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    Apresentação

    Centro de Robótica Industrial e Sistemas Inteligentes

    Integrando o Cluster I+I - Indústria e Inovação, no Centro de Robótica Industrial e Sistemas Inteligentes (CRIIS) desenvolvemos e implementamos soluções inovadoras nos campos da robótica industrial e sistemas inteligentes. No CRIIS trabalhamos em estreita colaboração com empresas, outros Centros INESC TEC e outros Institutos e Universidades, seguindo o lema da Investigação e Desenvolvimento para a Inovação, Design, Prototipagem e Implementação. No Centro, abordamos as seguintes áreas de investigação principais: Navegação e Localização de Robôs Móveis, Sensores Inteligentes e Controlo de Sistemas Dinâmicos, Visão Industrial 2D/3D e Deteção Avançada, Manipuladores Móveis, Estruturas Especiais e Arquiteturas para Robôs, Interfaces de Robô-Humano e Realidade Aumentada, Robótica Industrial e Robôs Colaborativos do Futuro, Integração Vertical, IoT e Indústria 4.0.

    notícias
    Engenharia e Gestão de Sistemas

    Projeto europeu que quer contribuir para a digitalização da construção naval vence prémio internacional

    O INESC TEC é um dos parceiros do projeto europeu Mari4_YARD, dedicado ao desenvolvimento de soluções de construção flexível e modular em estaleiros navais de pequena e média dimensão, que foi um dos premiados na categoria Viabilidade Económica, da prestigiada iniciativa Waterbone Awards. O prémio reconheceu o trabalho levado a cabo no âmbito deste projeto, que visa o desenvolvimento de soluções tecnológicas de auxílio aos trabalhadores navais, assim como na preparação das operações para o futuro.

    13 fevereiro 2025

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    Projetos em destaque

    InOlive

    Sistema de monitorização inteligente para prevenção de pragas e doenças no olival

    2021-2023

    DIVA

    Boosting innovative DIgitech Value chains for Agrofood, forestry and environment

    2018-2021

    FDControlo

    Importância dos hospedeiros alternativos (plantas, insetos, vitis abandonada) na dispersão da doença da flavescência dourada (FD) da vinhda e das populações de scapholdeus titanus nas sub-regiões vitivinículas do Cávado e do Lima

    2018-2022

    Equipa
    Manuel Santos Silva

    Coordenador de Centro

    Luís Freitas Rocha

    Coordenador de Centro

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    Publicações

    CRIIS Publicações

    Ler todas as publicações

    2027

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

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

    Publicação
    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

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

    Publicação
    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.

    FecharLer Abstract

    2025

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

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

    Publicação
    Computers in Industry

    Abstract

    2025

    Pollinationbots - A Swarm Robotic System for Tree Pollination

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

    Publicação
    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.

    FecharLer Abstract

    2025

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

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

    Publicação
    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.

    FecharLer Abstract