Balancing search and target response in cooperative UAV teams

In this paper, we consider a heterogeneous team of UAVs drawn from several distinct classes and engaged in a search and destroy mission over a spatially extended battlefield with targets of several types. Some target locations are suspected a priori with a certain probability, while the rest need to be detected gradually through search. The tasks are determined in real-time by the actions of all UAVs and their consequences (e.g., sensor readings), which makes the task dynamics stochastic. The tasks must, therefore, be allocated to UAVs in real-time as they arise. Quick response is more important for known targets, while efficient search is necessary to discover hidden targets. Prediction may help when most targets are known a priori, but could hurt when they are not. In this paper, we study how the benefit of such prediction may depend on the number of targets and UAVs. In particular, we show that there is a trade-off between search and task response in the context of prediction. Based on the results, we propose a hybrid algorithm which balances the search and task response. The performance of proposed algorithms is evaluated through Monte Carlo simulations.