2020
Autores
Claro, R; Silva, R; Pinto, A;
Publicação
GLOBAL OCEANS 2020: SINGAPORE - U.S. GULF COAST
Abstract
This paper presents an algorithm for mapping monopiles from Offshore Wind Farms (OWF). The ASV (Autonomous Surface Vehicle) surveys the environment, detects and localizes monopiles using situational awareness system based on LiDAR, GPS and IMU (Inertial Measurement Unit) data. The position of the monopile is obtained based on the relative localization between the extrapolated center of the structure that was detected and the ASV. A positive detection of a monopile is referenced to a global positioning frame based on the GPS. Results in a simulator environment demonstrate the ability of this situational awareness system to identify monopiles with a precision of 0.005 m, which is relevant for detecting structural disalignments over time that might be caused by the appearance of scour in the structure's foundation.
2020
Autores
Pinto, M; Zajzon, N; Lopes, L; Bodo, B; Henley, S; Almeida, J; Aaltonen, J; Rossi, C; Zibret, G;
Publicação
Abstract
2020
Autores
Barbosa, S; Camilo, M; Almeida, C; Almeida, J; Amaral, G; Aplin, K; Dias, N; Ferreira, A; Harrison, G; Heilmann, A; Lima, L; Martins, A; Silva, I; Viegas, D; Silva, E;
Publicação
Abstract
2020
Autores
Loureiro, G; Dias, A; Martins, A;
Publicação
Robots in Human Life- Proceedings of the 23rd International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines, CLAWAR 2020
Abstract
For the past years, the interest in the use of Unmanned Aerial Vehicles (UAVs) has been increasing due to the multiple research topics provided by the field of aerial robotics. Conversely, vehicles are susceptible to failures or malfunctions. Consequently, one main emergent research topic is the detection of a safe landing spot in these emergency scenarios. Therefore, this paper exposes and details the multiple techniques that attempt to solve the problem of landing site detection. This paper aims to present the current literature with several sensors that can be used to solve the aforementioned problem. Finally, the paper presents our proposed approach with some preliminary results in simulation. © CLAWAR Association Ltd.
2020
Autores
Loureiro, G; Soares, L; Dias, A; Martins, A;
Publicação
FOURTH IBERIAN ROBOTICS CONFERENCE: ADVANCES IN ROBOTICS, ROBOT 2019, VOL 2
Abstract
This paper addresses the topic of emergency landing spot detection for Unmanned Aerial Vehicles (UAVs). During operation, the vehicle is susceptible to faults and must be able to predict the land spot able to ensure that the UAV will be able to land without damages and injuries to humans and structures. A method was developed, based on geometric features extracted from Light Detection And Ranging (LIDAR) data. A simulation environment was developed in order to validate the effectiveness and the robustness of the proposed method.
2020
Autores
Magalhães, C; Martins, A; Santos, AD;
Publicação
Zooplankton Ecology
Abstract
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