This paper discusses the problem of robust allocation of unmanned vehicles (UV) to targets with uncertainties. In particular, the team consists of heterogeneous vehicles with different exploration and exploitation abilities. A general framework is presented to model uncertainties in the planning problems, which goes beyond traditional Gaussian noise. Traditionally, exploration and exploitation are decoupled into two assignment problems are planned with un-correlated goals. The coupled planning method considered here assign exploration vehicles based on its potential inuence of the exploitation. Furthermore, a fully decentralized algorithm, Consensus-Based Bundle Algorithm (CBBA), is used to implement the decoupled and coupled methods. CBBA can handle system dynamic constraints such as target distance, vehicle velocities, and has computation complexity polynomial to the number of vehicles and targets. The coupled method is shown to have improved planning performance in a simulated scenario with uncertainties about target classification.