This thesis presents the design, development, and experimental validation of a perching drone equipped with an underactuated robotic gripper and tactile sensing for grasping onto structures. Perching extends drone endurance for applications such as long-term monitoring, while tactile sensing enables precise alignment when visual data is unreliable. A control strategy combining position-based control with tactile feedback is implemented using DIGIT tactile sensors for contact-aware adjustments. Two per-pixel inference models convert RGB images into tactile information: a sensitive contact model for binary contact detection and a depth reconstruction model that estimates surface normals, which is then used to determine the contact surface orientation. After outlier filtering, the depth model achieves a mean absolute error of 5.32° in orientation estimation. Experiments demonstrate reliable grasping with up to 12 cm of position error and successful correction of both position and orientation using simulated tactile input. These results highlight the potential of tactile-based strategies for robust aerial manipulation in uncertain environments.

The final report from Delft University of Technology outlines the TurbEye project, focusing on the development of an autonomous drone designed for the inspection and maintenance of offshore wind turbines. The project was conducted by a team of students with the objective of creating a drone capable of performing maintenance checks through various Non-Destructive Testing (NDT) methods.

The project targeted the offshore wind energy market, which is growing rapidly compared to the more saturated onshore market. Offshore wind farms are increasingly significant due to their larger turbine sizes and greater capacity for energy production. These larger turbines require more efficient and automated inspection methods, which is where the TurbEye drone comes into play.

The TurbEye drone is designed to be fully autonomous, using a hydrogen propulsion system to achieve a longer range and endurance. The drone can cover a range of 280 kilometers and fly for up to 3.5 hours on a single hydrogen tank, inspecting up to six wind turbines per trip. This extended range and autonomy are crucial for reducing the high costs associated with manual inspections and human intervention, which currently dominate the industry.

The market analysis revealed that the offshore wind energy sector is expanding, with significant investments and a projected increase in capacity. The analysis also highlighted the need for innovative inspection solutions to manage the growing number and size of turbines. The TurbEye team identified a market gap for hydrogen-propelled autonomous drones, which offer advantages in terms of efficiency and cost-effectiveness over current manual and semi-automated inspection methods.

The drone’s system comprises several subsystems: structures, propulsion, control, and inspection. The propulsion system includes a hydrogen tank, fuel cell, backup battery, and propellers, selected for their safety and efficiency. The structural design uses lightweight and strong materials, ensuring the drone's robustness. The control system integrates advanced sensors and algorithms for autonomous navigation and inspection, while the inspection system employs a combination of visual cameras, passive thermography, and geometry inspection tools to detect surface and sub-surface damage.

Operationally, the drone follows a five-stage process during missions, from pre-flight checks and sensor calibration to data retrieval and refueling post-inspection. This systematic approach ensures the drone operates efficiently and safely. The planning of inspection routes is optimized using the Vehicle Routing Problem (VRP) algorithm, tested on the Hornsea 2 wind farm, the world’s largest. This optimization minimizes the number of trips and fuel consumption, enhancing operational efficiency.

AI algorithms are implemented to analyze the inspection data, detecting damage and differentiating between dirt and actual damage. These algorithms were trained on publicly available datasets but require further refinement due to limited training data.

The financial analysis estimates the production cost of a single drone at around 60,000 euros, driven mainly by the cost of the fuel cell, 3D scanner, and cameras. The financial projections suggest a total revenue of 9.77 million euros over five years, with a return on investment of 30.8%.

The project also emphasizes sustainability, aiming to minimize environmental impact through the use of green hydrogen and recycled materials, and aligning with the United Nations' Engineering for Sustainable Development framework.

Future steps involve further improvements in control systems, AI model reliability, detailed CAD designs, and dynamic simulations of the propulsion system. Extensive testing of subsystems and the complete system will follow before moving into production and operational phases. The ultimate goal is to attract customers and perform efficient and cost-effective wind turbine inspections, thereby reducing downtime and maintenance costs for offshore wind farms.