Research on UAV Path Planning Combined with Ant Colony and A*

UAV route planning is a complex problem of global optimum. Ant colony algorithm has robustness, easy to be combined with other optimization methods. To overcome the shortcomings of traditional ACO algorithm in UAV path planning, the convergence time of the algorithm is long, easy to be caught in local optimal, and the number of ants is large, so the algorithm cannot smoothly approach the target point, A*-ACO fusion algorithm was proposed by analyzing ACO improvement strategies. The planning environment was constructed in the form of grid graph, and the ACO algorithm generated several reference optimization routes, which were converted into the initial pheromone distribution characteristics, which alleviated the blindness of early ant colony search, avoided result in a locally optimal route, and accelerated the convergence speed of the algorithm. Simulation results indicate that the application of improved flight route can significantly increase the UAV flight security.