To ensure the safety of a spacecraft, operators collect thousands of telemetry signals and monitor them for anomalies, which is both expensive and time-consuming. Space agencies have been researching Machine Learning (ML)-based Time Series Anomaly Detection (TSAD) methods to improve automation, but this is hindered by a lack of high-quality benchmarks. This thesis explores the application of ML-based TSAD on instrument telemetry data for the XMM-Newton space telescope. The data was explored to find several unique challenges, such as recurring eclipses and a high volume of missing data. A methodology was then developed to pre-process the raw data into an ML-compatible format, detect anomalies using a semi-supervised forecasting approach and post-process the detections into a benchmark-suitable format. The method yielded over 40 detections, which were partially validated in discussion with instrument engineers. A refined methodology, incorporating areas for improvement, is presented to proceed with an eventual XMM-Newton anomaly benchmark.

Unmanned Aerial Vehicles (UAVs) play a crucial role in various applications, including disaster response, infrastructure inspection, and search-and-rescue missions. To maximise their effectiveness, UAVs must achieve a high level of autonomy, particularly when navigating cluttered environments. This requires real-time collision avoidance algorithms that efficiently utilise on-board computational resources. Existing approaches either maintain explicit map representations, which provide memory of past observations but require significant computation, or directly process depth images, which are computationally efficient but restrict flight paths to the sensor’s field of view (FOV). This thesis presents a novel, lightweight collision avoidance algorithm that operates directly on depth images while retaining memory of past observations. By aggregating previous depth data, the algorithm enables UAVs to plan trajectories beyond the FOV of onboard sensors without the computational overhead of explicit map maintenance. Experimental results demonstrate that the proposed method outperforms existing state-of-the-art methods in terms of computational efficiency, flight speed, energy cost, and path length until collision.

This research investigated the application of electrical thrusters within the Attitude Determination and Control System (ADCS) of the European Space Agency’s LUMIO mission, a 12U CubeSat set to op- erate in a quasi-periodic halo orbit around the Earth-Moon L2 point. While CubeSats have become pivotal tools for lunar exploration, the use of electrical thrusters for ADCS, particularly in the lunar en- vironment, remains under-explored. This study aimed to evaluate the impact of replacing LUMIO’s current ADCS design by electrical thruster configurations on attitude control performance, robustness, and connectivity, bridging a critical gap in current literature.
A simulation framework was developed to assess spacecraft performance over a two-week period, ex- amining the LUMIO mission reaction wheel set-up, as well as four distinct thruster configurations. Key metrics included thrust output, angular velocity, and pointing accuracy, represented by the half-cone offset angle. In addition, power and energy requirements were investigated. Robustness tests eval- uated system adaptability to single thruster failures, solar array deployment, and high initial angular velocities. Physical validation was achieved by porting the control algorithm to an embedded STM32 Nucleo board, which was connected to a solenoid valve representing a dummy thruster. This setup allowed for real-time testing of execution accuracy, signal fidelity, and system integration.
The results demonstrated that the control algorithm consistently maintained high pointing accuracy, keeping the half-cone offset angle within the mission requirement of 0.18 [◦]. Reaction wheels and electrical thrusters effectively generated the required control torques, with negligible angular momen- tum build-up in the nominal simulation. Overdetermined thruster configurations showcased enhanced energy efficiency, reducing total energy consumption by approximately 29% compared to determinate setups. However, the study also highlighted challenges, such as the minimum impulse bit constraints of the Pocket Rocket thrusters, which limited precise low-thrust operations.
Robustness tests revealed that determinate configurations failed to accommodate single thruster fail- ures, while overdetermined systems adapted effectively, albeit with increased power demands. From the results, it is recommended for any space mission to be certain that the on-board computer is aware of any thruster fail occurring. The undeployed solar array configuration significantly reduced energy re- quirements, supporting its feasibility for early mission phases. The thruster configurations showed not to be able to realistically counteract de-tumbling manoeuvres, significantly degrading their feasibility in the actual LUMIO mission. Additionally, the embedded system validation confirmed accurate command signal generation and execution, bridging the gap between simulation and real-world implementation.
Although the electrical thruster configurations adhered to pointing accuracy requirements in nominal simulation conditions, they impose significantly higher power and energy demands and are unable to counteract high de-tumbling manoeuvrers compared to the current reaction wheel configuration in the LUMIO ADCS design. Addressing main engine parasitic torques and exploring alternative, higher- performance thrusters may offer a feasible solution for integrating electrical propulsion into lunar Cube- Sat ADCS. While the reaction wheel and non-electrical thruster combination remains the preferable option for now, this research demonstrates the potential for electrical thrusters in advancing CubeSat capabilities. Future studies should build upon this foundation by addressing current limitations, extend- ing simulation durations, and exploring innovative propulsion technologies to unlock their full potential in deep-space missions.

This thesis investigates the use of H-infinity Open Loop Shaping (OLS) for designing Thrust Vector Control (TVC) systems during the atmospheric ascent of flexible launch vehicles (LVs). The research has two main goals. The first goal is to assess whether H-infinity OLS can produce rigid body controllers for launch vehicles with comparable or superior robustness, stability, and performance to controllers designed with H-infinity Closed Loop Shaping (CLS), while also reducing design complexity and time. The second goal is to explore an integrated H-infinity OLS approach that tunes both the rigid body controller and the bending filter simultaneously, simplifying the traditional separate design process.
Results demonstrate that H-infinity OLS and H-infinity CLS yield controllers with identical performance, robustness, and stability when applied to design controllers with the same order and structure. However, H-infinity OLS significantly simplifies the design process by avoiding the manipulation of multiple transfer functions, offering higher reproducibility between flight points, and automatically ensuring robustness at both the plant input and output. Although these benefits come at the cost of converting requirements into open loop specifications and setting loop shaping weighting filters to meet the specifications, both challenges have been addressed and simplified in this thesis.
The thesis also extends the rigid body H-infinity OLS design process into an integrated methodology that simultaneously tunes both the rigid body controller and the bending filter, achieving identical results to the traditional separate design approach while reducing overall design time and complexity.
This research demonstrates that H-infinity OLS provides a robust framework for simplifying the design of TVC systems for launch vehicles, without compromising performance and robustness. Additionally, the integrated H-infinity OLS methodology for tuning both the rigid body controller and bending filter effectively streamlines the design process while achieving identical results to the traditional separate approach. These findings validate H-infinity OLS as a viable alternative to H-infinity CLS and introduce a more efficient methodology for designing both the rigid body controller and the bending filter for TVC control systems in launch vehicles.

Landing a rocket on Earth is a key factor in enabling quicker and more cost-effective access to space. However, it poses significant challenges due to the highly uncertain environment. A robust, reliable, and real-time capable Guidance, Navigation, and Control (GNC) system is essential to guide the vehicle to the landing site while meeting terminal constraints and minimizing fuel consumption.. A 6-Degrees Of Freedom (DOF) flight simulator is developed, including accurate vehicle and environmental models such as variable Mass, Center of Mass and Inertia (MCI), winds, and detailed aerodynamics. Furthermore, not only thrust magnitude and engine deflections are used as controls, but also two sets of aerodynamic fins, as they are present in real rockets. Finally, initial condition dispersions and uncertainties in dynamics, navigation, and controls are included, to assess the robustness of the GNC strategy.
This study applies Meta Reinforcement Learning (meta-RL) to the terminal rocket landing problem. Through repeated interactions between an agent and its environment over multiple episodes, a Neural Network (NN) develops a policy that maps observed states to control actions. Unlike traditional methods, meta-RL leverages both current and past observations to output the optimal action, enhancing the robustness of the policy. Recurrent Neural Networks (RNNs) like Long-Short Term Memory (LSTM), are commonly used in meta-RL for their ability to handle sequential data. However, this thesis investigates also the use of attention-based Gated Transformer XL (GTrXL) networks, which are promising to improve the solution accuracy.
This research confirms this hypothesis, demonstrating that the GTrXL-based policy significantly outperforms the LSTM-based one. This policy is also less sensitive to internal hyperparameters, provided that the NN has a sufficient number of weights and biases to capture all the relevant features. The results indicate that incorporating complex vehicle and environmental models, along with dispersed initial conditions, aerodynamic and wind uncertainties, navigation and control errors, and actuator deflection rate constraints into the training process, yields a robust GTrXL-based policy. This guidance policy is able to meet terminal constraints on position, horizontal velocity, vertical angle, and angular rates in 1000/1000 simulations. The only exception is the vertical velocity which is exceeded on average by only 2-3 m/s. Including a thrust rate constraint mitigates this issue, reducing the average violation to 1 m/s. Finally, developing a terminal patch to increase the thrust magnitude in the last few meters before touchdown completely eliminates the vertical velocity issue, producing a 100% success rate, with all the Monte Carlo runs meeting all the terminal constraints. Moreover, the NN produces a solution in about 6 ms, showing great potential for its real-time use. The results are consistently superior to those of a conventional Guidance and Control (G&C) strategy where a Linear Quadratic Regulator (LQR) controller tracks an optimal trajectory, consuming only 6% more fuel.
Recommendations for future works include improving the reward function to better handle the vertical velocity constraint, and penalizing control effort to have smoother Thrust Vector Control (TVC) and fins control profiles. It is also suggested to expand the simulation scenario, including the unpowered aerodynamic descent.

Recent years have seen the exponential growth of the number of artificial objects orbiting the Earth. Since space debris can cause substantial damage, measures are investigated to make space operations more sustainable. Amongst these, there is an interest in the development of methods to detect possible collisions between objects in space. Traditional methods require highly accurate propagations with large computational times, so the focus of this thesis is the modelling of faster and more accurate algorithms to enhance collision risk assessment. To avoid long propagations, neural networks have been trained for orbit prediction and uncertainty estimation, with the main goal of calculating the collision probability between two objects. Different analyses have been made to assess the accuracy, stability, applicability and generalisation of the methods developed. The results show much faster collision risk calculations than traditional methods with a similar level of accuracy. It is also demonstrated how a tool which can generalise to satellites with different geometries can be built.

A recent trend in the Aerospace industry is the miniaturisation of spacecraft. Historically these could not be controlled actively, limiting their operations and lifespan. TU Delft has already committed numerous research towards the development of miniaturised propulsion system using different technologies. This thesis continues work on the Vaporising Liquid Micro-resistojet (VLM) developed by Versteeg. Already three other master students have worked with this thruster and encountered several issues. Water tests had been attempted before but were unsuccessful due to the used feed system. Moreover, the latest experiments showed a significant degradation in terms of nozzle geometry and leak rate, which likely led to a decreased thruster performance. Lastly, the change to the TB-50m thrust bench resulted in unexpected drift behaviour.

This thesis addresses these challenges by implementing changes to the thruster en test set-up. The thruster’s sealing surface has been restored to its original state using a CNC-machine. This was done to counter degradation of the nozzle geometry and reduce the VLM’s leak rate. Additionally, a sealing gasket was developed for the same reason. Unfortunately, its implementation resulted in an undesirable deviation of the nozzle’s geometry and was thus left out. A new water feed system is developed, based on the use of a pressurised tank. This reduced unwanted effects, such as bubble formation, that were encountered with the previously used syringe-based feed system. With the implementation of a liquid mass flow meter, the flow rate can be measured more accurately, thus reducing uncertainty in the VLM’s performance. While these improvements proved to be successful initially, several issues encountered during the work have resulted in a worse overall performance. The nozzle geometry already showed signs of deformation after just two months. Furthermore, a major leak was identified which could not be addressed on time. Despite this, tests were executed with chamber temperatures up to 400 °C using both nitrogen and water as propellants. For nitrogen, the measured thrust varied between 4.1 and 8.6 mN for a throat Reynolds range of 1250 to 3300. The specific impulse increased from 34 to 37 s, while the specific impulse efficiency increased from 0.33 to 0.52. Discharge coefficients between 0.66 and 0.72 were measured. Although the errors are comparable (± 15% at most), this performance is significantly worse compared to earlier results. The water experiments were not successful due to an uncontrollable thruster operation. Regardless of this, the obtained test results showed an equally degraded thruster performance. These were executed at chamber pressures of ∼0.85 bar and at various chamber temperatures. Thrust and specific impulse levels of 4.2 mN and 67 s were achieved, with errors as large as ± 34%. The corresponding specific impulse efficiency and discharge coefficient were 0.56 and 0.39, respectively. The throat Reynolds number decreased from 1550 to 1000. Because of these unsuccessful tests, the VLM performance could not be verified against earlier results, let alone used for validating the developed analytical model. Recommendations are provided to address the encountered issues and improve the thruster’s performance. This should enable successful nitrogen and water tests in the future.

The escalating problem of space debris necessitates effective solutions, such as active debris removal, to ensure sustainable orbital environments. Solar sails, with their nearly unlimited ΔV budget, present a promising, propellant-free method for targeting and potentially removing debris. However, the unique dynamics of solar sails prevent them from maintaining fixed positions relative to a target. This research explores the use of hold trajectories as alternatives to static hold points during far-range rendezvous operations. Using InTrance, a trajectory optimisation tool based on neuroevolution, the study assesses how operational constraints impact hold trajectories and the viability of preliminary homing trajectories. Results indicate that hold trajectories allow safe, extended solar sail operations close to debris, supporting sustained observation and monitoring efforts for debris management.

As the Moon reemerges as a renewed fronteer in space exploration, the Lunar Zebro project proposes to deploy a swarm of miniature rovers for efficient lunar surface exploration. One of their goals is to leverage recent advancements in deep learning and AI-accelerating hardware, in conjunction with Commercial Off-The-Shelf technologies and the NewSpace movement, to enhance the autonomous capabilities of these nano-rovers. This research focuses on integrating AI-accelerating hardware within the stringent Size, Weight, and Power (SWaP) constraints of these lunar rovers. It evaluates the suitability of various hardware configurations. A Convolutional Neural Network for hazard detection was trained and tested on different devices and scenarios. Finally, the operational cycle of the rover was simulated and the constrained resources were tracked for the different design options.

The Mars Ascent Vehicle will bring samples back from the surface of Mars to its low orbit in 2031. Its preliminary design has been performed by NASA, defining it as a two-stage solid propellant rocket. However, a research gap exists in finding what the optimum solid rocket motors geometries are to bring the Martian samples to a target orbit between 300km and 375km above Mars.
To this extent, the ascent and propellant burn have been modelled, allowing for variation in the geometry of the motors, but also in the launch and separation angles, and in thrust vectoring control. This model has been incorporated into an optimisation, from which a minimum lift-off mass of 343kg was found, 57kg less than required. In the process, 18 thousand different solutions have been found that satisfy all requirements, would the priority shift from the mass of the vehicle to for instance the burn time, or the final altitude.

The growing satellite congestion in Earth orbits increases the risk of Kessler Syndrome, that could potentially hinder humanity's activities in space. This problem could be tackled with Active Debris Removal or On-Orbit Servicing missions. This thesis project aimed to investigate the combined controller approach for OOS mission reach phase, in which both the s/c base and robotic manipulator are actively controlled by a single control system. The multi-body dynamics were defined with the SpaceDyn toolbox in Matlab and the advanced control method was chosen, Model Predictive Control (MPC). The advantage of MPC is the optimization-based strategy, a straightforward definition of constraints and dynamics prediction within the future horizon. The NMPC approach with successive linearization was designed and verified, the controller was tuned and the control system was tested in simulated scenario cases. This study was performed in collaboration with the German Aerospace Center DLR within RICADOS project.

The plasma behaviour in an air-propelled Hall thruster (SITAEL HT5k) is investigated by means of numerical modelling and experimental measurements. The air plasma employed (nitrogen/oxygen mixture) is firstly chemically characterized, identifying the most relevant species produced: four neutrals (N₂, N, O₂, O), four positive ions (N₂⁺, N⁺, O₂⁺, O⁺) and electrons. Then, it is explored the kinetic theory of ion-ion streaming instabilities in the presheath, relevant to electropositive multispecies plasmas, extending it to the four-ions case under study. These results are used to adapt a non-stationary 1D fully fluid Hall thruster model to operation with an air plasma. Calibration with an experimental discharge current signal confirms the physical validity of the formulation in reproducing breathing mode oscillations, even in multispecies plasmas. The experimental data also consist of fast-diving triple Langmuir probe measurements, which are used to validate the model, showing reasonable accuracy of the spatio-temporal distributions of the plasma properties.

Satellite tracking is used to predict a satellite’s orbit to communicate and perform other key functions. It is commonly performed using large ground-based radars with an accuracy of 1 to 10 km or specialized onboard satellite systems. Ground optical systems are a proliferating technology for satellite tracking, identification, and communications. Such optical systems have a narrow field of view, and to ensure smooth and quick satellite acquisition, the satellite must be tracked with higher accuracy (in the order of 100 m). Hence, to allow for optical ground systems to function smoothly they require satellite tracking systems providing sufficient accuracy which is not common for small satellites.
This work analyses two radio-based methods to provide sufficiently accurate satellite tracking for the next TU Delft satellite to be acquired optically. These methods utilize the innate satellite radio communications subsystem of the satellite without modifications. To optimize the analysis work, the model complexity was gradually built up. The first method analysed is phase interferometry, which based on a first-order model, turned out to be non-viable. The second method investigated is time difference of arrival. Increasingly more sophisticated models were developed to understand its performance and limitations. For example, for Delfi-PQ, by utilizing 25 ground stations in a square configuration with 1000 km baseline and a noise level of 236 ns (71 m), the target satellite in a 500 km circular orbit can be acquired, with at least the required accuracy, for 18% of the area where the satellite can be received by a minimum of 4 ground stations. It is expected that this performance can be improved through future work. Specifically, by utilizing multiple measurements taken at different times, the satellite orbital parameters can be estimated directly.
We conclude time difference of arrival to be a promising method for further research, development, and eventual implementation by TU Delft. The approach and structure of an even more sophisticated model is detailed for future work.

Programmers usually write test cases to test onboard software. However, this procedure is time-consuming and needs sufficient prior knowledge. As a result, small satellite developers may not be able to test the software thoroughly.

A promising direction to solve this problem is reinforcement learning (RL) based testing. It searches testing commands to maximise the return, which represents the testing goal. Testers need not specify prior knowledge besides the reward function and hyperparameters. Reinforcement learning has matured in software testing scenarios, such as GUI testing. However, migration from such scenarios to onboard software testing is still challenging because of different environments.

This work is the first research to apply reinforcement learning in real onboard software testing and one of few studies that perform RL-based testing on embedded software without a GUI. In this work, the RL agent observes current code coverage and the interaction history, selects a pre-defined command, or organises a command from pre-defined parameters to maximise cumulative reward. The reward function can be code coverage (coverage testing) or estimated CPU load (stress testing). Three RL algorithms, including the tabular Q-Learning, Double Duelling Deep Q Network (D3QN), and Proximal Policy Optimization (PPO), are compared with a random testing baseline and a genetic algorithm baseline in the experiments.

This study also performs regression testing with a trained RL agent, i.e., to test a version of onboard software that it has never seen before. To do that, the agent processes graph input with code coverage information. The graph is extracted from the onboard software source code via static code analysis. The work tries two graph neural network architectures (GGNN and GAT) with several graph pooling mechanisms to process the graph input.

Apart from the test command generation algorithms, some middleware is also implemented, including a command/response parser, a state identification module, a branch coverage collection tool, and a tool to extract the graph representation and node features. During onboard software testing, the onboard computer (OBC) or the electrical group support equipment (EGSE) can be the master of the bus. The command generation algorithms can run on a lab PC or a cloud server.

The research reveals the advantages and drawbacks of using reinforcement learning to test onboard software. On the one hand, RL-based testing performs well in non-deterministic environments (e.g., stress testing) and regression testing. On the other hand, more straightforward methods like random testing and the genetic algorithm are more useful in deterministic environments.

This document also introduces relative background knowledge. It leaves many recommendations for future work, such as improving sampling efficiency, generalization, and learning a model for fault detection in satellite operation.

Stereo matching, the process of inferring depth maps from stereo images, is one of the most heavily investigated topics in computer vision. It is part of the first module of navigation systems of planetary rovers, e.g., NASA’s Mars Exploration Rover (MER) missions, NASA’s Mars Science Laboratory (MSL) mission, and ESA’s ExoMars mission. Many stereo matching algorithms, traditional and deep learning based, are available and their performance is typically evaluated on indoor objects or outdoor city scenes. In this thesis, our goal is to improve insight into the performance of stereo matching algorithms for the Martian surface on a single-board computer. First, we search for and compare stereo matching algorithms ranked on stereo vision datasets to obtain a manageable set of algorithms and verify their implementations. Second, we derive performance metrics from the requirements and validate our set of verified algorithms on the Katwijk Beach Planetary Rover dataset. Through experimental evaluation, we gain insight into the performance of four stereo matching algorithms.

This thesis was aimed for the Master of Science degree in Mechanical Engineering at The Delft University of Technology. The goal of this research was to formulate an assignment algorithm for autonomous guidance, control, and navigation system for spacecraft formation flying using a Model Predictive Control approach for estimation of fuel expenditure. This report is split into three parts. First, the earth's gravitation field and its variation are discussed. The relative motion in the orbit around the earth is briefly explained. The derivation of linearized relative dynamics by Clohessy-Wiltshire is presented. In the second part, the assignment problem is explained. The majority of the literature has been using the distance between the deployed state to the target state. A new approach is discussed using fuel as the assignment parameter including the framework for the assignment parameters is established. For the third part, fuel expenditure estimation using two types of control schemes is explained. The Artificial Potential Function approach is concisely explained. The choice of Model Predictive Control over Artificial Potential Function is discussed. The aim for the third part was the formulation of the control problem using the Model Predictive Control with a linearization dynamics model of the satellites. The system model used in the MPC formulation was constructed based on the Clohessy–Wiltshire relative equations of motion. The demonstrations were established using randomized positions. The simulations were performed and estimation of the fuel expenditure was explained based on the simulation results. The proposed assignment algorithm was tested using the fuel cost estimations from the MPC-based simulations. The results showed that the algorithm performed as designed. Lastly, the possible future research options are listed out.

As a part of a Future Launcher Preparatory Programme contract issued by the European Space Agency, the aerospace startup company Dawn Aerospace is developing an integral, additive manufactured (AM), liquid rocket engine. This 2.5 kN class engine is propelled by the storable propellants 90% hydrogen peroxide and kerosene and used in the application of a (sub)orbital spaceplane. In this study, a preliminary design for the thrust chamber of the AM engine is proposed, which is realized from the superalloy Inconel 718. To avoid the chamber wall from melting under the extreme thermal loads which originate from the ongoing combustion reaction, both regenerative cooling and film cooling with hydrogen peroxide are considered. The proposed designs are based on a custom-developed 2-D numerical analysis code, which considers non-linear steady-state heat transfer and linear elastic structural deformation. The results of this code are validated against hot-fire tests of both regenerative cooled and film cooled thrust chambers, propelled by 90% hydrogen peroxide and kerosene. Moreover, an immersion screening test campaign is conducted to validate the chemical compatibility of AM Inconel 718 with 90% hydrogen peroxide. This thesis compares the proposed AM chamber designs to the current bimetallic chamber design that is already developed and hot-fired by Dawn Aerospace. The bimetallic design comprises a coated copper-alloy liner and relies mostly on non-AM production techniques. The coating is introduced to improve the chemical compatibility with hydrogen peroxide. Simulations show that the total available delta-v of the spaceplane is reduced from 3.46 km/s to 3.38 km/s when the bimetallic design is replaced by an AM Inconel 718 design, relying on regenerative cooling and film cooling. On the contrary, the AM design may offer a thrust chamber dry mass reduction of more than 75%, whereas the total number of sealing interfaces can be reduced by more than 50%. In addition, the inspection-heavy coating, which is plated onto the copper alloy liner of the current bimetallic engine design, can be removed in the AM design.

Various machine learning algorithms have been applied to find optimal lowthrust
satellite trajectories, however, no fair comparison of their accuracy has been made yet. In this paper, two common and promising supervised machine learning algorithms are compared for their regression capacities:
the artificial neural network and the Gaussian process. A grid search is performed to evaluate the performance of the algorithms on different datasets, varying the input features for training, and the hyperparameters of the algorithms. The best performing Gaussian process and artificial neural network are applied as surrogate in a model that optimizes a trajectory from Earth to Mars using a differential evolution optimization strategy. The performance of the models is evaluated on both the Euclidean distance between the inputs corresponding to the predicted minima of the surrogate method and the nearest local minimum obtained with the shaping method, and the accuracy of the predicted Δ푉 budget required. For both quantities, the Gaussian process outperforms the artificial neural network. The minimum required Δ푉 budget to reach Mars was predicted more accurately, and the duration of the trajectory and the best moment to launch were found more often and accurately as well.

Humans are embarking on a new era of space exploration with the plan of sending crewed spacecraft beyond Low Earth Orbit (LEO), to the Moon, Mars and beyond. NASA is committed to land astronauts on the lunar surface by the year 2024. The goal of NASA's lunar exploration program - Artemis, a collaboration with commercial and international partners including the European Space Agency (ESA), is to establish sustainable exploration by the end of this decade. The plan is to use what is learned on and around the Moon to take the next giant leap, namely sending astronauts to Mars. Activities planned during the Artemis missions, especially in the early phases, involve finding critical resources needed for long-term exploration, and acquiring more knowledge on Moon, Earth and the universe by carrying out experiments. All these activities will involve extensive lunar geological field work, which is orders of magnitude more complex than field geology on Earth. Extravehicular activities (EVAs) will become increasingly more complicated than the tasks executed during the early Artemis missions and generally during human spaceflight missions so far. EVA systems and crewmember skills that currently do not exist will be required. This plan entails many challenges as real-time support from ground control cannot be provided to astronauts who thus need to become more autonomous. Hence, modern human-machine interfaces have to be designed to support astronauts during their deep space missions. Augmented reality (AR) and the internet-of-things (IoT) are changing the way industries work, especially AR has found application for space applications, specifically for procedural work. Nevertheless, only one AR space-related study focused on the use of AR for future human planetary exploration, namely on navigation and traverse planning. While cuff-checklists guided Apollo astronauts on the Moon, wrist displays and tablets represent the standard tools during today's astronaut analog planetary EVA missions. However, these are often operationally unfeasible as crew has to handle several tools simultaneously and/or repeatedly look at the display and thus gets distracted from the surroundings leading to a potential loss of situational awareness, affecting their safety. Based on the research that is currently being performed on IoT technologies in combination with AR for visualisation and enhanced situational awareness purposes, the benefits obtained through the use of these technologies applied to future human planetary EVAs, more specifically geological site inspections, were explored in this research. The AR-IoT surface exploration tool developed for this research introduces a new approach for astronauts to carry out geological site inspections. The tool enables hands-free operations such as data logging, detailed photo-documentation, taking site coordinates, descriptions of sites through the presence of a verbal “field notebook”, as well as mapping and highlighting features during a traverse by creating waypoints, while providing crew with suit diagnostics. A user-centered design method was adopted to design the AR-IoT tool deployed on the Microsoft HoloLens. This highly iterative design process involved two to three expert reviews for each of the first three concepts, and three heuristic evaluations for the fourth concept until the subsequent generation of the first prototype. Key usability and user interaction aspects, pertinent capabilities determining the adoption of innovative interfaces, essential insights into future human-machine interaction and design requirements for AR meant for EVA astronauts were gathered through semi-structured interviews held with four ESA astronauts and astronaut geological field activities experts. The interviews together with the qualitative and quantitative data collected through the questionnaires were then used to assess the usability of the AR-IoT tool. Moreover, these data provided with additional knowledge on user-centered design AR studies in general but also and particularly on user-centered AR space-related studies. Valuable insights into interface design and user interaction aspects were gained. Results from the qualitative content analysis of the interviews stressed the importance of user satisfaction (32% of 139 quotes) as a usability aspect. Key design factors identified were: displaying solely important information in the field-of-view while adjusting it to the user's visual acuity, easy usage, simplicity, helpfulness and extensibility. User interaction was the second most mentioned (24% of 139 quotes) aspect. While multimodal interaction was considered feasible, no conclusions could be drawn on the most suitable combination of inputs. Nonetheless, most experts defined voice the most intuitive input. Based on the positive feedback from ESA astronauts and other experts, the AR-IoT proof of concept proved to be a potentially usable tool for future geological site inspection activities. The AR-IoT tool is therefore a promising asset for analogue training missions, such as Caves & Pangaea, and in the future for lunar geological field work. While limitations in both research and design are outlined, a set of recommendations aimed at warranting future testing and development of a more advanced AR-IoT tool for astronaut geological field activities is provided.

Mars is expected to become a focal point of exploration (human and robotic) in the near future. Extensive operations and continued human presence on the planet would require a robust space infrastructure. Be it navigation satellite constellations or scientific missions in low Mars orbits (LMO) and Areosynchronous orbits (ASO), every satellite would have a definitive period of operation after which it becomes derelict. At the end-of-life (EOL) the satellite could either be left unattended or dealt with in a sustainable manner. The first option is what has led to the problem of space debris in terrestrial orbits. To protect our access to Mars, proactive sustainability needs to be practised already in the design stages of such missions. This project aimed at providing graveyard orbit solutions in circummartian space for future Mars debris. 200-year period stability was studied for orbits using the symplectic integration technique. Extensive validations were performed and propagation settings were tuned to suit a variety of configurations. A plethora of graveyard orbit solutions were found and presented for orbits in ASO and LMO regimes. For example, it was found that transferring an ASO satellite to 400 km below the nominal orbit altitude would ensure a stability margin of +/-25 km for at least 200 years. The protected zones were found to be safe from debris even for an uncertainty in initial eccentricity of 0.01 and a tumbling satellite. Multiple orbital geometry orientations (combinations of semi-major axis, inclination, right ascension of the ascending node), satellite geometries (various values of area-to-mass ratios) and uncertainties were studied to produce a comprehensive analysis of long-term stability of potential graveyard orbits around Mars, making them attractive for such purposes.