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    About

    About

    Paulo Rebelo completed is master degree in Electrical and Computers Engineering, in the filed of industrial automation in March 2017, with a specialization in industrial robotics at FEUP - Faculty of Engineering of the University of Porto. During the year of 2016, he developed his master thesis in Continental Mabor, in Lousado, where the main goal was the automation of a calendered rolls cut system at one specific machine of the company.

    Nowadays, he is a researcher in INESC TEC, in Porto, since March 2017. Here, he worked in several projects in different areas: mobile robotics, collaborative manipulators, artificial vision systems, automation systems and IoT systems (Industry 4.0), these were his expertise areas.
    To this day, he has worked on the following projects: FASTEN, ScalABLE4.0, MANUFACTUR4.0, PRECISIONcork, MTEX-Multi and PRODUTECH. Conciliating to development it also does a bit of project management.

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    Publications
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    2023

    Multi-robot Coordination for a Heterogeneous Fleet of Robots

    Authors
    Pereira, D; Matos, D; Rebelo, P; Ribeiro, F; Costa, P; Lima, J;

    Publication
    ROBOT2022: FIFTH IBERIAN ROBOTICS CONFERENCE: ADVANCES IN ROBOTICS, VOL 2

    Abstract

    2021

    Force control heuristics for surpassing pose uncertainty in mobile robotic assembly platforms

    Authors
    Moutinho, D; Rebelo, P; Costa, C; Rocha, L; Veiga, G;

    Publication
    2021 IEEE INTERNATIONAL CONFERENCE ON AUTONOMOUS ROBOT SYSTEMS AND COMPETITIONS (ICARSC)

    Abstract

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    2021

    A* Based Routing and Scheduling Modules for Multiple AGVs in an Industrial Scenario

    Authors
    Santos, J; Rebelo, PM; Rocha, LF; Costa, P; Veiga, G;

    Publication
    ROBOTICS

    Abstract
    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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