Thesis

Artificial Intelligence-assisted optical navigation for small body missions with application to Hera

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Awarding institution
  • University of Strathclyde
Date of award
  • 2025
Thesis identifier
  • T17449
Person Identifier (Local)
  • 202188560
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Qualification Name
Department, School or Faculty
Abstract
  • Planetary defence against asteroid is a booming field in the space sector. High-levelof autonomy is required on board spacecraft during their proximity operations aroundthese small bodies, both to cope with the highly uncertain non-linear dynamical environment which surrounds them, and to reduce ground operation complexity related tothe significant delays in communication covering the distances involved in these missions. This thesis presents an Artificial Intelligence (AI)-assisted Image Processing (IP)algorithm to support the optical navigation of asteroid rendezvous missions during theirclose proximity operations. By focusing on the case scenario of the ESA’s Hera missionto binary asteroid system (65803) Didymos, this work aims to tackle challenges of thecurrent paradigm of methodologies involved in standard and intelligent IP algorithms.Firstly, by exploiting Convolutional Neural Networks, the algorithm is designed anddeveloped to cope with scenarios involving adverse illumination conditions, irregularshape of the target body and the presence of external bodies. Secondly, the algorithm isrefined and implemented in an Open Loop navigation system to assess its performancesin the context of proximity operations. Finally an incremental validation test campaignis performed to assess the applicability of the developed algorithm on board asteroidrendezvous missions spacecraft. The test campaign objective is twofold: on one handit aims to solve standard AI-related issues, i.e. bridging domain gaps to account forcontingencies; on the other it aims to validate the algorithm on board spaceborne computers within the Guidance, Navigation and Control system of the spacecraft. Thisthesis primarily contributes by designing and implementing a structured pipeline fordeploying AI-based IP algorithm in asteroid optical navigation, enabling a systematicevaluation of its suitability and performance.
Advisor / supervisor
  • Feng, Jinglang
  • Gil-Fernández, Jesús
Resource Type
DOI

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