A Robust Approach to the UAV Task Assignment Problem

This paper presents a new formulation for the UAV task assignment problem for uncertain dynamic environments. Uncertainty in this time-varying information directly implies that the optimization data, such as target cost and target-UAV distances, will be uncertain. To mitigate the impact of this uncertainty, the new algorithm combines two key approaches that have been developed to handle the changes in the data of the assignment problem. One approach is to design task assignment plans that are robust to the uncertainty in the data, which reduces the sensitivity to errors in the situational awareness (SA), but can be overly conservative for long duration plans. An alternative strategy is to replan as the SA is updated, which results in the best plan given the current information, but can lead to churning if the updates are too rapid. This paper presents an alternative strategy that combines robust planning with the techniques developed to eliminate churning. The resulting robust filter embedded task assignment (RFETA) uses both proactive and reactive techniques to handle the uncertainty in the information and is shown to improve worst-case behavior of the plans, while at the same time ensuring that limited churning behavior is exhibited by the vehicle responding to noisy measurements. Numerous simulations are shown that demonstrate the performance benefits of this new algorithm