Abstract
Offshore wind turbine application has been widespread in the last years, with an estimation that in 2030 will reach a total capacity of 234GW. Offshore wind farms introduce advantages in terms of environmental impact (noise, impact on birds, disrupted landscapes) and energy production (34% onshore and 43% offshore). Still, they also introduce scientific challenges in developing methodologies that allow wind farm inspection (preventive maintenance) safety for humans. This paper presents a UAV approach for autonomous inspection of inland windturbine and describes the field tests in Penela, Portugal. From the state-of-the-art available wind turbine inspection, in 2015, we carried out the first autonomous inspection with a UAV. The inspection of wind blades offshore is an ongoing project; therefore, the paper also presents the preliminary results with a simulation environment to validate the 3D LiDAR and the inspection procedure with new challenges effects: floating platform, wind gusts, and unknown initial blade position.

Abstract
This paper presents the results from search and rescue missions performed with the aerial robot OTUS in the the context of the ISEP/INESC TEC aerial robotics team participation on the euRathlon 2015 robotics competition. The multi-domain (land, sea and air) search and rescue scenario is described and technical solution adopted is presented with emphasis on the perception system. The calibration of the image based system is addressed. Results from the operational missions performed are also discussed. The aerial autonomous vehicle was able to successfully perform multiple tasks from the aerial reconnaissance and 3D mapping to the identification of leaking pipes, obstructed passages and missing workers. The system was validated a realistic operational scenario and won the Grand Challenge in cooperation with land and marine robotics partner teams. This challenge was the first time that a real time collaborative team of aerial, land and marine robots was deployed successfully in a search and rescue mission.

Abstract
The effective monitoring and maintenance of power lines are becoming increasingly important due to a global growing dependence on electricity. The costs and risks associated with the traditional foot patrol and helicopter-based inspections can be reduced by using UAVs with the appropriate sensors. However, this implies developing algorithms to make the power line inspection process reliable and autonomous. In order to overcome the limitations of visual methods in the presence of poor light and noisy backgrounds, we propose to address the problem of power line detection and modeling based on LiDAR. The PL2DM, Power Line LiDAR-based Detection and Modeling, is a novel approach to detect power lines. Its basis is a scan-by-scan adaptive neighbor minimalist comparison for all the points in a point cloud. The power line final model is obtained by matching and grouping several line segments, using their collinearity properties. Horizontally, the power lines are modeled as a straight line, and vertically as a catenary curve. Using a real dataset, the algorithm showed promising results both in terms of outputs and processing time, adding real-time object-based perception capabilities for other layers of processing.

Abstract
The growing dependence of modern-day societies on electricity increases the importance of effective monitoring and maintenance of power lines. Endowing UAVs with the appropriate sensors for inspecting power lines, the costs and risks associated with the traditional foot patrol and helicopter-based inspections can be reduced. However, this implies the development of algorithms to make the inspection process reliable and autonomous. Visual methods are usually applied to locate the power lines and their components, but poor light conditions or a background rich in edges may compromise their results. To overcome those limitations, we propose to address the problem of power line detection and modeling based on LiDAR. A novel approach to the power line detection was developed, the PL2DM -Power Line LiDAR-based Detection and Modeling. It is based in a scan-by-scan adaptive neighbor minimalist comparison for all the points in a point cloud. The power line final model is obtained by matching and grouping several line segments, using their collinearity properties. Horizontally, the power lines are modeled as a straight line, and vertically as a catenary curve. The algorithm was validated with a real dataset, showing promising results both in terms of outputs and processing time, adding real-time object-based perception capabilities for other layers of processing.

Abstract
It is commonly accepted that one of the most important factors for assuring the high performance of an electrical network is the surveillance and the respective preventive maintenance. From a long time ago that TSOs and DSOs incorporate in their maintenance plans the surveillance of the grid, where is included the aerial power lines inspection. Those inspections started by human patrol, including structure climbing when needed and later were substituted by helicopters with powerful sensors and specialised technicians. More recently the Unmanned Aerial Vehicles (UAV) technology has been used, taking advantage of its numerous advantages. This paper addresses the problem of improving the real-time perception capabilities of UAVs for endowing them with capabilities for safe and robust autonomous and semi-autonomous operations. It presents a new vision based power line detection algorithm denoted by PLineD, able to improve the detection robustness even in the presence of image with background noise. The algorithm is tested in real outdoor images of a dataset with multiple backgrounds and weather conditions. The experimental results demonstrate that the proposed approach is effective and able to implemented in real-time image processing pipeline.