Coordinated planning of air and space assets : an optimization and learning based approach

Abstract

collect information. This may include taking pictures of the ground, gathering infrared photos, taking atmospheric pressure measurements, or any conceivable form of data collection. Often these separate organizations have overlapping collection interests or flight plans that are sending sensors into similar regions. However, they tend to be controlled by separate planning systems which operate on asynchronous scheduling cycles. We present a method for coordinating various collection tasks between the planning systems in order to vastly increase the utility that can be gained from these assets. This 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 without changing any aspects of the schedulers. We expand on previous work in this area by inclusion of a learning method to capture information about the uncertainty pertaining to the completion of collection tasks, and subsequently utilize this information in a mathematical programming method for resource allocation. An analysis of results and improvements as compared to current operations is presented at the end.