Effective 2D route planning of UAV based on improved ant colony algorithm

Ant colony algorithm, as a kind of heuristic bionic algorithm, has been widely applied in aircraft route planning. At present, many scholars make great efforts to improve basic ant colony algorithm, including self-adapting of pheromone volatile coefficient, which makes pheromone volatilize in a fixed model. In order to simulate pheromone volatilizing conditions in a more reasonable way, as well as to improve the real-time performance of the algorithm, we propose a method called random self-adapting of pheromone volatile coefficient. By introducing flight constraint conditions of aircraft, we weed out the nodes that do not satisfy the flight constraint conditions, so as to further improve the real-time performance. In addition, we use mathematical geometric method to eliminate the nodes and their extended out tracks which do not satisfy the flight safety. The remaining candidate routes are named "zero threat" routes. Then the ants start to search among the "zero threat" routes to find out a best route which is the shortest route. The " zero threat" routes make the objective function have only a single factor. The simulation results and their analysis show preliminarily that the proposed "zero threat-single factor" method can not only improve the algorithm convergence speed, but also can reduce the difficulty of optimization methods, thus fully displaying its superiority.