 The ERC-Funded EXTREMA Project: Achieving Self-Driving Interplanetary CubeSatsGianfranco Domenico (),
Eleonora Andreis,
Andrea Morelli,
Gianmario Merisio,
Vittorio Franzese,
Carmine Giordano,
Alessandro Morselli,
Paolo Panicucci,
Fabio Ferrari and
Francesco Topputo
A chapter in Modeling and Optimization in Space Engineering, 2023, pp 167-199 from Springer Abstract: Abstract In the last decade, the new space economy has underlined the importance of the space sector for public and private actors. Advances in CubeSats technologies allowed widespread access to large and small businesses, with private companies and undersized players benefitting from the reduced development, manufacturing, and launch costs. As a result, the number of space assets orbiting the Earth had an exponential rise. Soon this momentum will affect outer and interplanetary space as well. The current paradigm for deep-space missions relies on the involvement of the ground segment to perform several routinary tasks, including determining the spacecraft position, computing the reference trajectory, and uploading control maneuvers. These activities are known as Guidance, Navigation, and Control (GNC). To date, this approach has proven to be sustainable, but the increasing number of Spacecraft is showing the fragility of ground-based assets. In particular, the scarce number of deep-space ground facilities and the involvement of human operators throughout the entire mission make the approach hardly scalable to an enlarged crowd of deep-space probes. The EXTREMA (Engineering Extremely Rare Events in Astrodynamics for Deep-Space Missions in Autonomy) project aims toward a paradigm shift on how deep-space GNC is performed by enabling CubeSats with autonomous GNC capabilities. The project has received a consolidator grant from the European Research Council (ERC), a prestigious acknowledgment that funds cutting-edge research in Europe. EXTREMA is built upon three pillars: autonomous navigation, autonomous guidance and control, and autonomous ballistic capture. This chapter presents the motivations behind the project goals by underlining the current state of the art for each Pillar and how EXTREMA aims to overcome it. First, the methodology employed within EXTREMA is outlined by expounding the algorithms designed within each pillar. Then, their validation through tailor-made hardware-in-the-loop test benches is illustrated. The outcome of each Pillar is combined into an integrated HIL facility to test autonomous GNC systems: the EXTREMA Simulation Hub. Eventually, the test cases to prove the feasibility of the EXTREMA vision are detailed, and the potential impact of the project is discussed. By freeing interplanetary CubeSats from human supervision, the project resolutions will be effortlessly transferrable to larger spacecraft with more powerful and diverse payloads, ultimately paving the way to systematic deep-space exploration and exploitation. Keywords: Deep-space CubeSats; Autonomous GNC; Interplanetary missions; Space exploration and exploitation; Hardware-in-the-loop simulations (search for similar items in EconPapers) There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it. Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-3-031-24812-2_6 Ordering information: This item can be ordered from DOI: 10.1007/978-3-031-24812-2_6 Access Statistics for this chapter More chapters in Springer Optimization and Its Applications from Springer |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.