Autonomous Scheduling Requirements for Agile Cubesat Constellations in Earth Observation

Distributed Space Missions such as formation flight and constellations, are being recognized as important Earth Observation solutions to increase measurement samples over space and time. Cubesats are increasing in size (27U, 40 kg) with increasing capabilities to host imager payloads. Given the precise attitude control systems emerging commercially, Cubesats now have the ability to slew and capture images within short notice. Prior literature has demonstrated a modular framework that combines orbital mechanics, attitude control and scheduling optimization to plan the time-varying orientation of agile Cubesats in a constellation such that they maximize the number of observed images, within the constraints of hardware specs. Schedule optimization is performed on the ground autonomously, using dynamic programming with two levels of heuristics, verified and improved upon using mixed integer linear programming. Our algorithm-in-the-loop simulation applied to Landsat's use case, captured up to 161% more Landsat images than nadir-pointing sensors with the same field of view, on a 2-satellite constellation over a 12-hour simulation. In this paper, we will derive the requirements for the above algorithm to run onboard small satellites such that the constellation can make time-sensitive decisions to slew and capture images autonomously, without ground support. We will apply the above autonomous algorithm to a time critical use case - monitoring of precipitation and subsequent effects on floods, landslides and soil moisture, as quantified by the NASA Unified Weather Research and Forecasting Model. Since the latency between these event occurrences is quite low, they make a strong case for autonomous decisions among satellites in a constellation. The algorithm can be implemented in the Plan Execution Interchange Language - NASA's open source technology for automation, used to operate the International Space Station and LADEE's in flight software - enabling a controller-in-the-loop demonstration. The autonomy software can then be integrated with NASA's open source Core Flight Software, ported onto a Raspberry Pi 3.0 for a software-in-the-loop demonstration. Future use cases can be time critical events such as cloud movement, storms or other disasters, and in conjunction with other platforms in a Sensor Web.