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Multi-Robot Federated Edge Learning Framework for Efficient Coordination and Information Management in Smart Construction

  • Xinqi Liu
  • Jihua Wang
  • Ruopan Huang
  • Wei Pan

Smart construction involves a growing array of devices that generate extensive data, capable of enhancing construction efficiency and productivity. Nonetheless, the handling of this diverse and abundant information, along with the geographical spread of construction sites, poses challenges to effective communication and information processing within the management system. Multi-robot systems, as a new type of Internet of Things device, have the potential ability to coordinate workers to complete their work while serving as an edge node for information storage and processing. This paper presents a multi-robot federated edge learning framework that facilitates construction information management and communication. The work demonstrates the role of distributed databases in processing information during project execution, in contrast to centralized information systems. To address the intricacies of construction sites and the wide array of equipment involved, unmanned aerial vehicles and quadruped robots are employed as edge nodes. The formation of a federated edge learning framework ensures the real-time processing of massive data and data privacy issues. The Federated Multi-Robot (FedMR) framework is a global sharing model focused on preserving differential privacy protection. This framework is distributed to multiple edge robots in each round, enabling local real-time processing of robot tasks. The system can accomplish target detection and tracking of workers based on computer vision. Additionally, we collect MiC energy consumption data during the construction process and predict carbon emissions. Based on the implementation and testing of the system, it has been shown to provide structured and reliable information, fast local transmission, and the ability to process information in real-time. The system's ability to coordinate workers and process information makes it a valuable tool in smart construction

  • Keywords:
  • Construction management,
  • federated learning,
  • multi-robots,
  • Information management,
  • differential privacy,
  • Modular Integrated Construction(MiC),
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Xinqi Liu

The University of Hong Kong, Hong Kong

Jihua Wang

The University of Hong Kong, Hong Kong

Ruopan Huang

The University of Hong Kong, Hong Kong

Wei Pan

The University of Hong Kong, Hong Kong - ORCID: 0000-0002-2720-3073

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  • Publication Year: 2023
  • Pages: 553-563

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  • Publication Year: 2023

Chapter Information

Chapter Title

Multi-Robot Federated Edge Learning Framework for Efficient Coordination and Information Management in Smart Construction

Authors

Xinqi Liu, Jihua Wang, Ruopan Huang, Wei Pan

DOI

10.36253/979-12-215-0289-3.54

Peer Reviewed

Publication Year

2023

Copyright Information

© 2023 Author(s)

Content License

CC BY-NC 4.0

Metadata License

CC0 1.0

Bibliographic Information

Book Title

CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality

Book Subtitle

Managing the Digital Transformation of Construction Industry

Editors

Pietro Capone, Vito Getuli, Farzad Pour Rahimian, Nashwan Dawood, Alessandro Bruttini, Tommaso Sorbi

Peer Reviewed

Publication Year

2023

Copyright Information

© 2023 Author(s)

Content License

CC BY-NC 4.0

Metadata License

CC0 1.0

Publisher Name

Firenze University Press

DOI

10.36253/979-12-215-0289-3

eISBN (pdf)

979-12-215-0289-3

eISBN (xml)

979-12-215-0257-2

Series Title

Proceedings e report

Series ISSN

2704-601X

Series E-ISSN

2704-5846

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