  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.
