Session
Technical Session 5: Ground Systems
Location
Utah State University, Logan, UT
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.
The paper introduces the GOMSPACE Hands-Off Operations Platform (HOOP), an automated, flexible, and scalable end-to-end satellite operation framework for commanding and monitoring subsystems, single-satellites, or constellation-class missions. To this, the POWVER initiative at Saarland University has contributed state-of-the-art dynamic programming and learning techniques based on profound battery and electric power budget models. These models are continually kept accurate by extrapolating data from telemetry received from satellites. The resulting machine learning approach delivers optimal, efficient, scalable, usable, and robust flight plans, which are provisioned to the satellites with zero need for human intervention—but which are still under the full control of the mission operator. We report on insights gained while validating the integrated POWVER-HOOP approach in orbit on the dual-satellite mission GOMX–4 by GOMSPACE that is currently in orbit.
On the Automation, Optimization, and In-Orbit Validation of Intelligent Satellite Constellation Operations
Utah State University, Logan, UT
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.
The paper introduces the GOMSPACE Hands-Off Operations Platform (HOOP), an automated, flexible, and scalable end-to-end satellite operation framework for commanding and monitoring subsystems, single-satellites, or constellation-class missions. To this, the POWVER initiative at Saarland University has contributed state-of-the-art dynamic programming and learning techniques based on profound battery and electric power budget models. These models are continually kept accurate by extrapolating data from telemetry received from satellites. The resulting machine learning approach delivers optimal, efficient, scalable, usable, and robust flight plans, which are provisioned to the satellites with zero need for human intervention—but which are still under the full control of the mission operator. We report on insights gained while validating the integrated POWVER-HOOP approach in orbit on the dual-satellite mission GOMX–4 by GOMSPACE that is currently in orbit.
