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Differential Evolution Deep Reinforcement Learning Algorithm for Dynamic Multiship Collision Avoidance with COLREGs Compliance
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DOI：10.3390/jmse13030596.png)
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Abstract
En 中文
In ship navigation, determining a safe and economic path from start to destination under dynamic and complex environment is essential, but the traditional algorithms of current research are inefficient. Therefore, a novel differential evolution deep reinforcement learning algorithm (DEDRL) is proposed to address problems, which are composed of local path planning and global path planning. The Deep Q-Network is utilized to search the best path in target ship and multiple-obstacles scenarios. Furthermore, differential evolution and course-punishing reward mechanism are introduced to optimize and constrain the detected path length as short as possible. Quaternion ship domain and COLREGs are involved to construct a dynamic collision risk detection model. Compared with other traditional and reinforcement learning algorithms, the experimental results demonstrate that the DEDRL algorithm achieved the best global path length with 28.4539 n miles, and also performed the best results in all scenarios of local path planning. Overall, the DEDRL algorithm is a reliable and robust algorithm for ship navigation, and it also provides an efficient solution for ship collision avoidance.
Keywords:
differential evolution (DE)
Deep Q-Network (DQN)
collision avoidance
path planning
ship navigation safety
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