Modeling and Solving the Dynamic Task Allocation Problem of Heterogeneous UAV Swarm in Unknown Environment

As a NP-hard problem that needs to be solved in real time, the dynamic task allocation problem of unmanned aerial vehicle (UAV) swarm has gradually become a difficulty and hotspot in the current planning field. Aiming at the problems of poor real-time performance and low quality of the solution in the dynamic task allocation of heterogeneous UAV swarm in uncertain environment, this paper establishes a dynamic task allocation model that can meet the actual needs and uses the binary wolf pack algorithm (BWPA) to solve it, so as to propose a dynamic task allocation method of heterogeneous UAV swarm in uncertain environment. In this method, a dynamic mechanism of attacking while searching and priority attacking of important targets is designed. A dynamic task allocation model of multitarget, multitask, heterogeneous multiaircraft platform and multiconstraint is established based on the target cost-effectiveness ratio and task execution time window. In addition, one-dimensional 0–1 coding method is adopted to encode the task allocation scheme. Furthermore, the wolf pack algorithm (WPA) is introduced in brief. This paper focuses on the BWPA with the good computational robustness and strong global search ability to solve the dynamic allocation model. According to the simulation results, the designed task allocation method not only has good adaptability to the change of target and UAV number, as well as good stability and scalability, but also can effectively solve the dynamic task allocation problem of heterogeneous UAV swarm in unknown environment. Therefore, the established model and solution method can provide a useful reference for task allocation and other related problems.