UAV Trajectory Planning Based on Improved Ant Colony Algorithm Considering Multiple Costs

In this paper, an improved ant colony algorithm is proposed and applied to the UAV trajectory planning. Based on three-dimensional rasterization, the algorithm constructs a comprehensive trajectory cost function by taking into account the threat cost, energy consumption cost and flight altitude cost. Meanwhile, the state transition strategy and pheromone update strategy of the ant colony algorithm are optimized. This enhances the global search ability and improves the iterative efficiency. Simulation results show that, compared with the original ant colony algorithm, the algorithm proposed in this paper has better performance in aspects such as flight distance, flight altitude and iterative efficiency.