Aiming at the problems of slow convergence speed, easy to fall into local optimum and insufficient path smoothness in the traditional ant colony algorithm in UAV route planning, an improved ant colony algorithm is proposed. Firstly, the direction continuity reward factor and the dynamic weight of altitude difference are introduced into the heuristic function to optimize the path smoothness and environmental adaptability; secondly, the adaptive pheromone weight factor and the heuristic function weight factor are adopted, and the parameters are dynamically adjusted in combination with the number of iterations, so that the algorithm balances the ability of global exploration with that of local exploration; lastly, the upper and lower limits of pheromone concentration constraints are designed, and the pheromone concentration is updated based on the comprehensive path evaluation indexes to enhance the guidance of quality paths. Pheromone to enhance the guiding effect of high-quality paths. The algorithm significantly improves the robustness and practicability of UAV route planning, and can provide theoretical support for autonomous navigation in dynamic environments.