PPG.EGPSA/ITEGAM

URI permanente desta comunidadehttps://rigalileo.itegam.org.br/handle/123456789/1

A comunidade dispõe da produção técnica e científica do Programa de Pós-graduação em Engenharia, Gestão de Processos, Sistema e Ambiental (PPG.EGPSA) do Instituto de Tecnologia e Educação Galileo da Amazônia (ITEGAM), fruto da atividade de pesquisa e desenvolvimento (P&D). É possível acessar os trabalhos de conclusão do programa de pós-graduação, artigos e livros vinculados a pesquisa, desenvolvimento, inovação e extensão.

Navegar

Filtros

20243

Configurações

Assunto: search.filters.subject.Offloading

Resultados da Pesquisa

Agora exibindo 1 - 3 de 3
  • Imagem de Miniatura
    Item
    Uma proposta de arquitetura para offloading de dados de robôs móveis autônomos por meio de servidores 5G
    (Instituto de Tecnologia e Educação Galileo da Amazônia, 2024) COSTA, Gledyson Cidade da; TABOADA, Carlos Manoel; LEITE, Jandecy Cabral
    Autonomous mobile robots (AMRs) have made a name for themselves in industry due to their ability to make independent decisions and operate in unpredictable environments, using sensors for precise navigation. This paper proposes a new architecture for offloading the processing of mapping, navigation, and real-time localization data from AMRs, using a private 5G network and peripheral computing. The motivation is to reduce the local computational load of AMRs, improve navigation accuracy, and increase system efficiency. The research was carried out through a non-systematic literature review, simulation development, and full-scale prototyping. The proposal replaces the traditional PLC and PC BOX of AMRs with a Central Control Unit (CCU), transferring processing to the cloud, taking advantage of the low latency and high speed of the 5G network. The main results indicate an improvement in the management and operation of AMRs in complex industrial environments. The proposed architecture provides faster response times and greater scalability, thanks to the 5G network's ability to handle large volumes of data in real time. In addition, integration with the Internet of Things (IoT) allows for more efficient and reliable control, easing the computational burden on AMRs. In short, this architecture offers a flexible and efficient solution for operating AMRs, contributing to greater overall system efficiency and improving scalability and performance in industrial environments.
  • Imagem de Miniatura
    Item
    Developing an AMR Prototype with Processing Offloading Using 5G Servers for Industry 4.0
    (Instituto de Tecnologia e Educação Galileo da Amazônia, 2024) COSTA, Gledyson Cidade da; RODRÍGUEZ, Carlos Manoel Taboada; LEITE, Jandecy Cabral; LEITE, Jandecy Cabral
    This article addresses the development of an Autonomous Mobile Robot (AMR) prototype with processing offloading using 5G servers, aiming at industrial automation in Industry 4.0. The proposed solution transfers complex computational tasks to external servers, leveraging the high speed and low latency of the 5G network. The system integrates technologies such as IoT, cyber-physical systems (CPS), edge computing, and artificial intelligence, improving the efficiency and flexibility of industrial processes. The results indicate that offloading via 5G significantly improves the performance of AMRs, reducing energy consumption and maintenance costs, while increasing the durability of components.
  • Imagem de Miniatura
    Item
    Developing An AMR Prototype With Processing Offloading Using 5G Servers For Industry 4.0
    (Instituto de Tecnologia e Educação Galileo da Amazônia, 2024-09-01) COSTA, Gledyson Cidade da; RODRÍGUEZ, Carlos Manoel Taboada; LEITE, Jandecy Cabral
    This study explores the development of an autonomous mobile robot (AMR) prototype with processing offloading using 5G servers for Industry 4.0. The goal is to improve AMR performance by reducing the computational load on the robot and optimizing energy efficiency. The developed architecture integrates the AMR with the 5G infrastructure, distributing processing between the robot and external servers, enabling real-time response. The results show that this approach improves AMR navigation and efficiency in industrial environments.