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Detalhes

Detalhes

  • Nome

    Tiago André Santos
  • Cargo

    Assistente de Investigação
  • Desde

    01 junho 2015
Publicações

2023

GeoTec: A System for 3D Reconstruction in Underground Environment (Aveleiras Mine, Monastery of Tibães, NW Portugal)

Autores
Pires, A; Dias, A; Rodrigues, P; Silva, P; Santos, T; Oliveira, A; Ferreira, A; Almeida, J; Martins, A; Chaminé, I; Silva, E;

Publicação
Advances in Science, Technology and Innovation

Abstract

2023

Simulation Environment for UAV Offshore Wind-Turbine Inspection

Autores
Oliveira, A; Dias, A; Santos, T; Rodrigues, P; Martins, A; Silva, E; Almeida, J;

Publicação
OCEANS 2023 - LIMERICK

Abstract
Offshore wind farms are becoming the main alternative to fossil fuels and the future key to mitigating climate change by achieving energy sustainability. With favorable indicators in almost every environmental index, these structures operate under varying and dynamic environmental conditions, leading to efficiency losses and sudden failures. For these reasons, it's fundamental to promote the development of autonomous solutions to monitor the health condition of the construction parts, preventing structural damage and accidents. This paper introduces a new simulation environment for testing and training autonomous inspection techniques under a more realistic offshore wind farm scenario. Combining the Gazebo simulator with ROS, this framework can include multi-robots with different sensors to operate in a customizable simulation environment regarding some external elements (fog, wind, buoyancy...). The paper also presents a use case composed of a 3D LiDAR-based technique for autonomous wind turbine inspection with UAV, including point cloud clustering, model estimation, and the preliminary results under this simulation framework using a mixed environment (offshore simulation with a real UAV platform).

2023

Methodological insights from unmanned system technologies in a rock quarry environment and geomining heritage site: coupling LiDAR-based mapping and GIS geovisualisation techniques

Autores
Pires, A; Dias, A; Silva, P; Ferreira, A; Rodrigues, P; Santos, T; Oliveira, A; Freitas, L; Martins, A; Almeida, J; Silva, E; Chaminé, HI;

Publicação
Arabian Journal of Geosciences

Abstract

2022

Unmanned Aerial Vehicle for Wind-Turbine Inspection. Next Step: Offshore

Autores
Dias, A; Almeida, J; Oliveira, A; Santos, T; Martins, A; Silva, E;

Publicação
2022 OCEANS HAMPTON ROADS

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.

2021

Hyperspectral Imaging System for Marine Litter Detection

Autores
Freitas, S; Silva, H; Almeida, C; Viegas, D; Amaral, A; Santos, T; Dias, A; Jorge, PAS; Pham, CK; Moutinho, J; Silva, E;

Publicação
OCEANS 2021: SAN DIEGO - PORTO

Abstract
This work addresses the use of hyperspectral imaging systems for remote detection of marine litter concentrations in oceanic environments. The work consisted on mounting an off-the-shelf hyperspectral imaging system (400-2500 nm) in two aerial platforms: manned and unmanned, and performing data acquisition to develop AI methods capable of detecting marine litter concentrations at the water surface. We performed the campaigns at Porto Pim Bay, Fail Island, Azores, resorting to artificial targets built using marine litter samples. During this work, we also developed a Convolutional Neural Network (CNN-3D), using spatial and spectral information to evaluate deep learning methods to detect marine litter in an automated manner. Results show over 84% overall accuracy (OA) in the detection and classification of the different types of marine litter samples present in the artificial targets.