Date of Award
5-2025
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Aerospace, Physics, and Space Sciences
First Advisor
Dr. Madhur Tiwari
Second Advisor
Dr. Son Luu Nguyen
Third Advisor
Dr. Camilo Andres Riano-Rios
Fourth Advisor
Dr. Ratneshwar Jha
Abstract
Asteroid exploration remains a popular topic in the scientific community, however hurdles still exist for controlling spacecraft within the asteroid environment. Communication delays often require the usage of limited onboard computing hardware for navigation. Additionally, long mission timelines must be accommodated with highly efficient fuel use. Considering these issues, it is apparent that any guidance, navigation, and control (GNC) system in these spacecraft should emphasize both computational and fuel efficiency in its design. Furthermore, the integration of a robust state estimation system is necessary for the successful deployment of such systems. The development of a controller aiming to address these difficulties is detailed in this thesis. The developed controller aims to generate near-optimal control outputs from raw sensor data without computationally expensive state estimation or optimization algorithms.
Recommended Citation
Quinn, Patrick David, "Developing End-to-End Imitation Learning for Asteroid Proximity Operations" (2025). Theses and Dissertations. 1548.
https://repository.fit.edu/etd/1548
