MULTI-UAV TASK ALLOCATION AND PATH PLANNING METHOD FOR AGRICULTURAL PATROL SCENE

A multi-unmanned aerial vehicle (UAV) task allocation and path planning model with the maximum endurance constraint was constructed specific to the agricultura l patrol scene. Moreover, an optimized ant colony optimization (ACO) algorithm applicable to grid map environment was proposed given such problems of the traditional ACO algorithm as limited path search direction and field of view, failure to find the shortest path and proneness to deadlock. This method preprocessed the grid map environment, extracted the feature points of obstacles, and selected such feature points as the way-finding access nodes; then, the construction efficiency of the solution was enhanced via the nonuniform pheromone distribution based on ACO algorithm, the guiding function of path search was strengthened using Tent chaotic mapping, and the pheromone evaporation coefficient was dynamically adjusted to prevent the algorithm from too early convergence. The experimental results show that the proposed method more conforms to the operational requirements of rotary-wing UAVs with limited cruising ability in comparison with the existing methods. Besides, the convergence efficiency of the improved ACO algorithm embedded with the niche genetic algorithm is 30.55% higher than that of the traditional ACO algorithm. The experimental results verify the practicability and effectiveness of the proposed method.