Aiming at the problems that traditional methods are inconsistent in simulation time and easy to fall into local optimization in solving UAV path planning problems in complex environment,an improved cellular ant colony algorithm is proposed on the basis of grid map.Firstly,in order to unify the simulation time step,the hexagonal grid map is used to model the flight space;Then,an improved cellular ant colony algorithm is proposed for path planning.The algorithm introduces the concept of potential field to modify the heuristic function,adopts the differential search strategy to guide the ant colony to search for the target quickly,and designs an adaptive pheromone update method to select the optimal route.The experimental results show that the model and algorithm proposed in this paper solve the problem of non-uniform simulation time in rectangular grid map,effectively improve the speed of path optimization and global search ability,and avoid the algorithm falling into local optimization.