Space structures such as the International Space Station experience continuous degradation throughout their operational lifetime due to micrometeoroid impacts, radiation exposure, thermal cycling, and material ageing. Current external inspection approaches rely primarily on astronaut spacewalks and robotic manipulators, which introduce operational risks, require significant resources, and provide limited flexibility. As orbital infrastructure grows in scale and complexity, autonomous inspection systems become increasingly necessary.

This report presents the detailed design of Project EVE, an autonomous inspection system consisting of two free-flying drones designed to inspect large space structures using complementary non-destructive testing methods. The objective is to detect, localise, and characterise structural damage while reducing dependence on human intervention.

The selected architecture employs two specialised vehicles. Alpha performs rapid large-area inspection and identifies regions of interest, while Beta conducts detailed follow-up inspection. Together, the drones combine optical imaging, three-dimensional structured light, infrared thermography, and shearography to enable both surface and subsurface defect detection. To support detailed inspections in orbit, a deployable shading mechanism creates controlled thermal transients without dedicated heating hardware.

The final integrated design demonstrates that autonomous multi-drone inspection can provide a scalable, safer, and operationally flexible alternative to conventional inspection approaches for future long-duration space infrastructure. ... Read more

A portable, camera-based fiducial positioning approach intended for general contact NDT is presented and experimentally assessed, with phasedarray ultrasonic testing (PAUT) used as the demonstrator application. The positioning method employs inside-out tracking, with a camera rigidly mounted to the probe and a 3D fiducial reference object fixed in the inspection environment. The probe 6-DoF pose is obtained by estimating the camera pose and applying a pivot-based co-calibration to express the phased-array measurement in the fiducial frame. A holistic assessment is performed by (i) quantifying in-plane and out-of-plane translation repeatability and rotational repeatability over relevant working distances and viewing angles, (ii) characterising the camera-to-probe co-calibration accuracy, and (iii) propagating these contributions to an end-toend defect localisation error. Experiments show submillimeter translation repeatability within a practical operating range of 1.5–2.0 m, degrading to a few millimeters at∼3 m and for larger off-axis viewing angles. The pivot co-calibration achieves an RMS error of approximately 1.5 mm relative to a calibration jig. End-to-end validation on a GFRP plate demonstrates millimeter-scale defect localisation and sizing, with sub-millimetre in-plane shift relative to a reference C-scan. These results indicate that fiducial-based positioning provides accuracy sufficient for contact PAUT and constitutes a viable alternative to encoder-based tracking for portable NDT. ... Read more

This thesis investigates the feasibility of detecting dents on aircraft structures using drones. It was initiated in collaboration with Mainblades, a company specializing in aircraft inspections, and aims to integrate dent detection into its existing platform. Identifying dents for Maintenance, Repair, and Overhaul (MRO) operators is critical because their presence in aircraft structures can compromise structural integrity, negatively impact fatigue and aerodynamic performance, and dents could be accompanied by cracks due to excessive plastic deformation.

The study addresses this goal through a three-part approach. The first part focuses on identifying and adapting suitable sensors for drone-based inspection. Secondly, the thesis investigates the performance of 8tree’s dentCHECK sensor to understand the feasibility of drone-based deployment. The selected sensor, 8tree’s dentCHECK, was tested in controlled experiments using an ABB robotic arm to replicate realistic drone movements and evaluate sensor response under translational and angular disturbances. Finally, a custom machine learning algorithm is developed to classify dents from synthetic point clouds. The algorithm is designed to be drone-agnostic and capable of handling varying data quality from different sensors available on the market.

The results indicate that while structured light sensors, such as 8tree's dentCHECK, show significant promise for drone deployment, their performance is mainly limited by the drone's drift. For the vibrations, linear vibrations in the Z direction (i.e., vertical motion as the drone moves up and down) are critical, while angular vibrations for the roll and pitch were inconclusive. Nevertheless, despite not meeting Boeing's full tolerance criteria (± 0.05 mm for depth and ± 1mm for length and width) when airborne, the 8tree sensor significantly outperforms manual inspection methods in speed and measurement consistency, despite not yet meeting Boeing’s tolerance criteria when airborne. It is a good benchmark for identification purposes that can be complemented and characterized manually with handheld scanners.

At the other end, synthetic point cloud datasets incorporating mathematical and FEM-based dent geometries with Gaussian and robot-collected drone noise profiles were used to train a U-Net-based machine learning model for automatic dent segmentation. The developed machine learning model demonstrated reliable identification for dents larger than 0.5 mm, although further data diversity is needed to enhance length and width predictions. Together, these findings demonstrate that integrating dent detection into autonomous drone platforms is both feasible and promising, laying the groundwork for future refinements in drone stability and data-driven detection methods. ... Read more

Thermal Mathematical Models (TMMs) are used to predict the thermal behavior of satellite structures in orbit. However, due to inherent uncertainties in these models, physical testing is necessary to achieve reliable predictions. While these tests are critical, they often introduce uncontrolled uncertainties, such as heat leaks and measurement errors, making the correlation process complex and time-consuming. To address these challenges, this thesis proposes an alternative testing methodology that reduces uncertainties in thermal test data by using the phase shifts between temperature responses from oscillatory heat loads for TMM correlation. By comparing the measured and predicted phase shifts, thermal model parameters such as conductance or capacitance can be effectively correlated. The results demonstrate that this methodology is largely insensitive to uncontrolled conductive heat leaks, allowing the correlation process to focus only on key thermal parameters. This approach improves the reliability of thermal test data and streamlines the correlation process. Moreover, its potential application in ambient conditions offers a promising testing solution for early-phase model correlation. ... Read more

This report aims to detail the design process and final design for a Search and Rescue (SAR) drone that will search for victims under collapsed buildings. Firstly, the objectives and requirements for the drone system are given by the overall mission objectives. Then a detailed description of the design for each subsystem is presented. This is followed by an overview of the combined and integrated system. Subsequently, a performance analysis and a use case example of the system are given to indicate how the drone will perform during a mission.
Furthermore, the Reliability, Availability, Maintainability and Safety (RAMS) characteristics are displayed together with an overview of how sustainability has been integrated into the design. The plan for how further development and eventual production will be tackled is proposed. The
operational aspect is also discussed. The report finishes with an overall financial evaluation of the product and the main conclusions and recommendations found during the design process. The report is a continuation and overview of the work done and detailed in the Project Plan,
Baseline Report and Midterm Report by Bergmans et al. [2–4]... ... Read more

The Last Hope drone will autonomously find a clear path into the sky from the ground and ascend to an altitude of up to two thousand meters. Within 20 minutes it transmits a call for help with exact location information to rescue operators via the Iridium satellite network... ... Read more

This work is a part of the Automated Rotor Blade Inspection (ARBI) project. ARBI aims to improve the rotorcraft RTB process with novel rotor blade measurement equipment. The objective of this research is to investigate how the variation in structural properties of the rotor blades composite structure affects the dynamical response during the RTB process. It is hypothesized that the variation in stiffness and shift in the center of gravity of the structure is caused by in-service defects and repairs.
The results of the NDI inspections give a detailed overview of what type of non-uniformities are found in CH-47 rotor blades. A 2D analytical model is developed to quantify structural blade properties of blades that contain non-uniformities. This model provides the ability to determine the impact of these non-uniformities on the structural parameters based on NDI results and helps to improve the FEM model that is used for RTB simulation.
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Digital Image Correlation is a powerful tool with which full-field strains can be extracted from a series of digital images. If applied on the micro-scale of composites, the fiber-matrix interaction can be studied. To do so, different challenges have to be overcome. First, composite specimens have to be prepared for microscopy. Then, a speckle pattern for the DIC algorithm has to be applied, for which different methods are examined and optimized. Next, digital microscopic imaging systems are compared and characterized. Finally, a relation between parameters of the DIC algorithm (such as subset shape and size) and the accuracy is determined. ... Read more