Capuano, Giovanni Maria (2025) Edge AI for Autonomous Spacecraft: Enabling Onboard Intelligence with FPGA Acceleration. [Tesi di dottorato]
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| Tipologia del documento: | Tesi di dottorato |
|---|---|
| Lingua: | English |
| Titolo: | Edge AI for Autonomous Spacecraft: Enabling Onboard Intelligence with FPGA Acceleration |
| Autori: | Autore Email Capuano, Giovanni Maria giovannimaria.capuano@unina.it |
| Data: | 10 Dicembre 2025 |
| Numero di pagine: | 184 |
| Istituzione: | Università degli Studi di Napoli Federico II |
| Dipartimento: | Ingegneria Elettrica e delle Tecnologie dell'Informazione |
| Dottorato: | Information technology and electrical engineering |
| Ciclo di dottorato: | 38 |
| Coordinatore del Corso di dottorato: | nome email Russo, Stefano sterusso@unina.it |
| Tutor: | nome email Strollo, Antonio Giuseppe Maria [non definito] Petra, Nicola [non definito] Severi, Mariano [non definito] |
| Data: | 10 Dicembre 2025 |
| Numero di pagine: | 184 |
| Parole chiave: | Artificial Intelligence, FPGA-based Acceleration, Autonomous Spacecraft, Object Detection, Relative Navigation, Radiation Tolerance |
| Settori scientifico-disciplinari del MIUR: | Area 09 - Ingegneria industriale e dell'informazione > ING-INF/01 - Elettronica |
| Informazioni aggiuntive: | Ciclo di appartenenza: 38 |
| Depositato il: | 10 Dic 2025 21:05 |
| Ultima modifica: | 02 Set 2026 08:04 |
| URI: | https://www.fedoa.unina.it/id/eprint/15958 |
Abstract
Artificial intelligence (AI)-driven onboard data processing is a cornerstone of future space missions, enabling autonomous and time-critical decision-making in applications such as planetary exploration, in-orbit servicing, and Earth Observation (EO). Executing AI algorithms directly onboard mitigates the latency and bandwidth constraints inherent to ground-based processing. However, most commercial off-the-shelf edge-AI accelerators lack the radiation tolerance required for long-duration and deep-space missions. Space-qualified Field-Programmable Gate Arrays (FPGAs) provide a robust alternative, combining fault tolerance, low power consumption, and in-flight reconfigurability. This thesis investigates FPGA-based acceleration of deep learning models for real-time target detection in optical Earth observation. The proposed SR-YOLOv5s network integrates a convolutional super-resolution backbone to enhance the recognition of small maritime targets, achieving a mean mAP50 of 0.9272 ± 0.0029 and a 64.5% gain in very small object detection. Implemented on the Microchip PolarFire System-on-Chip with the CoreVectorBlox accelerator, the model achieves 18 fps while consuming less than 2 W of power, demonstrating the feasibility of compact, energy-efficient neural pipelines for size-, weight-, and power-constrained platforms such as CubeSats. Integration of the CoreVectorBlox within the ANHEO autonomous navigation unit demonstrated the advantages of FPGA reconfigurability through bitstream updates, yielding a 3.3× performance improvement and a 50% energy reduction per frame in the satellite detection task. Altogether, the findings establish reconfigurable FPGAs as a key enabler of intelligent and resilient spacecraft, providing the capability to adapt onboard computational resources to evolving mission objectives, operational conditions, and algorithmic developments.
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