Robotics
Work description
The present work aims to develop an integrated methodology for the inspection of offshore wind turbines using an unmanned VTOL (Vertical Take-Off and Landing) aerial platform, combining vision systems, thermography, LiDAR, and tomography-based techniques. One of the central aspects of the work will be the development of hybrid inspection methods that exploit, in a complementary manner, the characteristics of fixed-wing aircraft and multirotor platforms. The objective is to benefit from the greater endurance, speed, and energy efficiency of fixed-wing flight, while combining these advantages with the hovering capability, high manoeuvrability, and positioning accuracy of a quadrotor-type platform. The methodology should enable the flight mode to be adapted to the different stages of the inspection process, using fixed-wing operation for transit, approach, and large-scale survey tasks, and multirotor operation for high-precision local inspections near the blades, tower, and nacelle. The proposed solution should also be robust to GNSS degradation or loss of positioning quality, which may occur during operations close to large structures. For this purpose, a state estimator will be developed to fuse information from Visual-Inertial Odometry, LiDAR, and other navigation sensors, improving the relative positioning of the VTOL with respect to the wind turbine and ensuring greater accuracy, continuity, and safety during inspection operations. The different sensing systems should be integrated to support the detection and characterization of surface, thermal, geometric, and internal defects. It will also be necessary to develop trajectory planning, positioning, and data acquisition strategies capable of reconciling sensor requirements with the aerodynamic and operational constraints of the platform. A key requirement will be to minimize, as much as possible, the period during which the nacelle must remain stopped or locked. To this end, fast and adaptive inspection strategies should be investigated, combining global surveys with detailed inspections focused only on identified regions of interest. The work should culminate in the definition and validation of a multimodal inspection methodology based on a hybrid VTOL platform, including operational modes, robust state estimation, trajectory planning, sensor integration, data acquisition and fusion, and criteria for assessing the structural condition of the wind turbine components
Academic Qualifications
Master's degree in Electrical and Computer Engineering.
Minimum profile required
• Hold a Master’s degree in Electrical and Computer Engineering or a related scientific field, preferably with a specialization in Autonomous Systems or a related area, and possess a scientific and professional background demonstrating a profile suitable for the activities to be carried out.• Not have previously been awarded a doctoral grant or a doctoral grant in companies directly funded by FCT, regardless of its duration.• Reside permanently and habitually in Portugal at the start date of the period of the work plan to be carried out abroad, should the proposed work plan include a period at foreign institutions (mixed grants). This requirement applies to both Portuguese and foreign citizens.• Not hold a doctoral degree.
Preference factors
• Academic background in Electrical Engineering, particularly with a specialization in Autonomous Systems; • Previous experience in the development of VTOL robotic platforms, multirotors and/or fixed-wing aircraft; • Knowledge of navigation, localization, state estimation, GNSS, IMU, Visual-Inertial Odometry, LiDAR, and sensor fusion techniques; • Knowledge of computer vision, image processing, thermography, SLAM, and navigation in GNSS-degraded or GNSS-denied environments; • Programming skills, preferably in C/C++ and/or Python, as well as experience with ROS/ROS 2 and simulation tools for robotic systems; • Experience in experimental development, sensor integration, and testing of autonomous platforms; • Scientific publications in international conferences and/or journals in the areas of autonomous aerial vehicles, aerial robotics, autonomous navigation, perception, and sensor fusion; • Interest in robotic applications for the inspection, maintenance, and monitoring of offshore infrastructures; • Autonomy, problem-solving skills, and strong motivation for research and development activities.
Application Period
Since 01 Oct 2026 to 15 Oct 2026
Centre
Robotics and Autonomous Systems
Scientific Advisor
André Dias