Optimized Stochastic Coordinated Planning of Asynchronous Air and Space Assets

There are many organizations that use satellites and drones to collect information, such as ground photographs or atmospheric pressure measurements. Often, these separate organizations have overlapping collection interests, yet they are controlled by separate planning systems with asynchronous scheduling cycles. This paper develops a method for coordinating various collection tasks between the planning systems to increase the overall utility of the collected data. The method focuses on allocation of collection requests to scheduling systems rather than complete centralized planning over the entire system so that the current planning infrastructure can be maintained. Previous work in this area is expanded upon by inclusion of an online learning method to capture information about the uncertainty pertaining to the scheduling and completion of collection tasks, which is subsequently used in a mathematical programming method for resource allocation. An analysis of results and improvements as compared to current operations is presented at the end through a few different theoretical scenarios. These results provide evidence that the newly developed methods can increase the total value of serviced requests compared to current operations, with some theoretical scenarios producing more than double the value using the new methods over the current styles of planning.