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Sobre

Sobre

José Lima obteve o grau de Licenciado, de Mestre e de Doutor em Engenharia Eletrotécnica e de Computadores pela Faculdade de Engenharia da Universidade do Porto em 2001, 2004 e 2009, respetivamente. Atualmente é Professor adjunto do Departamento de Eletrotecnia da Escola Superior de Tecnologia e de Gestão no Instituto Politécnico de Bragança onde leciona unidades curriculares como Eletrónica de Potência, Microprocessadores, Conversores de Energia e Eletrónica. É também investigador no Centro de Robótica Industrial e Sistemas Inteligentes (CRIIS) do INESC TEC no Porto. Tem publicados cerca de 60 artigos científicos em revistas e conferências internacionais. É investigador de diversos projetos de investigação europeus assim como nacionais. Possui experiência no desenvolvimento de aplicações industriais. As suas áreas de interesse são: simulação, localização e navegação de robôs, sistemas embebidos e visão artificial.

Tópicos
de interesse
Detalhes

Detalhes

010
Publicações

2022

Realistic 3D Simulation of a Hybrid Legged-Wheeled Robot

Autores
Soares, IN; Pinto, VH; Lima, J; Costa, P;

Publicação
ROBOTICS FOR SUSTAINABLE FUTURE, CLAWAR 2021

Abstract
In order to study the behavior and performance of a robot, building its simulation model is crucial. Realistic simulation tools using physics engines enable faster, more accurate and realistic testing conditions, without depending on the real vehicle. By combining legged and wheeled locomotion, hybrid vehicles are specially useful for operating in different types of terrains, both indoors and outdoors. They present increased mobility, versatility and adaptability, as well as easier maneuverability, when compared to vehicles using only one of the mechanisms. This paper presents the realistic simulation through the SimTwo simulator software of a hybrid legged-wheeled robot. It has four 3-DOF (degrees of freedom) legs combining rigid and non-rigid joints and has been fully designed, tested and validated in the simulated environment with incorporated dynamics. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

2022

Design and Modelling of a Modular Robotic Joint

Autores
Rocha, M; Pinto, VH; Lima, J; Costa, P;

Publicação
ROBOTICS FOR SUSTAINABLE FUTURE, CLAWAR 2021

Abstract
The industry tends to increasingly automate as many processes as possible, and to make this possible, they often resort to the use of robotic arms. This paper presents the development of a proposal for a modular joint for robotic arms that allows: to obtain the best possible torque/weight ratio; to be controlled in speed and/or position; to communicate with other joints and external microcontrollers; to keep the cost as low as possible; and to be easily reconfigurable. The proposed prototype was validated with real results. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

2022

Reinforcement Learning for Collaborative Robots Pick-and-Place Applications: A Case Study

Autores
Gomes, NM; Martins, FN; Lima, J; Wörtche, H;

Publicação
Automation

Abstract
The number of applications in which industrial robots share their working environment with people is increasing. Robots appropriate for such applications are equipped with safety systems according to ISO/TS 15066:2016 and are often referred to as collaborative robots (cobots). Due to the nature of human-robot collaboration, the working environment of cobots is subjected to unforeseeable modifications caused by people. Vision systems are often used to increase the adaptability of cobots, but they usually require knowledge of the objects to be manipulated. The application of machine learning techniques can increase the flexibility by enabling the control system of a cobot to continuously learn and adapt to unexpected changes in the working environment. In this paper we address this issue by investigating the use of Reinforcement Learning (RL) to control a cobot to perform pick-and-place tasks. We present the implementation of a control system that can adapt to changes in position and enables a cobot to grasp objects which were not part of the training. Our proposed system uses deep Q-learning to process color and depth images and generates an ?-greedy policy to define robot actions. The Q-values are estimated using Convolution Neural Networks (CNNs) based on pre-trained models for feature extraction. To reduce training time, we implement a simulation environment to first train the RL agent, then we apply the resulting system on a real cobot. System performance is compared when using the pre-trained CNN models ResNext, DenseNet, MobileNet, and MNASNet. Simulation and experimental results validate the proposed approach and show that our system reaches a grasping success rate of 89.9% when manipulating a never-seen object operating with the pre-trained CNN model MobileNet.

2022

A Short Term Wind Speed Forecasting Model Using Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System Models

Autores
Amoura, Y; Pereira, AI; Lima, J;

Publicação
SUSTAINABLE ENERGY FOR SMART CITIES, SESC 2021

Abstract
Future power systems encourage the use of renewable energy resources, among them wind power is of great interest, but its power output is intermittent in nature which can affect the stability of the power system and increase the risk of blackouts. Therefore, a forecasting model of the wind speed is essential for the optimal operation of a power supply with an important share of wind energy conversion systems. In this paper, two wind speed forecasting models based on multiple meteorological measurements of wind speed and temperature are proposed and compared according to their mean squared error (MSE) value. The first model concerns the artificial intelligence based on neural network (ANN) where several network configurations are proposed to achieve the most suitable structure of the problem, while the other model concerned the Adaptive Neuro-Fuzzy Inference System (ANFIS). To enhance the results accuracy, the invalid input samples are filtered. According to the computational results of the two models, the ANFIS has delivered more accurate outputs characterized by a reduced mean squared error value compared to the ANN-based model.

2022

Improving Incremental Encoder Measurement: Variable Acquisition Window and Quadrature Phase Compensation to Minimize Acquisition Errors

Autores
Lima, J; Pinto, VH; Moreira, AP; Costa, P;

Publicação
2022 IEEE INTERNATIONAL CONFERENCE ON AUTONOMOUS ROBOT SYSTEMS AND COMPETITIONS (ICARSC)

Abstract

Teses
supervisionadas

2021

Simulation and Planning of a 3D Spray Painting Robotic System

Autor
João Marcelo Casanova Almeida Tomé Santos

Instituição
UP-FEUP

2021

Sistema Inteligente de Deteção de Pessoas para Robôs Móveis Autónomos de Desinfeção

Autor
Hugo Lima Mendonça

Instituição
UP-FEUP

2021

Articulação Modular para Braços Robóticos

Autor
Marco António Mendonça Rocha

Instituição
UP-FEUP

2021

Realistic multisensory virtual reality simulator for firefighter training: comparing the physiological response of trainees in real and virtual environments

Autor
David Gonçalves Narciso

Instituição

2021

Application of Lean methodologies in Information Security processes improvement

Autor
Francisco Ribeiro Pereira da Silva

Instituição
UP-FEUP