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About

About

M. F. Silva was born in April 11, 1970. He graduated, received the MSc. and the PhD. degrees in electrical and computer engineering from the Faculty of Engineering of the University of Porto, Portugal, in 1993, 1997 and 2005, respectively. Presently he is Coordinator Professor at the Institute of Engineering of the Polytechnic Institute of Porto, Department of Electrical Engineering, and Senior Researcher at the Centre for Robotics in Industry and Intelligent Systems of INESC TEC. He is the author or more than 150 publications in international journals and conferences and has been involved in several R&D projects. He has also been actively involved in the organization of several international conferences, belongs to the CLAWAR Association Management Team and was President of the Portuguese Robotics Society. His research focuses on modelling, simulation, industrial robotics, mobile robotics, biological inspired robotics, and education in engineering.

Interest
Topics
Details

Details

  • Name

    Manuel Santos Silva
  • Role

    Centre Coordinator
  • Since

    03rd January 2012
022
Publications

2025

A review of advanced controller methodologies for robotic manipulators

Authors
Tinoco, V; Silva, MF; Santos, FN; Morais, R; Magalhaes, SA; Oliveira, PM;

Publication
INTERNATIONAL JOURNAL OF DYNAMICS AND CONTROL

Abstract
With the global population on the rise and a declining agricultural labor force, the realm of robotics research in agriculture, such as robotic manipulators, has assumed heightened significance. This article undertakes a comprehensive exploration of the latest advancements in controllers tailored for robotic manipulators. The investigation encompasses an examination of six distinct controller paradigms, complemented by the presentation of three exemplars for each category. These paradigms encompass: (i) adaptive control, (ii) sliding mode control, (iii) model predictive control, (iv) robust control, (v) fuzzy logic control and (vi) neural network control. The article further introduces and presents comparative tables for each controller category. These controllers excel in tracking trajectories and efficiently reaching reference points with rapid convergence. The key point of divergence among these controllers resides in their inherent complexity.

2025

Automated optical system for quality inspection on reflective parts

Authors
Nascimento, R; Rocha, CD; Gonzalez, DG; Silva, T; Moreira, R; Silva, MF; Filipe, V; Rocha, LF;

Publication
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY

Abstract
The growing demand for high-quality components in various industries, particularly in the automotive sector, requires advanced and reliable inspection methods to maintain competitive standards and support innovation. Manual quality inspection tasks are often inefficient and prone to errors due to their repetitive nature and subjectivity, which can lead to attention lapses and operator fatigue. The inspection of reflective aluminum parts presents additional challenges, as uncontrolled reflections and glare can obscure defects and reduce the reliability of conventional vision-based methods. Addressing these challenges requires optimized illumination strategies and robust image processing techniques to enhance defect visibility. This work presents the development of an automated optical inspection system for reflective parts, focusing on components made of high-pressure diecast aluminum used in the automotive industry. The reflective nature of these parts introduces challenges for defect detection, requiring optimized illumination and imaging methods. The system applies deep learning algorithms and uses dome light to achieve uniform illumination, enabling the detection of small defects on reflective surfaces. A collaborative robotic manipulator equipped with a gripper handles the parts during inspection, ensuring precise positioning and repeatability, which improves both the efficiency and effectiveness of the inspection process. A flow execution-based software platform integrates all system components, enabling seamless operation. The system was evaluated with Schmidt Light Metal Group using three custom datasets to detect surface porosities and inner wall defects post-machining. For surface porosity detection, YOLOv8-Mosaic, trained with cropped images to reduce background noise, achieved a recall value of 84.71% and was selected for implementation. Additionally, an endoscopic camera was used in a preliminary study to detect defects within the inner walls of holes. The industrial trials produced promising results, demonstrating the feasibility of implementing a vision-based automated inspection system in various industries. The system improves inspection accuracy and efficiency while reducing material waste and operator fatigue.

2025

Algae and Fish Farming - An EPS@ISEP 2022 Project

Authors
Blomme, RF; Domissy, Z; Dylik, Z; Hidding, T; Röhe, A; Duarte, AJ; Malheiro, B; Ribeiro, C; Justo, J; Silva, MF; Ferreira, P; Guedes, P;

Publication
FUTUREPROOFING ENGINEERING EDUCATION FOR GLOBAL RESPONSIBILITY, ICL2024, VOL 3

Abstract
The European Project Semester (EPS) at Instituto Superior de Engenharia do Porto (ISEP) is a capstone engineering design program where students, organised in multidisciplinary and multicultural teams, create a solution for a proposed problem, bearing in mind ethical, sustainability and market concerns. The project proposals are usually aligned with the United Nations Sustainable Development Goals (SDG). New sustainable food production methods are essential to cope with the continuous population growth and aligned with SDG2 and SDG12. In this context, this paper describes the research and work done by a team of Erasmus students enrolled in EPS@ISEP during the spring of 2022. Since sustainable algae farming can be a suitable source of food, the team's goal was the design and develop a proof-of-concept prototype, named GREEN center dot flow, of a symbiotic aquaponic system to farm algae and fish. The smart GREEN center dot flow concept comprises a modular structure and an app for control and supervision. The proposed design was driven by state-of-the-art research, targeted to a specific market niche based on a market analysis, and considering sustainability and ethics concerns, all of which are described in this manuscript. A proof-of-concept prototype was built and tested to verify that it worked as intended.

2024

Vision Robotics for the Automatic Assessment of the Diabetic Foot

Authors
Mesquita, R; Costa, T; Coelho, L; Silva, MF;

Publication
FLEXIBLE AUTOMATION AND INTELLIGENT MANUFACTURING: ESTABLISHING BRIDGES FOR MORE SUSTAINABLE MANUFACTURING SYSTEMS, FAIM 2023, VOL 1

Abstract
Diabetes, a chronic condition affecting millions of people, requires ongoing medical care and treatment, which can place a significant financial burden on society, directly and indirectly. In this paper we propose a vision-robotics system for the automatic assessment of the diabetic foot, one the exams used for the disease management. We present and discuss various computer vision techniques that can support the core operation of the system. U-Net and Segnet, two popular convolutional network architectures for image segmentation are applied in the current case. Hardcoded and machine learning pipelines are explained and compared using different metrics and scenarios. The obtained results show the advantages of the machine learning approach but also point to the importance of hard coded rules, especially when well know areas, such as the human foot, are the systems' target. Overall, the system achieved very good results, paving the way to a fully automated clinical system.

2024

The CrossLog System Concept and Architecture

Authors
Silva, MF; Rebelo, PM; Sobreira, H; Ribeiro, F;

Publication
FLEXIBLE AUTOMATION AND INTELLIGENT MANUFACTURING: ESTABLISHING BRIDGES FOR MORE SUSTAINABLE MANUFACTURING SYSTEMS, FAIM 2023, VOL 2

Abstract
Logistics chains are being increasingly developed due to several factors, among which the exponential growth of e-commerce. Crossdocking is a logistics strategy used by several companies from varied economic sectors, applied in warehouses and distribution centres. In this context, it is the objective of the CrossLog - Automatic Mixed-Palletizing for Crossdocking Logistics Centers Project, to investigate and study an automated and collaborative crossdocking system, capable of moving and managing the flow of products within the warehouse in the fastest and safest way. In its scope, this paper describes the concept and architecture envisioned for the crossdocking system developed in the scope of the CrossLog Project. One of its main distinguishing characteristics is the use of Autonomous Mobile Robots for performing much of the operations traditionally performed by human operators in today's logistics centres.

Supervised
thesis

2023

Confronto de cadência MES com SAP e melhoria de processos

Author
ANA CATARINA REMA OLIVEIRA

Institution
IPP-ISEP

2023

Braço-robô comandável à distância para testar displays de automóveis

Author
MARIANA SOFIA SANCHES LEOCÁDIO

Institution
IPP-ISEP

2023

Controlo e Validação de Movimentos de um Robô Virtual

Author
GONÇALO DA COSTA DIAS NOITES

Institution
IPP-ISEP

2023

Automatização do Processo Manual de Pesagem de Pigmentos

Author
DIOGO GONÇALO LIMA DE FREITAS

Institution
IPP-ISEP

2023

Proof of Concept for a Visualization Interface into the Intralogistics Process

Author
STÉPHANE CASTANHEIRA OLIVEIRA

Institution
IPP-ISEP