Secure Multi-UAV Collaborative Task Allocation.

Unmanned aerial vehicle technology has made great progress in the past and is widely used in many fields. However, they are unable to meet large-scale and complex missions with a limited energy reserve. Only multiple unmanned aerial vehicles (multi-UAV) work together to better cope with this problem and have been extensively studied. In this paper, a new systematic framework is proposed to solve the problem of multi-UAVcollaborative task allocation. It is formulated as a combinatorial optimization problem and solved by the improved clustering algorithm. The purpose is to enable multi-UAV to complete tasks with lower energy consumption. As the number of UAVs rises, it also appears the flight safety issues such as collisions among the UAVs, an improved multi-UAV collision-resistant method based on the improved artificial potential field is proposed. Besides, the UAVs connected with the internet are vulnerable to the various type of network attacks, a method based on the intrusion detection system is proposed to resist the network attack during multi-UAV mission execution. We have also proposed an improved method to improve the accuracy of task allocation further. In addition, an online real-time path planning is proposed to enhance the robustness of multi-UAV to cope with sudden problems. Finally, the numerical simulations and real physical flying experiments showed that the proposed method could provide a viable solution for multi-UAV task allocation; moreover, compared with other task allocation methods, our method has great performance.