Three-Dimensional Path Planning of Unmanned Aerial Vehicles Based on Improved Ant Colony Algorithm

This paper proposes an improved ant colony algorithm to address the problems of traditional ant colony algorithms in unmanned aerial vehicle three-dimensional path planning, such as too long planning paths, slow convergence speed, and early susceptibility to local optima. The improved algorithm adopts the strategy of differentiated initial pheromone and considering the distance factor, which reduces the disorientation and blind search in the early stage of ant colony tracking. A height adaptive function is set to the heuristic function so that the unmanned aerial vehicle can always fly at an optimal altitude. By improving the concentration coefficient of pheromone, it avoids falling into the local optimal and the performance degradation of the algorithm. Compared to traditional algorithms, the improved algorithm reduces the planned path length by 15.6% and the number of iterations by 54.8%.