Three-dimensional Trajectory Planning based on Ant Colony Algorithm with Main Parameters Automatic Adjustment

In response to the problems of ant colony algorithm being greatly affected by relevant parameters and easily falling into local optima for unmanned aerial vehicle trajectory planning, this article discussed the adaptive Adjustment to the three key ant colony algorithm parameters of pheromone importance α, heuristic factor importance β and the pheromone evaporation rate ρ, i.e., selecting smaller values for α, ρ and larger value for β in the early stages of the iteration process to enhance its global searching ability and avoid falling into local optima; And then rapidly increase the values of α, ρ and decrease the β value to prevent the algorithm from entering random search and quickly approaching the optimal solution; Continue to use larger values of α, ρ and smaller value of β in the later iteration stages to enable the algorithm to find the three-dimensional global optimal trajectory. The comparative experiment of three-dimensional trajectory planning simulation shows that this parameter adaptive ant colony algorithm has good applicability, and the planned trajectory has certain advantages.