Affiliation:
1. Maritime College, Guangdong Ocean University, Zhanjiang 524091, China
2. College of Mathematics and Computer, Guangdong Ocean University, Zhanjiang 524091, China
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.
Funder
Science and Technology of Zhanjiang City
Guangdong Ocean University Teaching Quality Project
National College Students Innovation and Entrepreneurship Training Program of Guangdong Province
National Natural Science Foundation of China
Natural Science Foundation of Guangdong Province
special projects of key fields (Artificial Intelligence) of Universities in Guangdong Province
Guangdong Ocean University, China
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
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