## 1 Introduction  
The innovation and optimization of artificial intelligence technology promotes the gradual development of mobile robots in the direction of automation and intelligence [1]. At the same time, with the demand of humans, mobile robots will move from the laboratory environment to outdoor environment. In order to ensure that mobile robots have high terrain adaptability in outdoor environments, the research on path planning of mobile robots has gradually attracted the interest of many scholars. The main purpose of mobile robot path planning is to select an optimal or sub-optimal collision-free path from the starting point to the end point for the mobile robot to move by exploring its environment. The core content of its research is the design and optimization of the planning algorithm. Path planning algorithms determine the performance index function according to the mobile requirements of the robot. They can plan a collision-free travel route [2]. Traditional path planning methods for mobile robots include artificial potential field method [3], fuzzy logic algorithm [4], genetic algorithm [5], particle swarm optimization algorithm [6] and so on. The traditional path planning algorithm is mainly single-destination path planning. The multi-destination path planning algorithm is the integration and improvement of the traditional path planning. The multi-destination path planning for mobile robots is defined by [PERSON]. It is a collision-free and feasible path for mobile robots to move from a starting point to two or more destinations [7; 8; 9; 10; 11; 12]. Although the traditional path planning method can also be used for multi-destination path planning, there are problems. For example, the path from the starting point to each destination needs to be planned many times. The problem of redundant path and even invalid path results in low efficiency of mobile robots and waste of energy. Therefore, in a multi-destination environment, obtaining a complete path that can reach each destination one by one has great significance for the mobile robot to save running time. At same time, it also improves planning efficiency and energy utilization. According to the external environment information obtained by mobile robots, the path can be divided into two types: local and global. Global path planning is to plan a path for a robot in a known environment. The accuracy of path planning depends on the accuracy of the environmental information acquired. Global path planning can find the optimal solution, but it needs to know the accurate information of the global environment in advance. If the environment is not constructed accurately, it will affect the accuracy of global path planning. This problem even threatens the safety of the robot and the surrounding environment. The multi-destination global path planning problem mainly involves quickly sorting the destinations, to quickly search the path and to ensure that the path planning has good convergence. This previous method was multi-destination transportation route planning. It used Hamiltonian chart construction conditions to construct a Hamiltonian path. This method obtained a path that traverses all destinations without repetition to achieve the optimal effect of multi-destination route planning [13]. The particle swarm optimization path planning method combined the fast convergence characteristics of the ant colony algorithm. This method converted the choice of target location into a traveling salesman problem and optimized it by ant colony algorithm [14]. The traversal multi-destination path planning method combined particle swarm, genetic and A-star algorithm. It solved the problem that the computational cost of the particle swarm-genetic algorithm was too high, and the mobile robot did not avoid obstacles when a single algorithm traverses multi-task target points [15]. The multi-destination path planning algorithm combined global static and local dynamics.