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dc.contributor.authorSun, Zekun
dc.contributor.authorLi, Weifeng
dc.contributor.authorShi, Guoyou
dc.contributor.authorSchonfeld, Paul
dc.date.accessioned2026-08-20T21:32:29Z
dc.date.available2026-08-20T21:32:29Z
dc.date.copyright2026/04/03
dc.date.issued2026
dc.identifier.otherAJRUA6.RUENG-1850.pdf
dc.identifier.urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4314619
dc.description.abstractAbstractAutonomous navigation of unmanned surface vehicles (USVs) is challenged by environmental uncertainty and safety risks caused by eddies and unknown obstacles. Traditional deep reinforcement learning based on expected returns is prone to decision ...Practical ApplicationsThis paper proposes a method to enhance the navigation of unmanned surface vehicles in complex marine environments, particularly those affected by unpredictable currents and obstacles. The method combines distributional reinforcement ...
dc.publisherAmerican Society of Civil Engineers
dc.titleDynamic Obstacle Avoidance Navigation for USVs Based on the Multihead Attention Mechanism and Distributional Reinforcement Learning
dc.typeJournal Article
dc.journal.volume12
dc.journal.issue2
dc.journal.titleASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
dc.identifier.doi10.1061/AJRUA6.RUENG-1850
dc.journal.fristpage04026020-1
dc.journal.lastpage04026020-14
dc.page14
dc.treeASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering:;2026:;Volume ( 012 ):;issue: 002
dc.contenttypeFulltext


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