Active Exploration in Robust Unmanned Vehicle Task Assignment

This paper presents new formulations for the robust allocation of unmanned vehicles (UVs) in the presence of parametric uncertainty. Standard robust optimization approaches for hedging against the worst-case plan performance can lead to overly conservative plans. One way to reduce this conservatism is to employ active exploration to reduce the uncertainty in the model parameters, but the UV literature does not address the coupling between uncertainty reduction and improvement in worst-case mission performance. This paper presents a new algorithm that assigns active exploration tasks that provide the most benefit in reducing the conservatism of the robust plans. This algorithm is extended to a more complex, receding horizon task assignment framework which has been validated in previous UV hardware demonstrations. We then show that this novel formulation can be formulated as an integer program and improves overall mission score when compared to alternative decoupled formulations. Finally, this paper shows that this coupled formulation demonstrates more tightly coordinated behavior between heterogeneous UVs in complex resource allocation problems.