On the Automation, Optimization, and In-Orbit Validation of Intelligent Satellite Constellation Operations

TL;DR

This paper discusses the development and validation of automated, optimized, and intelligent control solutions for satellite constellation operations, addressing challenges in power management and decision-making in modern nanosatellite networks.

Contribution

It introduces novel software-based automation and learning techniques for satellite constellation management, validated through in-orbit nanosatellite missions.

Findings

Successful implementation of automated decision-making algorithms

Enhanced power management in satellite operations

Validation of solutions in real nanosatellite missions

Abstract

Recent breakthroughs in technology have led to a thriving "new space" culture in low-Earth orbit (LEO) in which performance and cost considerations dominate over resilience and reliability as mission goals. These advances create a manifold of opportunities for new research and business models but come with a number of striking new challenges. In particular, the size and weight limitations of low-Earth orbit small satellites make their successful operation rest on a fine balance between solar power infeed and the power demands of the mission payload and supporting platform technologies, buffered by on-board battery storage. At the same time, these satellites are being rolled out as part of ever-larger constellations and mega-constellations. Altogether, this induces a number of challenging computational problems related to the recurring need to make decisions about which task each satellite is to effectuate next. Against this background, GomSpace and Saarland University have joined forces to develop highly sophisticated software-based automated solutions rooted in optimal algorithmic and self-improving learning techniques, all this validated in modern nanosatellite networked missions operating in orbit.