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Presentation

Robotics in Industry and Intelligent Systems

At CRIIS, we work closely with Companies, other Institutes and Universities, following the motto from Research and Development to Innovation, Design, Prototyping and Implementation.

At our Centre, we address the following main research areas: Navigation and Localisation of Mobile Robots, Intelligent Sensors and Control of Dynamical Systems, 2D/3D Industrial Vision and Advanced Sensing, Mobile Manipulators, Special Structures and Architectures for Robots, Human Robot Interfacing and Augmented Reality, Future Industrial Robotics and Collaborative Robots, Vertical Integration, IoT, and Industry 4.0.

Latest News
Interest Topics
042

Featured Projects

PBA

Unified Framework for Mobile Robots

2018-2019

HORSE

Collaborative Robotics for Industrial Coating Cells

2018-2019

Robotica_ICARSC_2019

Conferência Robótica 2019 e ICARSC 2019

2018-2019

Refinação4.0

Serviços de consultoria especializada para desenvolvimento e operacionalização de provas de conceito

2018-2019

MetBots

Metabolomic robots with self-learning artificial intelligence for precision agriculture

2018-2020

SAFER

Safery verification for robotic software

2018-2021

COBOTIS

Human-robot interaction for collaborative robotics

2018-2021

COATING4.0

ANALYSIS AND DESIGN OF AN INNOVATIVE REMOTE PROCESS MANAGEMENT AND MONITORING SOLUTION (4.0)

2018-2019

FED

Ferradura, programação intuitiva para aplicações de soldadura robotizada

2018-2019

DIVA

Boosting innovative DIgitech Value chains for Agrofood, forestry and environment

2018-2021

FDControlo

Importância dos hospedeiros alternativos (plantas, insetos, vitis abandonada) na dispersão da doença da flavescência dourada (FD) da vinhda e das populações de scapholdeus titanus nas sub-regiões vitivinículas do Cávado e do Lima

2018-2022

DroneTool

Service to develop a prototype of a drone end-effector for leaf harvesting

2017-2019

Fasten

Flexible and Autonomous Manufacturing Systems for Custom-Designed Products

2017-2020

FAMEST

Footwear, Advanced Materials, Equipment's and Software Technologies

2017-2020

PRODUTECH_SIF

Soluções para a Indústria de Futuro

2017-2020

RIDDIG

Fábrica Digital

2017-2019

SistemaDPA

Sistema DPA para Espalhador de Estrume (sem ISOBUS) e Análise Química (NIR sensor) para Cisterna em Ambiente ISOBUS.

2017-2019

MANUFACTUR4.0

Desenvolvimento e implementação de tecnologias inteligentes e inovadoras nos setores industriais naval e metalomecânico

2017-2019

Water4Ever

Optimizing water use in agriculture to preserve soil and water resources

2017-2020

AGRINUPES

Integrated monitoring and control of water, nutrients and plant protection products towards a sustainable agricultural sector

2017-2020

Palcus

Sistema de Controlo de Máquinas Cénicas

2017-2019

ROMOVI

ROMOVI: Robô Modular e cooperativo para Vinhas de encosta

2017-2019

ScalABLE4.0

Scalable automation for flexible production systems

2017-2020

BIOTECFOR

Bionegócios e Tecnologia para a valorização eficiente dos recursos florestais endógenos no Norte de Portugal e Galiza

2017-2019

UnVirtual

Serviço de engenharia e desenvolvimento de robôs para jogo Unvirtual

2017-2018

GOTECFOR

Tecnologia para a mobilização e aproveitamento de Biomassa Florestal na agroindústria

2017-2020

DM4Manufacturing

DM4Manufacturing: Aligning Manufacturing Decision Making with Advanced Manufacturing Technologies

2016-2019

SmartFarming

Ferramenta avançada para operacionalização da agricultura de precisão

2016-2018

AdaptPack

Desenvolvimento de sistemas robóticos de paletização adaptativos e modulares de elevada flexibilidade

2016-2019

ATM

Advanced tools management

2016-2018

Inspectum

Sistema de Visão Artificial para Inspeção de Marcações Típicas em Pneus

2016-2017

TRiHo

RDH - Robot de Distribuição Hospitalar

2016-2019

PrecisionCork

PRECISIONcork - Medida e Controlo em Linha de Parâmetros Chave de Processo e de Qualidade de Produto

2016-2018

ColRobot

Collaborative Robotics for Assembly and Kitting in Smart Manufacturing

2016-2019

TEXTILPRINT

Desenvolvimento e programação de um novo sensor de resina, programação de cabeças de impressão, apoio ao desenvolvimento de software de monitorização e diagnóstico remoto a instalar nas máquinas de impressão textil

2016-2018

CoopWeld

Robótica colaborativa para soldadura de componentes em construção metálica

2015-2017

iMAN

iMAN - Intelligence for advanced Manufacturing systems

2015-2019

Submarino_Whale

Elaboração do projeto técnico de um dispositivo de controlo pneumático para equilíbrio hidrostático de um submersível

2015-2015

AutoClassII

Automatic Classification and Quality Control for Car Tires

2015-2018

FOCUS

Advances in FOrestry Control and aUtomation Systems in Europe

2014-2016

CLARISSA

The European Robotics Initiative for Strengthening the Competitiveness of SMEs in Manufacturing by integrating aspects of cognitive systems

2014-2016

STAMINA

Sustainable and reliable robotics for part handling in manufacturing automation

2013-2017

Team
003

Laboratories

Laboratory of Industrial Robotics and Automation

Laboratory of Mobile Robotics and Internal Logistics

Laboratory of Robotics for Agriculture and Forestry

Publications

CRIIS Publications

View all Publications

2019

Prototyping and Programming a Multipurpose Educational Mobile Robot - NaSSIE

Authors
Pinto, VH; Monteiro, JM; Gonçalves, J; Costa, P;

Publication
Robotics in Education - Advances in Intelligent Systems and Computing

Abstract

2019

Introduction to the Special Issue “Robotica 2016”

Authors
Cunha, B; Lima, J; Silva, M; Leitao, P;

Publication
Journal of Intelligent and Robotic Systems: Theory and Applications

Abstract

2019

Map-Matching Algorithms for Robot Self-Localization: A Comparison Between Perfect Match, Iterative Closest Point and Normal Distributions Transform

Authors
Sobreira, H; Costa, CM; Sousa, I; Rocha, L; Lima, J; Farias, PCMA; Costa, P; Paulo Moreira, AP;

Publication
Journal of Intelligent and Robotic Systems: Theory and Applications

Abstract
The self-localization of mobile robots in the environment is one of the most fundamental problems in the robotics navigation field. It is a complex and challenging problem due to the high requirements of autonomous mobile vehicles, particularly with regard to the algorithms accuracy, robustness and computational efficiency. In this paper, we present a comparison of three of the most used map-matching algorithms applied in localization based on natural landmarks: our implementation of the Perfect Match (PM) and the Point Cloud Library (PCL) implementation of the Iterative Closest Point (ICP) and the Normal Distribution Transform (NDT). For the purpose of this comparison we have considered a set of representative metrics, such as pose estimation accuracy, computational efficiency, convergence speed, maximum admissible initialization error and robustness to the presence of outliers in the robots sensors data. The test results were retrieved using our ROS natural landmark public dataset, containing several tests with simulated and real sensor data. The performance and robustness of the Perfect Match is highlighted throughout this article and is of paramount importance for real-time embedded systems with limited computing power that require accurate pose estimation and fast reaction times for high speed navigation. Moreover, we added to PCL a new algorithm for performing correspondence estimation using lookup tables that was inspired by the PM approach to solve this problem. This new method for computing the closest map point to a given sensor reading proved to be 40 to 60 times faster than the existing k-d tree approach in PCL and allowed the Iterative Closest Point algorithm to perform point cloud registration 5 to 9 times faster. © 2018 Springer Science+Business Media B.V., part of Springer Nature

2019

Optimal Perception Planning with Informed Heuristics Constructed from Visibility Maps

Authors
Pereira, T; Moreira, A; Veloso, M;

Publication
Journal of Intelligent and Robotic Systems: Theory and Applications

Abstract
In this paper we consider the problem of motion planning for perception of a target position. A robot has to move to a position from where it can sense the target, while minimizing both motion and perception costs. The problem of finding paths for robots executing perception tasks can be solved optimally using informed search. In perception path planning, the solution when considering a straight line without obstacles is used as heuristic. In this work, we propose a heuristic that can improve the search efficiency. In order to reduce the node expansion using a more informed search, we use the robot Approximate Visibility Map (A-VM), which is used as a representation of the observability capability of a robot in a given environment. We show how the critical points used in A-VM provide information on the geometry of the environment, which can be used to improve the heuristic, increasing the search efficiency. The critical points allow a better estimation of the minimum motion and perception cost for targets in non-traversable regions that can only be sensed from further away. Finally, we show the contributed heuristic with improvements dominates the base PA* heuristic built on the euclidean distance, and then present the results of the performance increase in terms of node expansion and computation time. © 2018 Springer Science+Business Media B.V., part of Springer Nature

2019

Online inspection system based on machine learning techniques: real case study of fabric textures classification for the automotive industry

Authors
Malaca, P; Rocha, LF; Gomes, D; Silva, J; Veiga, G;

Publication
Journal of Intelligent Manufacturing

Abstract
This paper focus on the classification, in real-time and under uncontrolled lighting, of fabric textures for the automotive industry. Many industrial processes have spatial constraints that limit the effective control of illumination of their vision based systems, hindering their effectiveness. The ability to overcome these problems using robust classification methods with suitable pre-processing techniques and choice of characteristics will increase the efficiency of this type of solutions with obvious production gains and thus economical. For this purpose, this paper studied and analyzed various pre-processing techniques, and selected the most appropriate fabric characteristics for the considered industrial case scenario. The methodology followed was based on the comparison of two different machine learning classifiers, ANN and SVM, using a large set of samples with a large variability of lightning conditions to faithfully simulate the industrial environment. The obtained solution shows the sensibility of ANN over SVM considering the number of features and the size of the training set, showing the better effectiveness and robustness of the last. The characteristics vector uses histogram equalization, Laws filter and Sobel filter, and multi-scale analysis. By using a correlation based method was possible to reduce the number of features used, achieving a better balanced between processing time and classification ratio. © 2016 Springer Science+Business Media New York

Supervised Theses

2017

Desenvolvimento de um controlador modular para LeanAGV baseado na norma IEC 61131-3

Author
Luís Tiago da Silva Costa

Institution
UP-FEUP

2017

SUCESSO DA GESTÃO DE PROJETOS DE DESENVOLVIMENTO DE SISTEMAS DE INFORMAÇÃO - O CASO DOS PROJETOS EM INSTITUIÇÕES PÚBLICAS

Author
Antero Manuel de Carvalho Gonçalves

Institution
UTAD

2017

Desenvolvimento de um robô de baixo custo para o concurso Micromouse

Author
Samir Pinto Mehmeti

Institution
UTAD

2017

Rede de sensores para o auxílio ao idoso dependente no Domicilio

Author
Vítor Manuel Caldeira Afonso

Institution
UTAD

2017

Análise da variabilidade da frequência cardíaca em indivíduos saudáveis e doentes

Author
Cristina Monteiro Pinto

Institution
UTAD

Facts & Figures

26PhDs

2016

313EU Programmes (k€)

2016

62Proceedings in indexed conferences

2016