Cookies
O website necessita de alguns cookies e outros recursos semelhantes para funcionar. Caso o permita, o INESC TEC irá utilizar cookies para recolher dados sobre as suas visitas, contribuindo, assim, para estatísticas agregadas que permitem melhorar o nosso serviço. Ver mais
Aceitar Rejeitar
  • Menu
Sobre

Sobre

Paulo Rebelo concluiu o mestrado integrado em Engenharia Electrotécnica e Computadores, no ramo de automação industrial em março de 2017, com especialização em robótica industrial na FEUP - Faculdade de Engenharia da Universidade do Porto. Durante o ano de 2016, desenvolveu a sua tese de mestrado na Continental Mabor, em Lousado, onde o principal objectivo era a automatização de um sistema de corte de rolos calandrados numa máquina específica da empresa.

Desde março de 2017 que é investigador no INESC TEC, no Porto, e tem trabalhado em projetos de diferentes áreas: robótica móvel, manipuladores colaborativos, sistemas de visão artificial, sistemas de automação e sistemas IoT (Industry 4.0), estas são as suas áreas de especialização.
Até hoje, trabalhou nos seguintes projetos de investigação: FASTEN, ScalabLE4.0, MANUFACTUR4.0, PRECISIONcork, MTEX-Multi e PRODUTECH. Conciliando com o desenvolvimento, também faz um pouco de gestão de projetos.

Tópicos
de interesse
Detalhes

Detalhes

  • Nome

    Paulo Miranda Rebelo
  • Cargo

    Investigador
  • Desde

    19 abril 2017
  • Nacionalidade

    Portugal
  • Contactos

    +351220413317
    paulo.m.rebelo@inesctec.pt
014
Publicações

2024

The CrossLog System Concept and Architecture

Autores
Silva, F; Rebelo, M; Sobreira, H; Ribeiro, F;

Publicação
Lecture Notes in Mechanical Engineering

Abstract
Logistics chains are being increasingly developed due to several factors, among which the exponential growth of e-commerce. Crossdocking is a logistics strategy used by several companies from varied economic sectors, applied in warehouses and distribution centres. In this context, it is the objective of the “CrossLog – Automatic Mixed-Palletizing for Crossdocking Logistics Centers” Project, to investigate and study an automated and collaborative crossdocking system, capable of moving and managing the flow of products within the warehouse in the fastest and safest way. In its scope, this paper describes the concept and architecture envisioned for the crossdocking system developed in the scope of the CrossLog Project. One of its main distinguishing characteristics is the use of Autonomous Mobile Robots for performing much of the operations traditionally performed by human operators in today’s logistics centres. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

2023

Multi-robot Coordination for a Heterogeneous Fleet of Robots

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

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

Abstract
There is an increasing need for autonomous mobile robots (AMRs) in industrial environments. The capability of autonomous movement and transportation of items in industrial environments provides a significant increase in productivity and efficiency. This need, coupled with the possibility of controlling groups of heterogeneous robots, simultaneously addresses a wide range of tasks with different characteristics in the same environment, further increasing productivity and efficiency. This paper will present an implementation of a system capable of coordinating a fleet of heterogeneous robots with robustness. The implemented system must be able to plan a safe and efficient path for these different robots. To achieve this task, the TEA* (Time Enhanced A*) graph search algorithm will be used to coordinate the paths of the robots, along with a graph decomposition module that will be used to improve the efficiency and safety of this system. The project was implemented using the ROS framework and the Stage simulator. Results validate the proposed approach since the system was able to coordinate a fleet of robots in various different tests efficiently and safely, given the heterogeneity of the robots.

2021

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

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

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

Abstract
This paper presents a collaborative mobile manipulator assembly station, which uses force control to surpass the positional uncertainties arising from unstructured work environments and positional errors of the mobile platform. For this purpose, the use case of an internal combustion engine for the automotive industry was considered. Several force control heuristics relying on blind searches using oscillations and/or environment exploration were developed and implemented. Particular attention was given to the orientation errors of the mobile platform, as it was proved that they have a significant impact on the assembly task. The proposed heuristics showed great potential for the use case at hand. Particularly, when the orientation error of the platform is limited to +/- 2 degrees, the oscillation method complemented by environment exploration was able to surpass a maximum translation error of 32.3mm, whereas the environment exploration complemented by orientation correction was able to surpass an error of 73.3mm. Moreover, a generalization strategy was proposed, intending to expand the scope of the developed heuristics to other assembly applications.

2021

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

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

Publicação
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.