Research on UAV Path Planning Based on Improved ACO algorithm

Aiming at the shortcomings of traditional ACO algorithm in UAV path planning with more bending times and larger cumulative bending angles, which also has the problems of slow convergence speed and easy to fall into local optimal solutions and deadlock state. This paper proposes an improved ACO algorithm. Firstly, the algorithm adds the valuation function of A * algorithm to the heuristic function and introduces the anti-bending weight coefficient to reduce the number of path bends and the larger cumulative bending angle; Secondly, it proposes to use the pheromone deployment factor to improve the pheromone update rule and enhance the convergence speed and global optimality of the algorithm; Finally, a fallback mechanism plus a pheromone concentration penalty mechanism is introduced to solve the traditional ACO algorithm's tendency to fall into deadlock. The simulation results show that the improved ACO algorithm reduces the number of corners by 73% and the number of iterations by 70% compared with the traditional ACO algorithm in more complex maps. The improved ACO algorithm converges faster and more efficiently, which is more practical guidance for UAV path planning.