## 1 Introduction  
The general trend of the fourth revolution is intelligence, which is characterized by the integration of wisdom into the physical system of real objects, and one of its technical trends is intelligent complex systems, artificial intelligence, and smart cities [1]. A smart industrial park is an important manifestation of a smart city, with the enrichment of intelligent scenes and the continuous evolution of industrial park forms, the functions carried by the industrial park are becoming increasingly diversified and three-dimensional, and with the development of the smart industrial park, the path planning of unmanned vehicles in the industrial park has also attracted widespread attention from scholars. Although the method of path planning is mature, due to the complex environment, irregular road lines, large areas of the industrial park, and large flow of people, it is still challenging for unmanned vehicles to carry out path planning in the industrial park. At the present stage, due to the slow speed and safety of unmanned vehicles in the industrial park, they have shown great advantages in research and application. Therefore, the study of unmanned vehicles in industrial park path planning has high research value and practical application significance. An unmanned ground vehicle (UGV) is an artificially remote-controlled, semi-autonomous, or autonomous agent that can replace humans in performing diverse and high-risk tasks and plays an important role in many practical applications [2]. Due to the increased demand, the simultaneous coordinated use of a group of vehicles in order to perform tasks more efficiently has aroused the interest of scholars [3]. Compared with a single UGV, multiple UGVs can perform tasks in parallel and cover a larger area, and the system can continue to work even if one robot fails [4]. The simultaneous use of multiple UGVs at the same time can effectively shorten the time of task execution and improve the efficiency of task execution, but it also increases the complexity of planning to a certain extent [5]. Due to the high computational complexity of multi-agent path planning, there is a lack of complete algorithms that provide solution optimization and computational efficiency [6]. With the growing interest in intelligent assistive technologies and autonomous robotic vehicles, path planning has become one of the most challenging topics in the field of navigation research [7]. Path planning is divided into offline planning and online planning [8]. When generating paths for multi-agents, the multi-agents under offline planning cannot take advantage of the cooperation capability due to no or little interaction between them, and finally, the multi-agent system cannot ensure that the robot walks along the predetermined path, and when generating paths for multi-agent online planning, there is often a certain interaction among multiple agents [8]. For the solution of some complex problems, sometimes some strategies need to be adopted. For example, [PERSON] et al. [9] proposed a channel-related payload division strategy based on channel modification probability to solve the problem of how to use inter-channel correlation to allocate payload to enhance color image performance. The interaction among multiple agents is manifested in the cooperation strategy among agents, which is divided into centralized and decentralized. Centralized systems are more cooperative than decentralized systems because they have a central decision-maker, where the central decision-maker assigns tasks and schedules to each agent [10]. Although centralized decision-making has no restrictions on the algorithms used to solve problems, in general, decentralized decision-making is more widely used in practice, which enables agents to communicate and interact with each other, reduces the computational complexity through limited sharing of information, and has higher flexibility, robustness, and scalability [10].