Speaker
Description
Nanosatellites typically operate on a basis of scheduled, routine procedures, defined by users on the ground and dictated via pass uplinks. The development of machine learning algorithms, combined with constant advancement in the efficiency and capabilities of nanosatellite systems, has led to the point where artificial intelligence may be deployed on small satellites via low-power components such as FPGAs. The use of onboard AI facilitates a wealth of new capabilities and applications in nanosatellites, including improved processing and filtering of EO imagery and responsive onboard decision making with a diminished reliance on schedule uplinks. As part of a larger focus on responsive and intelligent spacecraft operations, Craft Prospect is developing a CubeSat payload which identifies user-chosen features in imagery captured via an integrated camera. This payload, the Forwards Looking Imager (FLI), provides the satellite with advanced knowledge of ground and atmospheric features ahead of the nadir, allowing it to make decisions on where to direct other onboard sensors, how to prioritise and filter downlink data, and when to respond to targets of opportunity. This presentation covers the development of the FLI, including system architecture, testing, performance metrics, capabilities and its integration into the standard CubeSat platform. The FLI marks the first system in a planned larger family of autonomy-enabling products, leveraging new technologies such as vision processing units (VPU). This family is being developed in alignment to CCSDS’s Mission Operations Service concept and its road map will be illustrated within the context of fitting into this concept.
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