Alberto Luigi Piredda
Supervised Learning algorithm for Autonomous Rendez-Vous in Space.
Rel. Elisa Capello. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Aerospaziale, 2026
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Abstract
The rapid growth of the satellites in Earth orbit and the prospect of increasingly dense constellations, is reshaping the operational landscape of spaceflight. In this environment, ground-intensive approaches to fleet operations, inspection, and anomaly response become progressively less scalable. This trend motivates au- tonomous rendezvous and proximity operations as enabling technologies for routine inspection and servicing, as well as for safer and more efficient operations in space missions. At the same time, it imposes stricter requirements on onboard guidance: algorithms must be robust, constraint-aware, and computationally efficient to support repeatable operations with limited human intervention. Autonomy, however, cannot be assessed solely through nominal simulation per- formance.
A flight-relevant guidance solution is expected to provide consistent convergence, enforce feasibility under limited control authority, tolerate uncertain- ties in dynamics and vehicle properties, and run with bounded latency on onboard computers
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