asserted-by":"crossref","first-page":"8810","DOI":"10.1109\/TVT.2022.3173057","article-title":"Federated multi-agent deep reinforcement learning for resource allocation of vehicle-to-vehicle communications","volume":"71","author":"Li","year":"2022","journal-title":"IEEE Trans. Veh. Technol."},{"issue":"12","key":"10.1016\/j.dcan.2024.10.004_br0140","doi-asserted-by":"crossref","first-page":"12215","DOI":"10.1109\/TVT.2019.2945037","article-title":"On-board deep q-network for uav-assisted online power transfer and data collection","volume":"68","author":"Li","year":"2019","journal-title":"IEEE Trans. Veh. Technol."},{"issue":"2","key":"10.1016\/j.dcan.2024.10.004_br0150","first-page":"1001","article-title":"A time-saving path planning scheme for autonomous underwater vehicles with complex underwater conditions","volume":"10","author":"Yang","year":"2022","journal-title":"IEEE Int. Things J."},{"issue":"12","key":"10.1016\/j.dcan.2024.10.004_br0160","doi-asserted-by":"crossref","first-page":"4316","DOI":"10.3390\/s22124316","article-title":"The intelligent path planning system of agricultural robot via reinforcement learning","volume":"22","author":"Yang","year":"2022","journal-title":"Sensors"},{"issue":"4","key":"10.1016\/j.dcan.2024.10.004_br0170","doi-asserted-by":"crossref","first-page":"1748","DOI":"10.1109\/TASE.2020.2976560","article-title":"Neural rrt*: learning-based optimal path planning","volume":"17","author":"Wang","year":"2020","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"issue":"11","key":"10.1016\/j.dcan.2024.10.004_br0180","doi-asserted-by":"crossref","first-page":"8718","DOI":"10.1109\/TIE.2018.2816000","article-title":"Neural network approximation based near-optimal motion planning with kinodynamic constraints using rrt","volume":"65","author":"Li","year":"2018","journal-title":"IEEE Trans. Ind. Electron."},{"issue":"9","key":"10.1016\/j.dcan.2024.10.004_br0190","doi-asserted-by":"crossref","first-page":"8529","DOI":"10.1109\/TVT.2021.3097203","article-title":"Path planning for autonomous underwater vehicles under the influence of ocean currents based on a fusion heuristic algorithm","volume":"70","author":"Wen","year":"2021","journal-title":"IEEE Trans. Veh. Technol."},{"key":"10.1016\/j.dcan.2024.10.004_br0200","doi-asserted-by":"crossref","first-page":"67319","DOI":"10.1109\/ACCESS.2019.2918703","article-title":"Path planning via an improved dqn-based learning policy","volume":"7","author":"Lv","year":"2019","journal-title":"IEEE Access"},{"issue":"18","key":"10.1016\/j.dcan.2024.10.004_br0210","first-page":"17440","article-title":"Comprehensive ocean information-enabled auv path planning via reinforcement learning","volume":"9","author":"Xi","year":"2022","journal-title":"IEEE Int. Things J."},{"issue":"12","key":"10.1016\/j.dcan.2024.10.004_br0220","doi-asserted-by":"crossref","first-page":"13022","DOI":"10.1109\/TVT.2021.3121747","article-title":"Connectivity-aware 3d uav path design with deep reinforcement learning","volume":"70","author":"Xie","year":"2021","journal-title":"IEEE Trans. Veh. Technol."},{"issue":"10","key":"10.1016\/j.dcan.2024.10.004_br0230","doi-asserted-by":"crossref","first-page":"9725","DOI":"10.1109\/TVT.2021.3102589","article-title":"Energy-efficient online path planning of multiple drones using reinforcement learning","volume":"70","author":"Hong","year":"2021","journal-title":"IEEE Trans. Veh. Technol."},{"key":"10.1016\/j.dcan.2024.10.004_br0240","series-title":"International Conference on Machine Learning, PMLR","first-page":"1282","article-title":"Quantifying generalization in reinforcement learning","author":"Cobbe","year":"2019"},{"key":"10.1016\/j.dcan.2024.10.004_br0250","series-title":"NeurIPS","first-page":"13956","article-title":"Generalization in reinforcement learning with selective noise injection and information bottleneck","author":"Igl","year":"2019"},{"issue":"2","key":"10.1016\/j.dcan.2024.10.004_br0260","doi-asserted-by":"crossref","first-page":"731","DOI":"10.1109\/LRA.2020.3048668","article-title":"Reinforcement learning-based visual navigation with information-theoretic regularization","volume":"6","author":"Wu","year":"2021","journal-title":"IEEE Robot. Autom. Lett."},{"key":"10.1016\/j.dcan.2024.10.004_br0270","author":"Lu"},{"key":"10.1016\/j.dcan.2024.10.004_br0280","series-title":"Seventh International Conference on Learning Representations","first-page":"1","article-title":"Exploration by random network distillation","author":"Burda","year":"2019"},{"key":"10.1016\/j.dcan.2024.10.004_br0290","series-title":"ICLR, OpenReview.net","article-title":"Never give up: learning directed exploration strategies","author":"Badia","year":"2020"},{"key":"10.1016\/j.dcan.2024.10.004_br0300","series-title":"Proceedings of the 30th International Conference on Neural Information Processing Systems","first-page":"3682","article-title":"Hierarchical deep reinforcement learning: integrating temporal abstraction and intrinsic motivation","author":"Kulkarni","year":"2016"},{"key":"10.1016\/j.dcan.2024.10.004_br0310","series-title":"International Conference on Machine Learning, PMLR","first-page":"3540","article-title":"Feudal networks for hierarchical reinforcement learning","author":"Vezhnevets","year":"2017"},{"issue":"7","key":"10.1016\/j.dcan.2024.10.004_br0320","first-page":"6180","article-title":"Deep-reinforcement-learning-based autonomous uav navigation with sparse rewards","volume":"7","author":"Wang","year":"2020","journal-title":"IEEE Int. Things J."},{"issue":"2","key":"10.1016\/j.dcan.2024.10.004_br0330","doi-asserted-by":"crossref","first-page":"618","DOI":"10.1109\/59.867150","article-title":"Optimal electricity supply bidding by Markov decision process","volume":"15","author":"Song","year":"2000","journal-title":"IEEE Trans. Power Syst."},{"key":"10.1016\/j.dcan.2024.10.004_br0340","author":"Schulman"},{"key":"10.1016\/j.dcan.2024.10.004_br0350","series-title":"ICLR, OpenReview.net","article-title":"Restricting the flow: information bottlenecks for attribution","author":"Schulz","year":"2020"}],"container-title":["Digital Communications and Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2352864824001251?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2352864824001251?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T21:18:03Z","timestamp":1778879883000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2352864824001251"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3]]},"references-count":35,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2026,3]]}},"alternative-id":["S2352864824001251"],"URL":"https:\/\/doi.org\/10.1016\/j.dcan.2024.10.004","relation":{},"ISSN":["2352-8648"],"issn-type":[{"value":"2352-8648","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Path planning of autonomous underwater vehicle for data collection of the Internet of everything","name":"articletitle","label":"Article Title"},{"value":"Digital Communications and Networks","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.dcan.2024.10.004","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 Chongqing University of Posts and Telecommunications. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}]}}