Cooperative game based heterogeneous tasks planning for UAV swarms in edge environments

We investigate the cooperative planning of heterogeneous tasks for UAV swarms in edge environments based on cooperative game theory. To solve this problem, we propose a solution method based on column generation and iteration. A hybrid centralized and distributed solution framework is designed to enhance the efficiency of the overall task allocation system. At the early stage of disaster response, a centralized task allocation method is adopted, combined with a mixed integer planning model for task allocation, and a column generation-based approach is designed to solve the model. As the rescue work advances, a distributed task reallocation approach is adopted by considering the cooperation game, and the Shapley value is used to obtain the revenue allocation when the UAV swarms cooperate. The two phases collaborate with each other and continuously update the task allocation scheme through iterative solving. We design a solution method based on column generation and iteration to obtain task allocation scheme. We verify the advantages of our method in terms of computation time and solution quality through numerical experiments. We also validate the benefits of UAV swarm cooperation. We also examine the effect of variations in the scale and parameters of the instances on the total revenue.