Autonomous Multiagent Search-and-Image Tasking for Satellite Constellations

Advances in spacecraft technology have enabled observational satellite constellations containing a greater number of imaging agents, allowing for more complex planetary and climate science missions. However, current methods for constellation operation identify targets and generate multiagent satellite scheduling solutions offline. This paradigm has large ground-based operational overheads since it requires a cycle of (i) data downlink, (ii) target identification, (iii) global task scheduling, and (iv) schedule upload; this results in plans that are brittle to changes to the system, respond sluggishly to an evolving target space, and are computationally expensive and time-consuming to find and uplink, especially as the number of agents increases. These inefficiencies could be mitigated through the introduction of distributed, autonomous tasking. I propose to design and evaluate decentralized satellite autonomy policies for optimization of constellation-wide search-and-image tasks, in which agents collaboratively search for and identify new targets and image them, maximizing the science output of the constellation. The decentralized method has key advantages: As satellites get new information about target locations, they can immediately evaluate which among the set of upcoming targets is best to image or if searching for new targets would better optimize global performance. On a larger scale, this means the constellation will dynamically respond to the evolving target space and be robust to global changes in the network of agents. Overall, this research will increase the responsiveness of terrestrial constellations for environmental monitoring, and it will enable deep-space constellations about bodies with dynamic planetary science environments where communication with offline systems is costly if not impossible.

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