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About

About

Luis Rocha received his Ph.D. in Electrical and Computer Engineering from the Faculty of Engineering University of Porto in 2014. He has been a researcher at INESC TEC since 2010, and he currently oversees the industrial manipulator research area at the Center for Robotics in Industry and Intelligent Systems (CRIIS). Member of the iilab executive committee (Industry and Innovation Lab of INESC TEC), in charge of the lab's infrastructure. Supervisor of several Master's and PhD theses. He has over 50 publications in international scientific journals and conference proceedings, and he is now an Associate Editor for the Industrial Robot Journal. His primary research interests include developing agile and human-centered industrial robotic systems, as well as investigating novel human-robot interaction solutions, robot programming procedures, and advanced perception and manipulation systems. He led the INESC team on a number of national and European R&D projects. Luís has 5 years of experience as an entrepreneur (robotics startup).

Details

Details

  • Name

    Luís Freitas Rocha
  • Role

    Centre Coordinator
  • Since

    01st September 2010
046
Publications

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

Methodology and Challenges of Implementing Advanced Technological Solutions in Small and Medium Shipyards: The Case Study of the Mari4_YARD Project

Authors
Grazi, L; Feijoo Alonso, A; Gasiorek, A; Pertusa Llopis, AM; Grajeda, A; Kanakis, A; Rodriguez Vidal, A; Parri, A; Vidal, F; Ergas, I; Zeljkovic, I; Durá, JP; Mein, JP; Katsampiris-Salgado, K; Rocha, LF; Rodriguez, LN; Petry, MR; Neufeld, M; Dimitropoulos, N; Köster, N; Mimica, R; Fernandes, SV; Crea, S; Makris, S; Giartzas, S; Settler, V; Masood, J;

Publication
Electronics

Abstract
Small to medium-sized shipyards play a crucial role in the European naval industry. However, the globalization of technology has increased competition, posing significant challenges to shipyards, particularly in domestic markets for short sea, work, and inland vessels. Many shipyard operations still rely on manual, labor-intensive tasks performed by highly skilled operators. In response, the adoption of new tools is essential to enhance efficiency and competitiveness. This paper presents a methodology for developing a human-centric portfolio of advanced technologies tailored for shipyard environments, covering processes such as shipbuilding, retrofitting, outfitting, and maintenance. The proposed technological solutions, which have achieved high technology readiness levels, include 3D modeling and digitalization, robotics, augmented and virtual reality, and occupational exoskeletons. Key findings from real-scale demonstrations are discussed, along with major development and implementation challenges. Finally, best practices and recommendations are provided to support both technology developers seeking fully tested tools and end users aiming for seamless adoption.

2025

Quality Inspection in Casting Aluminum Parts: A Machine Vision System for Filings Detection and Hole Inspection

Authors
Nascimento, R; Ferreira, T; Rocha, CD; Filipe, V; Silva, MF; Veiga, G; Rocha, L;

Publication
Journal of Intelligent & Robotic Systems

Abstract
Abstract Quality inspection inspection systems are critical for maintaining product integrity. Being a repetitive task, when performed by operators only, it can be slow and error-prone. This paper introduces an automated inspection system for quality assessment in casting aluminum parts resorting to a robotic system. The method comprises two processes: filing detection and hole inspection. For filing detection, five deep learning modes were trained. These models include an object detector and four instance segmentation models: YOLOv8, YOLOv8n-seg, YOLOv8s-seg, YOLOv8m-seg, and Mask R-CNN, respectively. Among these, YOLOv8s-seg exhibited the best overall performance, achieving a recall rate of 98.10%, critical for minimizing false negatives and yielding the best overall results. Alongside, the system inspects holes, utilizing image processing techniques like template-matching and blob detection, achieving a 97.30% accuracy and a 2.67% Percentage of Wrong Classifications. The system improves inspection precision and efficiency while supporting sustainability and ergonomic standards, reducing material waste and reducing operator fatigue.

2024

Inspection of Part Placement Within Containers Using Point Cloud Overlap Analysis for an Automotive Production Line

Authors
Costa, CM; Dias, J; Nascimento, R; Rocha, C; Veiga, G; Sousa, A; Thomas, U; Rocha, L;

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

Abstract
Reliable operation of production lines without unscheduled disruptions is of paramount importance for ensuring the proper operation of automated working cells involving robotic systems. This article addresses the issue of preventing disruptions to an automotive production line that can arise from incorrect placement of aluminum car parts by a human operator in a feeding container with 4 indexing pins for each part. The detection of the misplaced parts is critical for avoiding collisions between the containers and a high pressure washing machine and also to avoid collisions between the parts and a robotic arm that is feeding parts to a air leakage inspection machine. The proposed inspection system relies on a 3D sensor for scanning the parts inside a container and then estimates the 6 DoF pose of the container followed by an analysis of the overlap percentage between each part reference point cloud and the 3D sensor data. When the overlap percentage is below a given threshold, the part is considered as misplaced and the operator is alerted to fix the part placement in the container. The deployment of the inspection system on an automotive production line for 22 weeks has shown promising results by avoiding 18 hours of disruptions, since it detected 407 containers having misplaced parts in 4524 inspections, from which 12 were false negatives, while no false positives were reported, which allowed the elimination of disruptions to the production line at the cost of manual reinspection of 0.27% of false negative containers by the operator.

2024

Automating Lateral Shoe Roughing through a Robotic Manipulator Programmed by Demonstration

Authors
Ventuzelos, V; Petry, MR; Rocha, LF;

Publication
2024 IEEE INTERNATIONAL CONFERENCE ON AUTONOMOUS ROBOT SYSTEMS AND COMPETITIONS, ICARSC

Abstract
The footwear industry is known for its longstanding traditional production methods that require intense manual labor. Roughing, for example, is regarded as one of the significant and critical operations in shoe manufacturing and consists of using abrasive tools to remove a thin layer of the shoe's surface, creating a slightly roughened texture that provides a better surface area for adhesion. As such, workers are typically subjected to hazardous substances (i.e., dust, chromium), repetitive strain injuries, and ergonomic challenges. Although robots can automate repetitive tasks and perform with high precision and consistency, the footwear industry is usually reluctant to employ industrial robots due to the need for restructuring. This paper addresses the challenge of re-designing the lateral roughing of uppers to allow robot-assisted manufacturing with minimal modifications in the manufacturing process. The proposed innovative system employs a robotic manipulator to perform roughing based on data collected from preceding manufacturing steps. Workers marking the mesh line of each sole-upper pair can simultaneously teach the manipulator path for that same pair, using a programming-by-demonstration approach. Multiple paths were collected by outlining a piece of footwear, converted into robot instructions, and deployed on a simulated and real industrial manipulator. The key findings of this research showcase the capability of the proposed solution to replicate collected paths accurately, indicating potential applications not only in roughing processes but also in similar tasks like primer and adhesive application.

Supervised
thesis

2023

Robotic System for Depalletization

Author
João Pedro Gomes Costa

Institution
INESCTEC

2023

Adaptive Grasping Planning: A Novel Unified and Modular Grasping Pipeline Architecture

Author
João Pedro Carvalho de Souza

Institution
INESCTEC

2023

Automated Shoe Roughing Cell

Author
David José Lucas Raposo

Institution
INESCTEC

Cinemática Composta de Manipuladores Móveis

Author
Gonçalo Daniel Ribeiro da Silva

Institution
INESCTEC

Smart Collision Avoidance System for a Dual-Arm Manipulator

Author
Inês Pinto Frutuoso

Institution
INESCTEC