Master's degree theses

A board for autonomous spacecraft collision avoidance (COLA) is currently being designed at the u3S laboratory. As part of the COLA process, the spacecraft hosting the board shall be capable of performing a preliminary screening of possible close encounters with the many existent resident space objects (RSO), based on the minimum orbital intersection distance (MOID). Several methods have been proposed in the literature for accomplish this task, with different levels of accuracy and computational burden.

Through the thesis work, the candidate will assess and critically compare some of the existing MOID algorithms, for selecting the one more suitable for an onboard implementation on embedded hardware, trading-off between computational burden and accuracy of the computation. Towards this end, the research project will encompass at least the following steps

• Literature review and pre-selection of candidate algorithms for MOID computation.
• Implementation of a Matlab simulator for creating an orbital scenario including the main spacecraft plus hundreds/thousands of RSO considered as possible collision threats.
• Integration of the pre-selected algorithms in the simulator for computing the MOID between the main spacecraft and each of the RSOs
• Comparison of the algorithms in terms of accuracy and computational speed, and selection of a best one.
• Implementation of the selected algorithm on embedded hardware (e.g. raspberry PI or Arduino).

Number of students required: 

• 1 Student

Requirements:

• Attended and passed ‘Spacecraft Orbital Dynamics and Control’
• Good mathematical and programming skills

In the early stage of the definition of a space mission, it is often desirable to have a fast preliminary estimation of the DV cost of a low-thrust transfer. When the transfer is realised through a long spiral trajectory, a quick estimation of the total DV and transfer time can avoid lengthy calculations. For this reason, a number of authors have proposed simple control laws for the variation of specific orbital elements and/or analytical equations for the estimation of the DV associated to a given transfer. The usual approach to develop such approximations is that of integrating GPE using as integrand a fast anomaly variable while assuming the slow orbital elements as constants.

A recent approach developed in our lab proposes a more accurate analytical framework for integrating the GPE formulated as a system of linear differential equations. The thesis work consists of investigating the use of such a framework to develop analytical formulas for the secular orbital element variations, to be then used for the estimation of the DV associated to a given transfer.

Assessing and mitigating the risk of collisions between spacecraft in orbit, especially in LEO, is of paramount importance to ensure the safety of space assets, especially in the current context of the continuously increasing number of resident space objects. The common practice to evaluate the collision risk is by calculating the so-called 2D probability of collisions index, based on analysis of the closest approach between the two spacecraft in the so-called encounter plane. Although a 2D geometric analysis is suitable in most situations, some conjunctions require a full 3D analysis to assess the collision risk properly.

The thesis activity involves reviewing existing methods for 3D collision risk assessment and developing a detailed numerical simulator for assessing their performance. Extensive simulations in several representative conjunction scenarios will be performed to evaluate the respective advantages and drawbacks of the different methods. Areas of improvement shall also be identified.

The goal of this research is to explore novel techniques and develop an advanced deep learning model capable of detecting different satellite classes in space imagery. This multiclass satellite detector should be able to handle various satellite types, including different sizes, shapes, and orbital characteristics.
This research will contribute to the development of a generic multiclass satellite detector, enabling more comprehensive monitoring and analysis of satellite activities in space. The outcome of this thesis will have practical applications in satellite tracking, space debris monitoring, and overall space situational awareness.    This topic assumes a basic knowledge of Python. Participants will gain insights into the image formation and digitization process, along with hands-on experience in developing their own Deep Learning models using PyTorch, tailored specifically for the challenges of space environments.                                 

Apply deep learning techniques, specifically semantic segmentation, to synthetic satellite images. Focusing on developing models that can accurately segment and classify specific features or anomalies in the images of satellites will support the development of future on-orbit servicing and space debris removal initiatives.                    Participants will gain insights into the image formation and digitization process, along with hands-on experience in developing their own Deep Learning models using PyTorch, tailored specifically for the challenges of space environments.                              

Develop a real-time object detection system using PyTorch and deep learning models for space missions. Focus on optimizing the inference speed while maintaining high accuracy. Explore techniques such as model quantization, pruning, and parallelization to achieve real-time performance on space exploration devices. For this work we can buy a hardware accelerator for the better inference speed.                                                      Participants will gain insights into the image formation and digitization process, along with hands-on experience in developing their own Deep Learning models using PyTorch, tailored specifically for the challenges of space environments.