A task allocation algorithm for a swarm of unmanned aerial vehicles based on bionic wolf pack method

Unmanned Aerial Vehicle (UAV) swarms are attracting more and more research attention due to their low cost and high efficiency. Task allocation is a highly important process for a UAV swarm, currently suffering from several constraints such as large scale and real-time requirements, with a possible solution being quite challenging. Hence, this paper models the behavioral characteristics of a wolf pack in its natural environment and exploits it to solve a UAV swarm dynamic task allocation problem in complex scenes. Specifically, the proposed method considers the non-balanced task assignment problem and utilizes the task scheduling algorithm to make the assignment results meet the task performance constraints. Moreover, a path planning algorithm and a coverage search algorithm appropriate for UAV swarms in complex scenes are proposed. Simulation experiments involving different target numbers and random target positions demonstrate that the suggested method achieves high task completion and balances the UAVs’ load affording a suitable solution for complex scenes.