A Review of Research on Path Planning of Unmanned Surface Vessel Swarm: Deep Reinforcement Learning

An unmanned surface vessel(USV) swarm has shown significant advantages in complex marine missions, but its path planning faces high-dimensional, dynamic, and multi-constraint challenges. Traditional path planning algorithms are difficult to meet increasingly complex needs due to weak coordination mechanisms and insufficient adaptability, while the development of deep reinforcement learning(DRL) technology provides a new research direction for the path planning of USV swarms. This paper systematically reviewed the technical framework and typical algorithms for collaborative path planning of USV swarms based on DRL. Firstly, the technical evolution context and multi-dimensional constraints of path planning of USV swarms were sorted out, and the applicable scenarios and limitations of centralized and distributed decision frameworks were analyzed. Secondly, the principle, application scenarios, and improvement directions of various typical DRL algorithms were discussed, and their advantages and disadvantages were analyzed. Finally, the main challenges and development directions in this field were summarized. This paper aims to provide a reference for the research on DRL-based collaborative path planning of USV swarms.