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This service develops advanced solutions in automation and industrial robotics, including handlers and mobile robots, and promotes the integration of control intelligent systems and sensing.

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Browsing CRIIS by Author "5157"
  • Item
    Autonomous Robot Navigation for Automotive Assembly Task: An Industry Use-Case
    ( 2019) Héber Miguel Sobreira ; Germano Veiga ; António Paulo Moreira ; Rodrigues,F ; Lima,J ; Luís Freitas Rocha ; 5364 ; 5424 ; 5674 ; 5157
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    Collaborative Welding System using BIM for Robotic Reprogramming and Spatial Augmented Reality
    ( 2019) Carlos Miguel Costa ; Luís Freitas Rocha ; Malaca,P ; Pedro Gomes Costa ; António Paulo Moreira ; Tavares,P ; Armando Sousa ; Germano Veiga ; 6164 ; 5152 ; 5157 ; 5159 ; 5364 ; 5674
    The optimization of the information flow from the initial design and through the several production stages plays a critical role in ensuring product quality while also reducing the manufacturing costs. As such, in this article we present a cooperative welding cell for structural steel fabrication that is capable of leveraging the Building Information Modeling (BIM) standards to automatically orchestrate the necessary tasks to be allocated to a human operator and a welding robot moving on a linear track. We propose a spatial augmented reality system that projects alignment information into the environment for helping the operator tack weld the beam attachments that will be later on seam welded by the industrial robot. This way we ensure maximum flexibility during the beam assembly stage while also improving the overall productivity and product quality since the operator no longer needs to rely on error prone measurement procedures and he receives his tasks through an immersive interface, relieving him from the burden of analyzing complex manufacturing design specifications. Moreover, no expert robotics knowledge is required to operate our welding cell because all the necessary information is extracted from the Industry Foundation Classes (IFC), namely the CAD models and welding sections, allowing our 3D beam perception systems to correct placement errors or beam bending, which coupled with our motion planning and welding pose optimization system ensures that the robot performs its tasks without collisions and as efficiently as possible while maximizing the welding quality. © 2019 Elsevier B.V.
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    Map-Matching Algorithms for Robot Self-Localization: A Comparison Between Perfect Match, Iterative Closest Point and Normal Distributions Transform
    ( 2019) Héber Miguel Sobreira ; António Paulo Moreira ; Paulo José Costa ; Farias,PCMA ; José Lima ; Luís Freitas Rocha ; Sousa,I ; Carlos Miguel Costa ; 6164 ; 5153 ; 5156 ; 5157 ; 5364 ; 5424
    The self-localization of mobile robots in the environment is one of the most fundamental problems in the robotics navigation field. It is a complex and challenging problem due to the high requirements of autonomous mobile vehicles, particularly with regard to the algorithms accuracy, robustness and computational efficiency. In this paper, we present a comparison of three of the most used map-matching algorithms applied in localization based on natural landmarks: our implementation of the Perfect Match (PM) and the Point Cloud Library (PCL) implementation of the Iterative Closest Point (ICP) and the Normal Distribution Transform (NDT). For the purpose of this comparison we have considered a set of representative metrics, such as pose estimation accuracy, computational efficiency, convergence speed, maximum admissible initialization error and robustness to the presence of outliers in the robots sensors data. The test results were retrieved using our ROS natural landmark public dataset, containing several tests with simulated and real sensor data. The performance and robustness of the Perfect Match is highlighted throughout this article and is of paramount importance for real-time embedded systems with limited computing power that require accurate pose estimation and fast reaction times for high speed navigation. Moreover, we added to PCL a new algorithm for performing correspondence estimation using lookup tables that was inspired by the PM approach to solve this problem. This new method for computing the closest map point to a given sensor reading proved to be 40 to 60 times faster than the existing k-d tree approach in PCL and allowed the Iterative Closest Point algorithm to perform point cloud registration 5 to 9 times faster. © 2018 Springer Science+Business Media B.V., part of Springer Nature