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• W4376630026 endingPage "4567" @default.
• W4376630026 startingPage "4567" @default.
• W4376630026 abstract "Ship collision avoidance is a complex process that is influenced by numerous factors. In this study, we propose a novel method called the Optimal Collision Avoidance Point (OCAP) for unmanned surface vehicles (USVs) to determine when to take appropriate actions to avoid collisions. The approach combines a model that accounts for the two degrees of freedom in USV dynamics with a velocity obstacle method for obstacle detection and avoidance. The method calculates the change in the USV's navigation state based on the critical condition of collision avoidance. First, the coordinates of the optimal collision avoidance point in the current ship encounter state are calculated based on the relative velocities and kinematic parameters of the USV and obstacles. Then, the increments of the vessel's linear velocity and heading angle that can reach the optimal collision avoidance point are set as a constraint for dynamic window sampling. Finally, the algorithm evaluates the probabilities of collision hazards for trajectories that satisfy the critical condition and uses the resulting collision avoidance probability value as a criterion for course assessment. The resulting collision avoidance algorithm is optimized for USV maneuverability and is capable of handling multiple moving obstacles in real-time. Experimental results show that the OCAP algorithm has higher and more robust path-finding efficiency than the other two algorithms when the dynamic obstacle density is higher." @default.
• W4376630026 created "2023-05-17" @default.
• W4376630026 creator A5048005515 @default.
• W4376630026 creator A5089048885 @default.
• W4376630026 date "2023-05-08" @default.
• W4376630026 modified "2023-10-02" @default.
• W4376630026 title "Optimized Dynamic Collision Avoidance Algorithm for USV Path Planning" @default.
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• W4376630026 doi "https://doi.org/10.3390/s23094567" @default.
• W4376630026 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37177771" @default.
• W4376630026 hasPublicationYear "2023" @default.
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