CRIIS

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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 "5159"
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    A* Based Routing and Scheduling Modules for Multiple AGVs in an Industrial Scenario
    ( 2021) Santos,J ; Paulo Miranda Rebelo ; Luís Freitas Rocha ; Pedro Gomes Costa ; Germano Veiga ; 5159 ; 5364 ; 5674 ; 7077
    A multi-AGV based logistic system is typically associated with two fundamental problems, critical for its overall performance: the AGV’s route planning for collision and deadlock avoidance; and the task scheduling to determine which vehicle should transport which load. Several heuristic functions can be used according to the application. This paper proposes a time-based algorithm to dynamically control a fleet of Autonomous Guided Vehicles (AGVs) in an automatic warehouse scenario. Our approach includes a routing algorithm based on the A* heuristic search (TEA*—Time Enhanced A*) to generate free-collisions paths and a scheduling module to improve the results of the routing algorithm. These modules work cooperatively to provide an efficient task execution time considering as basis the routing algorithm information. Simulation experiments are presented using a typical industrial layout for 10 and 20 AGVs. Moreover, a comparison with an alternative approach from the state-of-the-art is also presented.
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    Bin Picking Approaches Based on Deep Learning Techniques: A State-of-the-Art Survey
    ( 2022) Cordeiro,A ; Luís Freitas Rocha ; Carlos Miguel Costa ; Pedro Gomes Costa ; Manuel Santos Silva ; 5159 ; 5364 ; 5655 ; 6164
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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.