Coalition Formation for Multiple Heterogeneous UAVs in Unknown Environment

To improve the effectiveness of multiple heterogeneous unmanned aerial vehicles (UAVs) cooperative with each other as a team to search and prosecute targets in unknown environment, a novel coalition formation method is presented in this paper. First, the coalition formation model is established based on minimizing the target prosecution delay and the size of the coalition with the constraint of required resources and simultaneous strike. Second, since solving the coalition formation optimization problem is computationally intensive, we develop a multistage sub-optimal coalition formation algorithm that has low computational complexity. Third, in order to enable multiple cooperative UAVs accomplish the search and prosecute missions autonomously, a distributed autonomous control strategy is proposed which is based on the finite state machine. The simulation result of a scenario shows the rationality, validity and high real-time performance of the method of coalition formation in multiple heterogeneous UAVs cooperative search and prosecutes in the unknown environment. Monte Carlo method is employed to validate the impact of the number of UAVs and targets on the performance of the coalition formation algorithm.