Collaborative Intelligence in the Era of Industry 5.0

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Industrial Electronics".

Deadline for manuscript submissions: closed (15 April 2025) | Viewed by 1279

Special Issue Editors


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Guest Editor
Irish Manufacturing Research Ltd., Rathcoole, Ireland
Interests: human robot collaboration; deep reinforcement learning; robot vision and task and motion planning

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Guest Editor
Department of Automation Technology and Mechanical Engineering, Tampere University, 33100 Tampere, Finland
Interests: cognitive robotics; human robot interaction; learning from demonstration; swarm intelligence

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Guest Editor
Chair of Cyber-Physical-Systems, Montauniversität Leoben, 8700 Leoben, Austria
Interests: cyber physical systems; robot learning; human robot cooperation; computational modeling

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Guest Editor
Irish Manufacturing Research Ltd., Rathcoole, Ireland
Interests: generative AI; human machine interface; collaborative robotics; evolutionary intelligence

Special Issue Information

Dear Colleagues,

In the evolving landscape of Industry 5.0, the integration of human creativity and decision-making with the precision, consistency, and endurance of smart machines and intelligent algorithms is transforming industrial operations. This fusion, known as Collaborative Intelligence, represents the synergy between humans and machines, where the strengths of each complement and enhance the other. As we advance into the Industry 5.0 revolution, the focus has shifted from mere automation and efficiency to a more holistic approach that prioritizes the well-being of operators, promotes sustainability, and ensures resilience in industrial processes. This new paradigm emphasizes the digitization of industrial assets and the creation of human-centric workspaces, where digital tools not only augment human capabilities but also evolve roles to meet the demands of a rapidly changing industrial environment.

The goal of this Special Issue is to explore the diverse facets of Collaborative Intelligence within the context of Industry 5.0. We invite researchers, industry practitioners, and thought leaders to submit original research, case studies, and reviews that delve into, but are not limited to, the following topics:

  • Physical and Intuitive Human Robot Interaction;
  • Digital Twins and Cyber–Physical Systems
  • AR/VR/XR in Industry 5.0;
  • Mobile Co-manipulation for Agile Manufacturing;
  • Data-centric Decision Making;
  • Embodied Intelligence and Cognitive Robotics ;
  • Human-in-the-Loop Systems ;
  • Natural Language Interfaces for Shared Autonomy;
  • Adaptive Learning and Task Assignment;
  • Context-aware Hybrid AI Systems;
  • Ethics and Trust in Collaborative Intelligence.

Dr. Sunny Katyara
Dr. Roel Pieters
Prof. Dr. Elmar Rueckert
Dr. Carlos Garcia Santiago
Guest Editors

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Keywords

  • collaborative robotics
  • generative AI
  • developmental and cognitive robotics
  • agile manufacturing
  • sustainability and circular economy
  • mixed reality
  • mobile robotics
  • deep reinforcement learning
  • cyber physical system
  • robot learning
  • digital twins and robot simulations
  • safety, security, ethics and trust

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Published Papers (1 paper)

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Research

21 pages, 36735 KiB  
Article
Adaptive Navigation Based on Multi-Agent Received Signal Quality Monitoring Algorithm
by Hina Magsi, Madad Ali Shah, Ghulam E. Mustafa Abro, Sufyan Ali Memon, Abdul Aziz Memon, Arif Hussain and Wan-Gu Kim
Electronics 2024, 13(24), 4957; https://doi.org/10.3390/electronics13244957 - 16 Dec 2024
Viewed by 705
Abstract
In the era of industrial evolution, satellites are being viewed as swarm intelligence that does not rely on a single system but multiple constellations that collaborate autonomously. This has enhanced the potential of the Global Navigation Satellite System (GNSS) to contribute to improving [...] Read more.
In the era of industrial evolution, satellites are being viewed as swarm intelligence that does not rely on a single system but multiple constellations that collaborate autonomously. This has enhanced the potential of the Global Navigation Satellite System (GNSS) to contribute to improving position, navigation, and timing (PNT) services. However, multipath (MP) and non-line-of-sight (NLOS) receptions remain the prominent vulnerability for the GNSS in harsh environments. The aim of this research is to investigate the impact of MP and NLOS receptions on GNSS performance and then propose a Received Signal Quality Monitoring (RSQM) algorithm. The RSQM algorithm works in two ways. Initially, it performs a signal quality test based on a fuzzy inference system. The input parameters are carrier-to-noise ratio (CNR), Normalized Range Residuals (NRR), and Code–Carrier Divergence (CCD), and it computes the membership functions based on the Mamdani method and classifies the signal quality as LOS, NLOS, weak NLOS, and strong NLOS. Secondly, it performs an adaptive navigation strategy to exclude/mask the affected range measurements while considering the satellite geometry constraints (i.e., DOP2). For this purpose, comprehensive research to quantify the multi-constellation GNSS receiver with four constellation configurations (GPS, BeiDou, GLONASS, and Galileo) has been carried out in various operating environments. This RSQM-based GNSS receiver has the capability to identify signal quality and perform adaptive navigation accordingly to improve navigation performance. The results suggest that GNSS performance in terms of position error is improved from 5.4 m to 2.3 m on average in the complex urban environment. Combining the RSQM algorithm with the GNSS has great potential for the future industrial revolution (Industry 5.0), making things automatic and sustainable like autonomous vehicle operation. Full article
(This article belongs to the Special Issue Collaborative Intelligence in the Era of Industry 5.0)
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