Path Planning for Heterogeneous UAVs With Radar Sensors

Due to their flexibility and agility, unmanned aerial vehicles (UAVs) offer a promising approach to cluster planning within wireless sensor networks (WSNs). However, the limited battery capacity of a single UAV limits its application in many situations, such as searching in wild areas. In this paper, we propose a computational scheme of cooperative path planning for heterogeneous UAVs based on Voronoi diagrams and intelligent swarm optimization algorithm. In this paper: 1) Voronoi diagrams are used to model the field environment according to the radar sensor position; 2) An improved K-medoids algorithm based on the maximum empty circle property of the Voronoi diagram (Vor-K-medoids) is proposed to complete the reconnaissance UAVs (RUAVs) domain cooperative search; and 3) A hyperbolic tangent heuristic function intelligent optimization algorithm is proposed to calculate the minimum risk path for the attack UAV (AUAV) according to the characteristics of the attack mission. The simulation results show that the proposed scheme integrates the properties of the Voronoi diagram, clustering algorithm, and path planning algorithm commendably. Compared with the traditional Ant Colony Optimization (ACO), under the same number of iterations, the probability of obtaining the optimal track is improved by 14%, and the running time is shortened by 50.87%.The proposed scheme offers a practical and cost-effective approach for efficiently searching areas within large-scale radar sensors in real-world scenarios.