Cookies Policy
The website need some cookies and similar means to function. If you permit us, we will use those means to collect data on your visits for aggregated statistics to improve our service. Find out More
Accept Reject
  • Menu
About
Download Photo HD

About

André Dias was born in Porto, Portugal 1980. He finished is lic. degree in Electrical and Electronic Engineering from ISEP Porto Polytechnic School in 2004. He pursue further studies and obtained his Master in Electronics and Computers Engineering, from IST University of Lisbon in 2008. In 2015 graduated (Phd) in Electronics and Computers Engineering, from IST University of Lisbon.
He currently is a professor at the School of Engineering (ISEP) of the Porto Polytechnic Institute (IPP) and senior researcher at the robotics and autonomous systems group of INESC TEC in Portugal, where he is project member in several international FP7, H2020 projects. He is the main author of several research publications in the domains of perception and mobile robotics applications.

Interest
Topics
Details

Details

010
Publications

2021

Emergency Landing Spot Detection Algorithm for Unmanned Aerial Vehicles

Authors
Loureiro, G; Dias, A; Martins, A; Almeida, J;

Publication
Remote Sensing

Abstract
The use and research of Unmanned Aerial Vehicle (UAV) have been increasing over the years due to the applicability in several operations such as search and rescue, delivery, surveillance, and others. Considering the increased presence of these vehicles in the airspace, it becomes necessary to reflect on the safety issues or failures that the UAVs may have and the appropriate action. Moreover, in many missions, the vehicle will not return to its original location. If it fails to arrive at the landing spot, it needs to have the onboard capability to estimate the best area to safely land. This paper addresses the scenario of detecting a safe landing spot during operation. The algorithm classifies the incoming Light Detection and Ranging (LiDAR) data and store the location of suitable areas. The developed method analyses geometric features on point cloud data and detects potential right spots. The algorithm uses the Principal Component Analysis (PCA) to find planes in point cloud clusters. The areas that have a slope less than a threshold are considered potential landing spots. These spots are evaluated regarding ground and vehicle conditions such as the distance to the UAV, the presence of obstacles, the area’s roughness, and the spot’s slope. Finally, the output of the algorithm is the optimum spot to land and can vary during operation. The proposed approach evaluates the algorithm in simulated scenarios and an experimental dataset presenting suitability to be applied in real-time operations.

2021

Improving the preparedness against an oil spill: Evaluation of the influence of environmental parameters on the operability of unmanned vehicles

Authors
Bernabeu, A; Plaza-Morlote, M; Rey, D; Almeida, M; Dias, A; Mucha, A;

Publication
Marine Pollution Bulletin

Abstract

2020

Teaching robotics with a simulator environment developed for the autonomous driving competition

Authors
Fernandes, D; Pinheiro, F; Dias, A; Martins, A; Almeida, J; Silva, E;

Publication
Advances in Intelligent Systems and Computing

Abstract
Teaching robotics based on challenge of our daily lives is always more motivating for students and teachers. Several competitions of self-driving have emerged recently, challenging students and researchers to develop solutions addressing the autonomous driving systems. The Portuguese Festival Nacional de Robótica (FNR) Autonomous Driving Competition is one of those examples. Even though the competition is an exciting challenger, it requires the development of real robots, which implies several limitations that may discourage the students and compromise a fluid teaching process. The simulation can contribute to overcome this limitation and can assume an important role as a tool, providing an effortless and costless solution, allowing students and researchers to keep their focus on the main issues. This paper presents a simulation environment for FNR, providing an overall framework able to support the exploration of robotics topics like perception, navigation, data fusion and deep learning based on the autonomous driving competition. © Springer Nature Switzerland AG 2020.

2019

ISEP/INESC TEC Aerial Robotics Team for Search and Rescue Operations at the euRathlon 2015

Authors
Sousa, P; Ferreira, A; Moreira, M; Santos, T; Martins, A; Dias, A; Almeida, J; Silva, E;

Publication
Journal of Intelligent and Robotic Systems: Theory and Applications

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. © 2018 Springer Science+Business Media B.V., part of Springer Nature

2019

LiDAR-Based Real-Time Detection and Modeling of Power Lines for Unmanned Aerial Vehicles

Authors
Azevedo, F; Dias, A; Almeida, J; Oliveira, A; Ferreira, A; Santos, T; Martins, A; Silva, E;

Publication
Sensors

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 PL 2 DM, 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.

Supervised
thesis

2021

Behavior Tree UAV Mission Control in Warehouse Logistics

Author
ANDRÉ FILIPE OLIVEIRA MOURA

Institution
IPP-ISEP

2021

Sistema de Visão Computacional Low-Cost para Deteção e Contagem de Pessoas e Veículos em Smart Cities

Author
MIGUEL MENDES AMADO

Institution
IPP-ISEP

2021

Graph-SLAM Approach for Indoor UAV Localization in Warehouse Logistics Applications

Author
JOSÉ FILIPE DA SILVA OLIVEIRA ANTUNES

Institution
IPP-ISEP

2021

Benchmark de Sistemas Embebidos para Machine Learning em Visão Computacional

Author
MIGUEL ÂNGELO LOURENÇO LOPES

Institution
IPP-ISEP

2021

Sistema de Visão Termográfico para Veículos Autónomos Aéreos

Author
CARLOS MANUEL DE SOUSA FERRÁS

Institution
IPP-ISEP