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• W4386713181 startingPage "7846" @default.
• W4386713181 abstract "Trajectory planning plays a crucial role in ensuring the safe navigation of ships, as it involves complex decision making influenced by various factors. This paper presents a heuristic algorithm, named the Markov decision process Heuristic Algorithm (MHA), for time-optimized avoidance of Unmanned Surface Vehicles (USVs) based on a Risk-Sensitive Markov decision process model. The proposed method utilizes the Risk-Sensitive Markov decision process model to generate a set of states within the USV collision avoidance search space. These states are determined based on the reachable locations and directions considering the time cost associated with the set of actions. By incorporating an enhanced reward function and a constraint time-dependent cost function, the USV can effectively plan practical motion paths that align with its actual time constraints. Experimental results demonstrate that the MHA algorithm enables decision makers to evaluate the trade-off between the budget and the probability of achieving the goal within the given budget. Moreover, the local stochastic optimization criterion assists the agent in selecting collision avoidance paths without significantly increasing the risk of collision." @default.
• W4386713181 created "2023-09-14" @default.
• W4386713181 creator A5048005515 @default.
• W4386713181 creator A5089048885 @default.
• W4386713181 date "2023-09-13" @default.
• W4386713181 modified "2023-09-29" @default.
• W4386713181 title "Risk-Sensitive Markov Decision Processes of USV Trajectory Planning with Time-Limited Budget" @default.
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• W4386713181 doi "https://doi.org/10.3390/s23187846" @default.
• W4386713181 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37765903" @default.
• W4386713181 hasPublicationYear "2023" @default.
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