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.

Many robotic systems rely on laser ranging sensors to navigate and map their environment. As robots are getting more advanced, smaller and cheaper, their sensors need to decrease in size and cost, while increasing their reliability and accuracy. Current laser-based sensors have difficulty to meet these properties. They are either large, expensive, contain moving parts or provide insufficient amounts of information.

This thesis introduces an integrated approach for the development of an eye-safe, low-cost, solid-state, wide-angle line-laser distance sensor, tackling these challenges. A prototype is derived by constructing a generic, camera independent, triangulation model. The model, combined with a set of pre-defined requirements, is used to select hardware components and predict the sensor limits. A unified calibration step is proposed to estimate both camera intrinsics as well as misalignment errors with the same set of data. Additionally, a microsecond-accurate open loop synchronization system for laser activation and imager exposure is presented.

Tests show that the prototyped sensor is able to measure distances up to 3 meters with an error of 4%. At 2 meter, the error is just under 2%. Additionally, the solid-state prototype has a field-of-view of 105 degrees, an angular resolution of 0.8 degrees, an update rate of 10Hz and an estimated cost of just below $35.