Alfredo Martins

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. © 2017 IEEE.

Abstract
This paper presents the work performed in the implementation of an underwater simulation environment for the development of an autonomous underwater vehicle for the exploration of flooded underground tunnels. In particular, the implementation of a laser based structured light system, multibeam sonar and other robot details were addressed. The simulation was used as a relevant tool in order to study and specify the robot multiple sensors characteristics and placement in order to adequately survey a realistic environment. A detailed description of the research and development work is presented along with the analysis of obtained results and the benefits this work brings to the project. © 2017 IEEE.

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