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Sobre

Sobre

José Boaventura-Cunha é Engenheiro em Eletrónica e Telecomunicações pela Universidade de Aveiro (1985) e Doutorado em Engenharia Electrotécnica e de Computadores pela UTAD-Universidade de Trás-os-Montes e Alto Douro, Portugal (2002). Atualmente exerce funções de Professor Associado com Agregação na Escola de Ciências e Tecnologia da UTAD.

Desde 2012 é membro do CRIIS- Centre for Robotics in Industry and Intelligent Systems no INESC TEC - Instituto de Engenharia de Sistemas e Computadores, Tecnologia e Ciência e é Coordenador do polo INESC TEC na UTAD.

Os seus interesses de investigação relacionam-se com as áreas de Instrumentação, modelação e controlo aplicados a processos industriais e agro-florestais.

Tópicos
de interesse
Detalhes

Detalhes

014
Publicações

2022

Localization and Mapping on Agriculture Based on Point-Feature Extraction and Semiplanes Segmentation From 3D LiDAR Data

Autores
Aguiar, AS; dos Santos, FN; Sobreira, H; Boaventura Cunha, J; Sousa, AJ;

Publicação
FRONTIERS IN ROBOTICS AND AI

Abstract
Developing ground robots for agriculture is a demanding task. Robots should be capable of performing tasks like spraying, harvesting, or monitoring. However, the absence of structure in the agricultural scenes challenges the implementation of localization and mapping algorithms. Thus, the research and development of localization techniques are essential to boost agricultural robotics. To address this issue, we propose an algorithm called VineSLAM suitable for localization and mapping in agriculture. This approach uses both point- and semiplane-features extracted from 3D LiDAR data to map the environment and localize the robot using a novel Particle Filter that considers both feature modalities. The numeric stability of the algorithm was tested using simulated data. The proposed methodology proved to be suitable to localize a robot using only three orthogonal semiplanes. Moreover, the entire VineSLAM pipeline was compared against a state-of-the-art approach considering three real-world experiments in a woody-crop vineyard. Results show that our approach can localize the robot with precision even in long and symmetric vineyard corridors outperforming the state-of-the-art algorithm in this context.

2022

State of the Art of Wind and Power Prediction for Wind Farms

Autores
Puga, R; Baptista, J; Boaventura, J; Ferreira, J; Madureira, A;

Publicação
INNOVATIONS IN BIO-INSPIRED COMPUTING AND APPLICATIONS, IBICA 2021

Abstract
There are different clean energy production technologies, including wind energy production. This type of energy, among renewable energies, is one of the least predictable due to the unpredictability of the wind. The wind prediction has been a deeply analysed field since has a considerable share on the green energy production, and the investments on this sector are growing. The efficiency and stability of power production can be increased with a better prediction of the main source of energy, in our case the wind. In this paper, some techniques inspired by “Biological Inspired Optimization Techniques” applied to wind forecast are compared. The wind forecast is very important to be able to estimate the electric energy production in the wind farms. As you know, the energy balance must be checked in the electrical system at every moment. In this study we are going to analyse different methodologies of wind and power prediction for wind farms to understand the method with best results. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

2022

State of the Art on Advanced Control of Electric Energy Transformation to Hydrogen

Autores
Puga, R; Boaventura, J; Ferreira, J; Madureira, A;

Publicação
INNOVATIONS IN BIO-INSPIRED COMPUTING AND APPLICATIONS, IBICA 2021

Abstract
The need for sustainable power production has led to the development of more innovative approaches to production and storage. In light of this hydrogen production through wind power has emerged as sufficient in ensuring that the objectives of the Paris Agreement are made. This paper discusses the state-of-art models and controls used in ensuring that greater efficiency is achieved in the processes of energy to hydrogen transformation. The paper concludes with a comparison of the models and determination of one which suffices in ensuring that hydrogen/energy transformation is more efficient.

2022

Dynamic Modelling of a Thermal Solar Heating System

Autores
Boaventura-Cunha, J; Ferreira, J;

Publicação
INNOVATIONS IN BIO-INSPIRED COMPUTING AND APPLICATIONS, IBICA 2021

Abstract
Nowadays the world faces the challenge to rapidly diminish the use of fossil fuels in order to reduce pollutants and the emission of greenhouse gases and to mitigate the global warming. Renewable energies, such as solar radiation, among others, are playing a relevant role in this context. Namely, the use of thermal energy storage systems in buildings and industry is increasing enabling to reduce operational costs and carbon dioxide emissions. Heat storage systems based in solar thermal panels for heating water in buildings are industrially mature but some improvements can be made to improve their efficiencies. In this work are presented the methods and the results achieved to model the dynamic behavior of the hot water temperature as function of the weather, operating conditions and technical parameters of the thermal solar system. This type of dynamic models will enable to optimize the efficiency of this type of systems regarding the use of auxiliary energy sources to heat the water whenever the temperature in the storage tank falls below a defined threshold level. As future work it is intended to use adaptive control algorithms to reduce the use of backup power sources (electricity, oil, gas) by using the information of the system status as well predictions for hot water consumption profiles and solar radiation.

2021

Particle filter refinement based on clustering procedures for high-dimensional localization and mapping systems

Autores
Aguiar, AS; dos Santos, FN; Sobreira, H; Cunha, JB; Sousa, AJ;

Publicação
ROBOTICS AND AUTONOMOUS SYSTEMS

Abstract

Teses
supervisionadas

2021

Development of a multimodal management platform for patients in physical rehabilitation

Autor
Tiago Luís Salgueiro dos Santos

Instituição
UP-FEUP

2020

Localization and Mapping based on Semantic and Multi-Layer Maps Concepts

Autor
André Silva Pinto de Aguiar

Instituição
UP-FEUP

2016

ANÁLISE DINÂMICA DE ESCOAMENTO DE FLUÍDOS

Autor
Maria Manuela Jorge Martins Ferreira

Instituição
UTAD

2016

Módulo para Gestão de Atualizações de Software IPBrick

Autor
José Francisco Ângelo Oliveira

Instituição
UP-FEUP

2016

ANÁLISE COMPUTACIONAL DA CONDUÇÃO DE CALOR EM DOMÍNIOS BIDIMENSIONAIS

Autor
Luís Adriano Preto Mendes Afonso

Instituição
UTAD