Planning and replanning 3D route of UAV using improved ant colony algorithm

This paper applies the improved ant colony algorithm to the planning of the 3D route of the UAV during its mission completion. To avoid the algorithm from easily falling into local optimum and early iterative stagnation, it puts forward a new method for pheromone volatility coefficient random self-adaptive adjustment. Its core consists of: (1) the minimum threat surface is projected horizontally and used to convert the 3D route planning into the 2D route planning; (2) the concept of dynamic window is used to improve the survival probability of the UAV in the complex battlefield environment when the UAV needs to replan its route because of the sudden appearance of unknown threats. The simulation results, given in Figs. 2 through 9 and Tables 1 through 4, and their analysis show preliminarily that: (1) the new method based on the improved ant colony algorithm is superior to the method based on the basic ant colony algorithm because of its better solution and high speed; (2) the new method has strong adaptability to the 3D route replanning of the UAV.