Cookies Policy
The website need some cookies and similar means to function. If you permit us, we will use those means to collect data on your visits for aggregated statistics to improve our service. Find out More
Accept Reject
  • Menu
    Presentation

    Robotics in Industry and Intelligent Systems

    As part of the Cluster I+I - Industry and Innovation, at the Centre for Robotics in Industry and Intelligent Systems (CRIIS) we design and implement innovative solutions in the fields of industrial robotics and intelligent systems. At CRIIS, we work closely with Companies, other INESC TEC Centres and other Institutes and Universities, following the motto from Research and Development to Innovation, Design, Prototyping and Implementation. At the 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.

    news
    Systems Engineering and Management

    European project aimed at contributing to the digitalisation of shipbuilding won an international award

    INESC TEC is one of the partners of the European project Mari4_YARD, dedicated to the development of flexible and modular construction solutions in small and medium-sized shipyards; this endeavour was one of the winners in the Economic Viability category of the prestigious Waterbone Awards initiative. The award acknowledged the work carried out within the scope of this project, which aimed to develop technological solutions to assist naval workers, as well as to prepare operations for the future.

    13th February 2025

    View all News
    Team
    Manuel Santos Silva

    Centre Coordinator

    Luís Freitas Rocha

    Centre Coordinator

    ViewClose Alphabet
    • a
    • b
    • c
    • d
    • e
    • f
    • g
    • h
    • i
    • j
    • k
    • l
    • m
    • n
    • o
    • p
    • q
    • r
    • s
    • t
    • u
    • v
    • w
    • x
    • y
    • z
    Publications

    CRIIS Publications

    View all Publications

    2027

    COGNITIVE WORKLOAD AND FATIGUE IN A HUMAN-ROBOT COLLABORATIVE ASSEMBLY WORKSTATION: A PILOT STUDY

    Authors
    Santos, J; Ferraz, M; Pinto, A; Rocha, LF; Costa, CM; Simões, AC; Bombeke, K; Vaz, M;

    Publication
    International Symposium on Occupational Safety and Hygiene: Proceedings Book of the SHO2023

    Abstract

    2025

    Human-in-the-loop Multi-objective Bayesian Optimization for Directed Energy Deposition with in-situ monitoring

    Authors
    Sousa, J; Sousa, A; Brueckner, F; Reis, LP; Reis, A;

    Publication
    ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING

    Abstract
    Directed Energy Deposition (DED) is a free-form metal additive manufacturing process characterized as toolless, flexible, and energy-efficient compared to traditional processes. However, it is a complex system with a highly dynamic nature that presents challenges for modeling and optimization due to its multiphysics and multiscale characteristics. Additionally, multiple factors such as different machine setups and materials require extensive testing through single-track depositions, which can be time and resource-intensive. Single-track experiments are the foundation for establishing optimal initial parameters and comprehensively characterizing bead geometry, ensuring the accuracy and efficiency of computer-aided design and process quality validation. We digitized a DED setup using the Robot Operating System (ROS 2) and employed a thermal camera for real-time monitoring and evaluation to streamline the experimentation process. With the laser power and velocity as inputs, we optimized the dimensions and stability of the melt pool and evaluated different objective functions and approaches using a Response Surface Model (RSM). The three-objective approach achieved better rewards in all iterations and, when implemented in areal setup, allowed to reduce the number of experiments and shorten setup time. Our approach can minimize waste, increase the quality and reliability of DED, and enhance and simplify human-process interaction by leveraging the collaboration between human knowledge and model predictions.

    CloseRead Abstract

    2025

    Collaborative fault tolerance for cyber–physical systems: The detection stage

    Authors
    Piardi, L; de Oliveira, AS; Costa, P; Leitão, P;

    Publication
    Computers in Industry

    Abstract

    2025

    Pollinationbots - A Swarm Robotic System for Tree Pollination

    Authors
    Castro, JT; Pinheiro, I; Marques, MN; Moura, P; dos Santos, FN;

    Publication
    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

    Abstract
    In nature, and particularly in agriculture, pollination is fundamental for the sustainability of our society. In this context, pollination is a vital process underlying crop yield quality and is responsible for the biodiversity and the standards of the flora. Bees play a crucial role in natural pollination; however, their populations are declining. Robots can help maintain pollination levels while humans work to recover bee populations. Swarm robotics approaches appear promising for robotic pollination. This paper proposes the cooperation between multiple Unmanned Aerial Vehicles (UAVs) and an Unmanned Ground Vehicle (UGV), leveraging the advantages of collaborative work for pollination, referred to as Pollinationbots. Pollinationbots is based in swarm behaviors and methodologies to implement more effective pollination strategies, ensuring efficient pollination across various scenarios. The paper presents the architecture of the Pollinationbots system, which was evaluated using the Webots simulator, focusing on path planning and follower behavior. Preliminary simulation results indicate that this is a viable solution for robotic pollination. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

    CloseRead Abstract

    2025

    Grapevine inflorescence segmentation and flower estimation based on Computer Vision techniques for early yield assessment

    Authors
    Moreira, G; dos Santos, FN; Cunha, M;

    Publication
    SMART AGRICULTURAL TECHNOLOGY

    Abstract
    Yield forecasting is of immeasurable value in modern viticulture to optimize harvest scheduling and quality management. The number of inflorescences and flowers per vine is one of the main components and their assessment serves as an early predictor, which can explain up to 85-90% of yield variability. This study introduces a sophisticated framework that integrates the benchmark of different advanced deep learning and classic image processing to automate the segmentation of grapevine inflorescences and the detection of single flowers, to achieve precise, early, and non-invasive yield predictions in viticulture. The YOLOv8n model achieved superior performance in localizing inflorescences ( F1-Score (Box) = 95.9%) and detecting individual flowers (F1-Score = 91.4%), while the YOLOv5n model excelled in the segmentation task ( F1-Score (Mask) = 98.6%). The models demonstrated a strong correlation (R-2 > 90.0%) between detected and visible flowers in inflorescences. A statistical analysis confirmed the robustness of the framework, with the YOLOv8 model once again standing out, showing no significant differences in error rates across diverse grapevine morphologies and varieties, ensuring wide applicability. The results demonstrate that these models can significantly improve the accuracy of early yield predictions, offering a noninvasive, scalable solution for Precision Viticulture. The findings underscore the potential for Computer Vision technology to enhance vineyard management practices, leading to better resource allocation and improved crop quality.

    CloseRead Abstract