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Electronics
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Collaborative Intelligence in the Era of Industry 5.0
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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 2076

Special Issue Editors

Dr. Sunny Katyara

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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
Dr. Roel Pieters

E-Mail Website
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
Prof. Dr. Elmar Rueckert

E-Mail Website
Guest Editor
Chair of Cyber-Physical-Systems, Montauniversität Leoben, 8700 Leoben, Austria
Interests: cyber physical systems; robot learning; human robot cooperation; computational modeling
Dr. Carlos Garcia Santiago

E-Mail Website
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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

25 pages, 3893 KiB  
Article
Electro-Thermal Model-Based Design of a Smart Latch in Automotive Systems for Performance and Reliability Evaluations
by Damiano Nardi, Pierpaolo Dini and Sergio Saponara
Electronics 2025, 14(10), 1962; https://doi.org/10.3390/electronics14101962 - 12 May 2025
Viewed by 206
Abstract
Industry 5.0 places growing emphasis on intelligent and efficient design methodologies aiming to reduce development times, accelerate the time-to-market, and enhance human–machine collaboration in creating new products. This article proposes the use of a model-based design (MBD) approach to developing a detailed electro-thermal [...] Read more.
Industry 5.0 places growing emphasis on intelligent and efficient design methodologies aiming to reduce development times, accelerate the time-to-market, and enhance human–machine collaboration in creating new products. This article proposes the use of a model-based design (MBD) approach to developing a detailed electro-thermal model (ETDM) of a Smart Latch Mechanism (SLM) used in automotive door automation systems. The proposed ETDM enhances the accuracy of the design and verification processes and enables the simulation of specific scenarios, such as fault conditions, within a virtual environment. The simulation-based framework presented in this article leverages partial knowledge of the system to enable rapid estimations of the performance and functional validation. It encompasses the injection of disturbances, the analysis of failure scenarios, and the use of processor-in-the-loop (PIL) procedures for validation purposes. This work aims to employ detailed modeling and simulation techniques and use publicly available technical data and work from the literature to eliminate the need for physical testing and instrumentation, enabling the development of models that accurately reflect the real-world behavior under defined operating conditions. The proposed framework has the potential to facilitate rapid prototyping and system reconfiguration, contributing to shorter development cycles and improved industrial efficiency by reducing both production times and the associated costs for established automotive subsystems where high precision is nonessential. Full article
(This article belongs to the Special Issue Collaborative Intelligence in the Era of Industry 5.0)
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41 pages, 2621 KiB  
Article
Trust by Design: An Ethical Framework for Collaborative Intelligence Systems in Industry 5.0
by Emmanuel A. Merchán-Cruz, Ioseb Gabelaia, Mihails Savrasovs, Mark F. Hansen, Shwe Soe, Ricardo G. Rodriguez-Cañizo and Gerardo Aragón-Camarasa
Electronics 2025, 14(10), 1952; https://doi.org/10.3390/electronics14101952 - 11 May 2025
Viewed by 408
Abstract
Industry 5.0 highlights human-centricity, sustainability, and resilience. This article presents a novel Trust by Design framework applicable to collaborative intelligence systems within Industry 5.0, addressing the need for collaborative systems to be reliable by design, incorporating ethical principles such as transparency, accountability, fairness, [...] Read more.
Industry 5.0 highlights human-centricity, sustainability, and resilience. This article presents a novel Trust by Design framework applicable to collaborative intelligence systems within Industry 5.0, addressing the need for collaborative systems to be reliable by design, incorporating ethical principles such as transparency, accountability, fairness, and privacy throughout the entire system lifecycle. The framework is grounded in select ethical philosophies applied to practical design requirements for human-AI collaboration, identifying key ethical challenges that threaten to damage trust and restrict the adoption of collaborative systems. The authors employ a qualitative, literature-driven method, conceptual modeling, and scenario-based case study analysis, synthesizing best practices and ethical policies from the EU AI Act, GDPR, and more. Trust by Design suggests a structured set of principles and implementation measures to embed ethics into every phase of the system’s lifecycle. The applicability and suitability of the framework are demonstrated through representative real-world application scenarios across industries. The results indicate that trust in collaborative intelligence systems is not static but dynamic, context-dependent, and controlled by transparency, fairness, and user experience. The framework includes instruments and methods to measure ethical performance, including trust metrics, override rates, fairness indicators, and incident tracking. Full article
(This article belongs to the Special Issue Collaborative Intelligence in the Era of Industry 5.0)
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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 776
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., DOP≤2). 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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