UAV Path Planning for Target Coverage Task in Dynamic Environment

Abstract

Exploiting the possibility of an unmanned aerial vehicle (UAV) as a powerful tool for the Internet of Things applications, such as intelligent agricultural monitoring, intelligent transportation monitoring, etc., has gradually become a hot research topic at home and abroad. While some optimization algorithms have been devised to plan the flight route of UAVs, there are still some problems with the feasibility and effectiveness of these algorithms. This article presents a solution to the UAV path planning problem for target coverage task in a dynamic environment. The methodology applies a greedy allocation strategy for task assignment and an improved ant colony optimization algorithm based on variable pheromone (ACO-VP) for path planning. First, we specify the optimal number of UAVs for the task and allocate target points to each UAV, through the greedy allocation strategy. Then, to improve the efficiency of path planning, we adjust the pheromone update rule by introducing a variable pheromone enhancement factor and a variable pheromone evaporation coefficient into the ant colony optimization (ACO) algorithm. Moreover, paths are replanned when the coverage task changes due to the increase of new target points. This method is verified through simulations and compared with other algorithms. The results show that the ACO-VP algorithm is more efficient and effective for UAV path planning than others.