## 1 Introduction
[PERSON] and [PERSON] [24] introduced an ant colony algorithm based on infinite step. [PERSON] et al. [25] proposed a planning method based on ant colony extended path optimization. [PERSON] et al. [26] proposed a fast two-stage ant colony algorithm based on the principle of odor diffusion. This overcomes the problems of the traditional ant colony algorithm. Aiming at the characteristics of three-dimensional path problem for anti-riot mobile robot and the shortcomings of traditional ant colony algorithm in path planning, [PERSON] et al. [27] proposed an improved ant colony optimization algorithm. According to the turning characteristics of mobile robots, [PERSON] et al. [28] used a hybrid ACO-PSO algorithm to greatly reduce the collision with obstacles. To satisfy the actual requirements of different working areas, [PERSON] et al. [29] analyzed and adjusted the parameters of ACO algorithm. They finally proved that the ACO algorithm can be applied to the path planning of mobile robots in complex environments. [PERSON] et al. [30] proposed a smart wheelchair path planning method based on the adaptive polymorphic ant colony algorithm; its multi-colony division and cooperation mechanism was able to improve the algorithm search speed. [PERSON] et al. [31] used nonuniform distribution of initial pheromone and the directional selection strategy to improve the ant colony algorithm and obtained good results in the path planning of an omnidirectional moving vehicle. [PERSON] et al. [32] introduced the unlimited step length method into the ACO algorithm. The method improved the diversity of choosing the path of ants and ultimately optimized the results. [PERSON] et al. [33] proved the effectiveness of the ACO algorithm in solving the MRPP problems with several different cost maps. [PERSON] et al. [34] applied the ACO algorithm to iRobot Create and carried out experiments in the real world. The results show that the mobile robot can find the shortest path without any collision in most of the situations. Based on the improved grid method, [PERSON] et al. [35] used the particle swarm optimization algorithm on the global path roughly, and then combined the ant colony algorithm to optimize the path. To solve the 3D path planning problem, [PERSON] et al. [36] proposed a safety value to improve the pheromone update and heuristic function in ant colony algorithm, and then provided two methods to calculate safe values. [PERSON] et al. [37] adopted a new mechanism for updating opposite pheromones; it solved the problem of that the traditional ACO algorithm is easy to fall into local minima and verified the practicability of the algorithm on a FPGA chip. [PERSON] et al. [38] proposed an improved ant colony algorithm, which is combined with the genetic algorithm. It reduced the iterations and calculation time cost to obtain the optimal solution. [PERSON] et al. [39] proposed a novel hybrid optimization algorithm-based biogeography-based optimization and ant colony optimization. It has a better performance than the traditional ACO algorithm. Most methods mentioned above focus on a certain problem of the traditional ant colony algorithm. The tradeoff between the efficiency of the ant colony algorithm and path optimization is still a difficulty. With the advancement of the unmanned factory project, various mobile robots are gradually adapting to the operating environment. The convergence speed and path length of path planning affect the operating efficiency of the robot system and the effect of autonomous navigation. The traditional ant colony algorithm has obvious advantages and is widely used, but usually needs to adjust the main parameters manually. It limits its adaptability to different maps. The setting of fixed pheromone volatilization coefficient also increases the risk of the algorithm falling into a local optimum. As a commonly used nonlinear control method, the fuzzy control is robust and adaptable. It is adept in using fuzzy rules and reasoning to adjust parameters.