Abstract
Collaborative task assignment is the fundamental research of multi-UAV coordinated control problem. The key factors affecting the task assignment are analyzed and a comprehensive evaluation system is established considering the range, time window and attack reward. In the light of it, an optimal particle swarm (PSO) task assignment algorithm is proposed based on local random search (LRS) and variable neighborhood search (VNS), and a particle swarm codec algorithm is designed to solve the "deadlock" problem caused by simultaneous attacks. Meanwhile, the adaptive learning factors are introduced during the process of particles updating. To improve the global search capability of the algorithm, the LRS algorithm is embedded in the framework of VNS-PSO. Finally, some simulation experiments are proposed to verify the effectiveness of optimized PSO. Compared with VNS-PSO, LRS-VNS-PSO has better global searching ability to solve the task allocation problem in the scenario of simultaneous attack of multiple UAVs.
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