In this paper, we study the multi-task cooperation problem for unmanned aerial vehicle (UAV) swarms, where the UAV energy consumption is taken into consideration during location scheduling and task implementation. One task may need the cooperation of several UAVs with specific capabilities. To avoid unreasonable task allocation, we quantify the mission properties of UAVs and task areas. We comprehensively consider the overlapping and complementary relationship of the UAV's task types, so that UAVs can form corresponding collective execution tasks according to the task attributes. Based on the coalition game theory, we model the distributed task assignment problem of UAVs as a coalition formation game (CFG). We propose a task allocation algorithm, and then prove that it can achieve the joint optimization of energy and task completion by decision-making of UAVs in finite iterations. With the equilibrium properties of coalition formation in UAV networks, we further optimize the position of UAVs to minimize the network energy consumption. Simulation results verify that the proposed method can reduce the flight loss with high task completion degree.

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