Due to the increasing interest of the aerospace industry and the scientific community in missions targeting halo orbits, a specific family of periodic solutions within the circular restricted three-body problem, it is highly desirable to find ways to reduce the cost of transfers between these orbits. To achieve this, depending on the mission characteristics, one could opt for reducing the required propellant mass or the time of flight. As such, with two objectives to be minimized, an ideal implementation for a mission designer would provide a Pareto front of feasible transfers instead of the single trajectory commonly obtained with conventional methods. Moreover, the necessary propellant mass can be minimized even further by means of electric low-thrust propulsion due to the significantly larger exhaust velocities. Therefore, the purpose of this research is to develop, implement, and study a suitable approach to obtain a collection of low-thrust transfers between halo orbits optimized in terms of both propellant mass and time of flight.

The approach employs an optimal control indirect method as thrust law which, combined with a heuristic optimizer based on differential evolution, can find a wide variety of trajectories that minimize the aforementioned objectives within the circular restricted three-body problem. Heuristic optimization is employed to remove the dependency of the solution on the provided initial guess and find trajectories in the region of the global minima. To satisfy the demanding boundary conditions characteristic of indirect methods, these constraints are included as a third objective for the optimizer to minimize as well. Due to the tolerance allowed on the constraints, the trajectories are subsequently refined with direct collocation methods. Then, they can be transitioned to a high-fidelity model, taking advantage of the versatility of direct methods. Furthermore, the implementation allows for the inclusion of invariant manifold phases arising from the departure and target orbits to obtain a wider set of Pareto-optimal solutions.

To assess the suitability of the proposed procedure, a specific transfer between two halo orbits around different Lagrange points of the Earth-Moon system was optimized. The results consisted of 100 Pareto-optimal transfers spanning more than 60 days, offering significant mission design freedom. Moreover, the Pareto front outperforms the trajectory found in the literature for a comparable use case by 30\% in all objectives. Next, an optimized trajectory was first successfully verified with the mission analysis software ASTOS and then refined with direct collocation. The refined trajectory exhibited negligible changes in performance, rendering the trajectories obtained with this approach as promising initial guesses for further optimization with direct collocation methods. Moreover, several major perturbations not included in the simplified dynamic system were also corrected with direct collocation. The next steps include: accounting for the eccentricity of the Moon's orbit to fully transition the trajectories to a high-fidelity model, assessing the optimization quality with different use cases, and implementing transfers between different periodic solutions and dynamic systems, such as the Sun-Earth system.

Cislunar space is getting increasingly important. Countries like the US and China are directing their attention towards the Moon with the Artemis and Chang’e missions. Simultaneously, space debris pollution of our orbits is escalating, increasing the risk of the Kessler syndrome occurring. Space Situational Awareness (SSA) aims to prevent this. The sheer amount of space debris makes continuous tracking unfeasible. This creates the need for accurate orbit determination and propagation between observations. Model frameworks have been developed extensively for space debris in near-Earth orbits, but there is little experience with cislunar space. This region is more challenging because of its unstable nature, increasing the risk of losing track of objects.

In this research, a model framework is developed, using the open-source Python package Tudat, that can estimate and propagate long-term cislunar space debris orbits accurately from optical data, whilst quantifying the uncertainties realistically over time using Monte Carlo simulation. Orbit determination has been performed using Weighted Least-Squares. The algorithm can estimate (amongst other parameters) initial states of the objects, model parameters like radiation pressure properties, and observation bias. We apply our framework to the Chang’e 2 and 3 upper stages. A selection of 13 estimation windows has allowed for analysis on a diverse range of orbital and observation characteristics. Moreover, the effect of close Moon approaches on orbit determination quality and propagation accuracy has been investigated.

A generic model framework has been found that achieves sufficient accuracy with reasonable computational load for all 13 use cases and can be used as a foundation for other cislunar space debris studies. The generic framework only estimates initial state, and uses a relatively simple dynamical model and integrator configuration. This allows sufficiently accurate propagation up to 2 years out-of-sample, depending strongly on the stability of the orbit. Afterwards, the generic model framework is tailored on individual use cases to improve its performance. Parameters like the radiation pressure coefficient and observation bias are estimated in this process. The tailored model framework improved performance for 8 out of 13 use cases (compared to the generic model framework), decreasing out-of-sample RMSE between 20-95% and increasing period of sufficient accuracy with up to 250 days. Estimating on 7-10 months of observations results in the best orbit determination quality and propagation accuracy for the cislunar use cases. The tailored model framework performs robustly for various non-linear orbits, except for out-of-sample close Moon approaches. Several solutions are proposed to solve this issue. Finally, it is found that the effect of uncertainty for cislunar space debris orbits over time is significant in the current framework. Uncertainty over time is especially large when estimating on short estimation windows (<4 months) and for orbits experiencing non-linear behaviour.

The congestion of orbits around the Earth, particularly in Low Earth Orbit (LEO), necessitates the implementation of space-based Space Situational Awareness (SSA) missions. This increasing orbital congestion, driven by the proliferation of mega constellations, poses significant challenges to satellite operations and collision avoidance. In response, commercial entities are beginning to plan and launch space-based SSA missions. Despite this emerging interest, there is a notable gap in the literature regarding the scheduling of operations for these missions, which are characterised by the inclusion of multiple operational modes.

Additionally, existing literature on operational scheduling often lacks high-fidelity modelling of onboard resource dynamics. This research addresses this gap by focusing on energy-constrained scenarios, developing novel operational scheduling frameworks suitable for assigning data collection and data downlink tasks for optical space-based SSA missions under such constraints.

To enhance the fidelity of the scheduling process, this research employs both low-fidelity and high-fidelity simulators to model the dynamic behaviour of onboard resources. The developed frameworks are tested in various scenarios to validate their practicality and effectiveness. A significant contribution of this research is the open-source availability of part of the developed scheduling frameworks, utilising software accessible under the TU Delft license. This open-source approach ensures that the research can be extended and built upon by future researchers, fostering ongoing advancements in the field of space-based SSA operations.

Performing crucial activities for space exploration, e.g., debris removal and on-orbit servicing, systems for Rendezvous and Proximity Operations (RPO) are required to be autonomous and scalable. Within this context, learning-based relative navigation has gained significant traction due to the latest advancements in AI and the cost-effectiveness of monocular cameras.

This thesis introduces a pose estimation system composed of an Object Detection (OD) and a Keypoint Detection (KD) network for keypoint extraction, coupled with a pose solver, and trained purely on synthetic images. When applying realistic data augmentations, the system achieves a reduction in KD error by 80%, further improved by training on photorealistic images. After an architecture optimization step, the final system consistently meets the inference time requirements on the Myriad X edge processor. This proves the feasibility of developing RPO systems with artificial data, showcasing a scalable approach, while complying with the limitations of onboard hardware.

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.

Single-Stage-To-Orbit (SSTO) launch vehicles are capable of reaching orbit while being potentially fully reusable. A technology capable of enabling SSTO access to space is the air-breathing engine, which has a high specific impulse and low thrust-over-weight (T/W) ratio compared to conventional rocket engines. This necessitates the use of a Horizontal Take-off and Horizontal Landing (HTHL) launch vehicle with a lifting surface, which enables banking maneuvers that can manipulate the orbital Right Ascension of the Ascending Node (RAAN) that the space plane achieves. This means that the space plane is capable of advancing or delaying its time of launch, while still achieving a similar orbital RAAN. This effectively increases the launch window even if a precise orbit insertion is necessary.

This thesis answers the question what the fuel-optimal ascent trajectory is for a space plane that would extend its launch window. In order to answer this question, the full six DoF equations of motion have been defined to simulate the ascent trajectory of a space plane. Additionally, a robust Guidance and Control system is developed that can use the nonlinear translational and rotational equations of motion.

For several launch times, ranging from two hours earlier to two hours later than the nominal launch time, the ascent trajectory has been optimized that included a banking maneuver. It was found that at the cost of propellant, it is possible to have a launch time delayed are advanced by as much as two hours, which means that the space plane can manipulate the RAAN by ± 30 degrees. The extra propellant cost can be related to increased drag losses during the banking maneuver.

Interplanetary missions have relied on Radio Science (RS) for recovering gravity fields via detecting their signature on the spacecraft’s trajectory. Yet the weak gravitational fields of small bodies, coupled with the prominent influence of confounding accelerations, hinder the efficacy of this method. Meanwhile, quantum sensors based on Cold Atom Interferometry (CAI) have demonstrated absolute measurements with an inherent stability and repeatability, reaching the utmost accuracy in microgravity. This work addresses the potential of CAI-based Gradiometry (CG) as a means to strengthen the RS gravity experiment for small-body missions. Phobos represents an ideal science case as astronomic observations and recent flybys have conferred enough information to define a robust orbiting strategy, whilst promoting studies linking its geodesic observables to its origin. A covariance analysis was adopted to evaluate the contribution of RS and CG in the gravity field solution, for a mission duration of one week.

The favourable observational geometry and the small characteristic period of the gravity signal add to the competitiveness of Doppler observables. Provided that empirical accelerations can be modelled below the nm/s2 level, RS is able to infer the 6x6 spherical harmonic spectrum to an accuracy of 0.1-1% w.r.t. the homogeneous interior values. If this correlates to a density anomaly beneath the Stickney crater, RS would suce to constrain Phobos’ origin. Yet, in event of a rubble pile or icy moon interior (or a combination thereof) CG remains imperative, enabling an accuracy below 0.1% for most of the 10x10 spectrum. Nevertheless, technological advancements will be needed to alleviate the current logistical challenges associated with CG operation. This work also reflects on the sensitivity of the candidate orbits with regards to dynamical model uncertainties, which are common in small-body environments. This brings confidence in the applicability of the identified geodetic estimation strategy for missions targeting other moons, particularly those of the Giant planets, which are targets for robotic exploration in the coming decades.

What is the most advantageous magnetic attitude control torquer for reducing angular rates of a PocketQube? To answer this question several magnetorquer concepts are explored and two concepts are built. In addition, a method to test these magnetorquers is developed. An Air-core magnetorquer system and a magnetorquer embedded in a PCB are designed, built and tested. By building and testing the designs also possible construction issues related to a concept can be found, which are not evident when only designing the magnetorquers. The Air-core magnetorquers also serves as a prototype for the magnetorquer for Delfi-PQ, the satellite to be launched by Delft University of Technology. After testing two concepts are then compared with each other and also with other concepts such as a ferromagnetic torque rod.

The control of aircraft can be carried out by Reinforcement Learning agents; however, the difficulty of obtaining sufficient training samples often makes this approach infeasible. Demonstrations can be used to facilitate the learning process, yet algorithms such as Apprenticeship Learning generally fail to produce a policy that outperforms the demonstrator, and thus cannot efficiently generate policies. In this paper, a model-free learning algorithm with Reinforcement Learning in the loop, based on Apprenticeship Learning, is therefore proposed. This algorithm uses external measurement to improve on the initial demonstration, finally producing a policy that surpasses the demonstration. Efficiency is further improved by utilising the policies produced during the learning process. The empirical results for simulated quadrotor control show that the proposed algorithm is effective and can even learn good policies from a bad demonstration.

The use of Taylor Series Integration (TSI) for the propagation of spacecraft trajectories and its combination with the relatively unknown Unified State Model (USM) coordinate system, resulting in the TSI-USM propagator, is known to allow fast propagation of interplanetary low-thrust trajectories. Unfortunately, the TSI-USM propagator is a problem-specific method, which is detrimental for its use in evolutionary algorithms.

In this thesis, a method was developed to generalize the TSI-USM propagator. The TSI-USM was generalized to allow the propagation of a spacecraft trajectory in a central gravitational field subject to any pre-set thrust profile, without the need to adapt the internal working of the propagator. The three-dimensional thrust profile should be specified a priori, and with respect to time, by the user or by an evolutionary algorithm. The method used to generalize the TSI-USM requires the coefficients of the cubic spline interpolation of the pre-set thrust profile to compute the recurrence relations of the TSI-USM.

Results show that, although the method indeed generalizes the TSI-USM, it is less accurate and requires more CPU time for a given accuracy than existing RK8(7)13M-based propagators. From this result, it can be expected that future attempts to generalize the TSI-USM propagator, without sacrificing its accuracy and computational speed, will be challenging.