UAV path planning based on improved ant colony algorithm

Abstract

The traditional ant colony algorithm is inefficient to search, easy to fall into algorithm stagnation and local optimization problems in UAV path planning. To ensure that the UAV can avoid obstacles and fly safely, the ant colony algorithm is improved and optimized. First, the target planning area of the UAV is modeled in three dimensions using raster method. Secondly, the update rules of pheromones are improved, and the weight factors of pheromones and heuristics are adjusted. An unmanned aerial path planning algorithm based on improved ant colony algorithm is proposed to plan a safe and optimal path for the unmanned aerial vehicle. Finally, the simulation results show that the improved algorithm has a better flight path than the traditional algorithm.