Agile, Autonomous Spacecraft Constellations with Disruption Tolerant Networking to Monitor Precipitation and Urban Floods

TL;DR

This paper presents a novel framework for agile, autonomous satellite constellations utilizing disruption-tolerant networking to improve real-time monitoring of precipitation and urban floods, demonstrating significant performance gains.

Contribution

It introduces an integrated algorithmic framework combining orbital mechanics, attitude control, and intelligent prediction for re-orienting small satellites in a constellation, enhancing responsiveness and observation accuracy.

Findings

Onboard scheduling observed ~7% more flood magnitude than ground-based.

Constellations with agility performed ~98% better than non-agile ones.

Low latency in inter-satellite communication enabled effective real-time data sharing.

Abstract

Fully re-orientable small spacecraft are now supported by commercial technologies, allowing them to point their instruments in any direction and capture images, with short notice. When combined with improved onboard processing, and implemented on a constellation of inter-communicable satellites, this intelligent agility can significantly increase responsiveness to transient or evolving phenomena. We demonstrate a ground-based and onboard algorithmic framework that combines orbital mechanics, attitude control, inter-satellite communication, intelligent prediction and planning to schedule the time-varying, re-orientation of agile, small satellites in a constellation. Planner intelligence is improved by updating the predictive value of future space-time observations based on shared observations of evolving episodic precipitation and urban flood forecasts. Reliable inter-satellite communication within a fast, dynamic constellation topology is modeled in the physical, access control and network layer. We apply the framework on a representative 24-satellite constellation observing 5 global regions. Results show appropriately low latency in information exchange (average within 1/3rd available time for implicit consensus), enabling the onboard scheduler to observe ~7% more flood magnitude than a ground-based implementation. Both onboard and offline versions performed ~98% better than constellations without agility.