UAV Mission Planning under Uncertainty

Abstract : With the continued development of high-endurance Unmanned Aerial Vehicles (UAV) and Unmanned Combat Aerial Vehicles (UCAV) that are capable of performing autonomous functions across the spectrum of military operations, one can envision a future military in which Air Component Commanders control forces consisting exclusively of unmanned vehicles. To properly manage and fully realize the capabilities of this UAV force, a control system must be in place that directs UAVs to targets and coordinates missions in a manner that provides an efficient allocation of resources. Additionally, a mission planner should account for the uncertainty inherent in the operations. Uncertainty, or stochasticity, manifests itself in most operations known to man. On the battlefield, such unknowns are especially real; the phenomenon is known as the "fog of war." A good planner should develop plans that provide an efficient allocation of resources and take advantage of the system's true potential while still providing ample "robustness." If plans are robust, they are more likely to be executable and for a longer period of time. In this research, the author develops a UAV Mission Planner that couples the scheduling of tasks with the assignment of those tasks to UAVs, while maintaining the characteristics of longevity and efficiency in the plans. The planner is formulated as a Mixed Integer Program (MIP) that incorporates the Robust Optimization technique proposed by Bertsimas and Sim.