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Intelligent Robotics in the Era of Industry 5.0
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Intelligent Robotics in the Era of Industry 5.0

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Robotics and Automation".

Deadline for manuscript submissions: 20 June 2025 | Viewed by 7564

Special Issue Editors

Dr. Ye He

E-Mail Website
Guest Editor
College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400044, China
Interests: mechanical innovation design method; electromechanical drive and intelligent control; man–machine collaboration and control; motion systems and bionic drives; mechanical metamaterials and applications; exoskeletons and smart wearable systems
Prof. Dr. Don McGlinchey

E-Mail Website
Guest Editor
Department of Mechanical Engineering, Glasgow Caledonian University, Glasgow G4 0BA, UK
Interests: modelling and simulation for digital twins; intelligent maintenance

Special Issue Information

Dear Colleagues,

Industry 5.0 aims to realize fully intelligentized and human-centered manufacturing systems through the convergence of advanced technologies such as artificial intelligence (AI), robotics, Internet of Things (IoT), cloud computing, big data analytics, and cyber-physical systems. This brings new opportunities and challenges for the research and development of intelligent robotics and collaborative systems that can seamlessly interact with humans in highly automated production environments.

This special issue aims to bring together the latest advances in intelligent robotics, human-robot collaboration, and AI-enabled manufacturing systems towards realizing the vision of Industry 5.0. Original research contributions as well as comprehensive review articles are welcomed in areas related to exoskeleton systems, human-robot interface, collaborative robots, intelligent robot control, cyber-physical systems, digital twins, AI-in-the-loop systems etc. We welcome contributions that include, but are not limited to, the following themes:

  • Exoskeleton systems
  • Human-robot interface and interaction
  • Collaborative robots
  • Wheeled-legged robots
  • Industry 4.0/5.0 technologies and applications
  • Applied machine learning and deep learning
  • Computer vision and sensor fusion
  • Data analytics and decision making
  • Robotic, mechatronic and manufacturing automation systems
  • Modeling and control of manufacturing machinery/processes
  • Digital twins and digital thread integration
  • Simulation and emulation of production systems
  • Cloud/edge computing platforms
  • Additive manufacturing
  • Micro/nano-fabrication
  • Machining and forming
  • Materials and processes
  • Sensor integration and industrial internet platforms
  • Wireless embedded intelligent systems
  • Future of industrial workers
  • Human-robot collaboration
  • AR/VR for operators
  • Green processes and assessment
  • Process monitoring and optimization
  • Blockchain for manufacturing
  • Distributed systems and control
  • Ecosystem and platform models

Dr. Ye He
Prof. Dr. Don McGlinchey
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. Applied Sciences 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

  • intelligent robotics and robotics systems
  • artificial intelligence for manufacturing
  • automation and control
  • cyber-physical and digital manufacturing systems
  • advanced manufacturing technologies
  • industrial IoT and networks
  • human aspects
  • sustainable manufacturing
  • emerging topics

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

Published Papers (5 papers)

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Research

19 pages, 9209 KiB  
Article
Optimizing Energy and Air Consumption in Smart Manufacturing: An Industrial Internet of Things-Based Monitoring and Efficiency Enhancement Solution
by Shahram Hanifi, Babakalli Alkali, Gordon Lindsay and Don McGlinchey
Appl. Sci. 2025, 15(6), 3222; https://doi.org/10.3390/app15063222 - 15 Mar 2025
Viewed by 701
Abstract
The rising cost of energy and the urgent need for sustainability have driven industries to adopt smarter solutions for monitoring and optimizing resource consumption. In this study, we present an Industrial Internet of Things (IIoT)-based approach for real-time energy and air consumption monitoring [...] Read more.
The rising cost of energy and the urgent need for sustainability have driven industries to adopt smarter solutions for monitoring and optimizing resource consumption. In this study, we present an Industrial Internet of Things (IIoT)-based approach for real-time energy and air consumption monitoring in manufacturing, focusing on a legacy Turret Punch Press (TPP) at Mitsubishi Electric Air Conditioning Systems Europe Ltd. (M-ACE). Due to its age and lack of modern monitoring capabilities, the machine was suspected to be inefficient, requiring a retrofitting strategy for improved transparency and optimization. To address these challenges, a structured IIoT-enabled monitoring system was deployed, integrating KEYENCE MP-F series sensors, an energy monitoring module, and Ethernet communication via Modbus TCP/IP. A comprehensive dashboarding system was developed for real-time visualization and analysis of energy consumption trends, identifying inefficiencies and optimizing machine usage. The data-driven approach revealed significant energy savings of up to 56% and uncovered hidden inefficiencies, including a persistent air leak. By implementing a smart shut-off valve triggered by real-time power consumption data, unnecessary air leakage was eliminated, reducing compressed air waste and overall energy costs. The results demonstrate the effectiveness of IIoT-based retrofitting for industrial energy efficiency, showcasing a scalable framework that can be applied across various machines and production environments. This study highlights the importance of data-driven decision-making in smart manufacturing, contributing to both cost reduction and sustainability goals in industrial settings. Full article
(This article belongs to the Special Issue Intelligent Robotics in the Era of Industry 5.0)
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25 pages, 25441 KiB  
Article
Integrating Radar-Based Obstacle Detection with Deep Reinforcement Learning for Robust Autonomous Navigation
by Nabih Pico, Estrella Montero, Maykoll Vanegas, Jose Miguel Erazo Ayon, Eugene Auh, Jiyou Shin, Myeongyun Doh, Sang-Hyeon Park and Hyungpil Moon
Appl. Sci. 2025, 15(1), 295; https://doi.org/10.3390/app15010295 - 31 Dec 2024
Cited by 4 | Viewed by 1394
Abstract
This study presents an approach to autonomous navigation for wheeled robots, combining radar-based dynamic obstacle detection with a BiGRU-based deep reinforcement learning (DRL) framework. Using filtering and tracking algorithms, the proposed system leverages radar sensors to cluster object points and track dynamic obstacles, [...] Read more.
This study presents an approach to autonomous navigation for wheeled robots, combining radar-based dynamic obstacle detection with a BiGRU-based deep reinforcement learning (DRL) framework. Using filtering and tracking algorithms, the proposed system leverages radar sensors to cluster object points and track dynamic obstacles, enhancing precision by reducing noise and fluctuations. A BiGRU-enabled DRL model is introduced, allowing the robot to process sequential environmental data and make informed decisions in dynamic and unpredictable environments, achieving collision-free paths and reaching the goal. Simulation and experimental results validate the proposed method’s efficiency and adaptability, highlighting its potential for real-world applications in dynamic scenarios. Full article
(This article belongs to the Special Issue Intelligent Robotics in the Era of Industry 5.0)
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16 pages, 3100 KiB  
Article
Efficient Robot Manipulation via Reinforcement Learning with Dynamic Movement Primitives-Based Policy
by Shangde Li, Wenjun Huang, Chenyang Miao, Kun Xu, Yidong Chen, Tianfu Sun and Yunduan Cui
Appl. Sci. 2024, 14(22), 10665; https://doi.org/10.3390/app142210665 - 18 Nov 2024
Cited by 1 | Viewed by 1812
Abstract
Reinforcement learning (RL) that autonomously explores optimal control policies has become a crucial direction for developing intelligent robots while Dynamic Movement Primitives (DMPs) serve as a powerful tool for efficiently expressing robot trajectories. This article explores an efficient integration of RL and DMP [...] Read more.
Reinforcement learning (RL) that autonomously explores optimal control policies has become a crucial direction for developing intelligent robots while Dynamic Movement Primitives (DMPs) serve as a powerful tool for efficiently expressing robot trajectories. This article explores an efficient integration of RL and DMP to enhance the learning efficiency and control performance of reinforcement learning in robot manipulation tasks by focusing on the forms of control actions and their smoothness. A novel approach, DDPG-DMP, is proposed to address the efficiency and feasibility issues in the current RL approaches that employ DMP to generate control actions. The proposed method naturally integrates a DMP-based policy into the actor–critic framework of the traditional RL approach Deep Deterministic Policy Gradient (DDPG) and derives the corresponding update formulas to learn the networks that properly decide the parameters of DMPs. A novel inverse controller is further introduced to adaptively learn the translation from observed states into various robot control signals through DMPs, eliminating the requirement for human prior knowledge. Evaluated on five robot arm control benchmark tasks, DDPG-DMP demonstrates significant advantages in control performance, learning efficiency, and smoothness of robot actions compared to related baselines, highlighting its potential in complex robot control applications. Full article
(This article belongs to the Special Issue Intelligent Robotics in the Era of Industry 5.0)
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14 pages, 5261 KiB  
Article
A Methodology for the Mechanical Design of Pneumatic Joints Using Artificial Neural Networks
by Michele Gabrio Antonelli, Pierluigi Beomonte Zobel, Enrico Mattei and Nicola Stampone
Appl. Sci. 2024, 14(18), 8324; https://doi.org/10.3390/app14188324 - 15 Sep 2024
Cited by 2 | Viewed by 1254
Abstract
The advent of collaborative and soft robotics has reduced the mandatory adoption of safety barriers, pushing human–robot interaction to previously unreachable levels. Due to their reciprocal advantages, integrating these technologies can maximize a device’s performance. However, simplifying assumptions or elementary geometries are often [...] Read more.
The advent of collaborative and soft robotics has reduced the mandatory adoption of safety barriers, pushing human–robot interaction to previously unreachable levels. Due to their reciprocal advantages, integrating these technologies can maximize a device’s performance. However, simplifying assumptions or elementary geometries are often required due to non-linear factors that identify analytical models for designing soft pneumatic actuators for collaborative and soft robotics. Over time, various approaches have been employed to overcome these issues, including finite element analysis, response surface methodology (RSM), and machine learning (ML) algorithms. Based on the latter, in this study, the bending behavior of an externally reinforced soft pneumatic actuator was characterized by the changing geometric and functional parameters, realizing a Bend dataset. This was used to train 14 regression algorithms, and the Bilayered neural network (BNN) was the best. Three different external reinforcements, excluded for the realization of the dataset, were tested by comparing the predicted and experimental bending angles. The BNN demonstrated significantly lower error than that obtained by RSM, validating the methodology and highlighting how ML techniques can advance the prediction and mechanical design of soft pneumatic actuators. Full article
(This article belongs to the Special Issue Intelligent Robotics in the Era of Industry 5.0)
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23 pages, 2081 KiB  
Article
Towards a Cognition-Based Framework Describing Interdisciplinary Expert Team Processes for Cognitive Robotics in Industry 5.0 Technologies
by Tina Morgenstern, Anja Klichowicz, Philip Bengler, Marcel Todtermuschke and Franziska Bocklisch
Appl. Sci. 2024, 14(10), 4121; https://doi.org/10.3390/app14104121 - 13 May 2024
Cited by 2 | Viewed by 1468
Abstract
With the evolution of traditional production towards smart manufacturing, humans and machines interact dynamically to handle complex production systems in semi-automated environments when full automation is not possible. To avoid undesirable side effects, and to exploit the full performance potential of experts, it [...] Read more.
With the evolution of traditional production towards smart manufacturing, humans and machines interact dynamically to handle complex production systems in semi-automated environments when full automation is not possible. To avoid undesirable side effects, and to exploit the full performance potential of experts, it is crucial to consider the human perspective when developing new technologies. Specifically, human sub-tasks during machine operation must be described to gain insights into cognitive processes. This research proposes a cognition-based framework by integrating a number of known psychological concepts. The focus is on the description of cognitive (team) processes in the resolution of anomalies within a manufacturing process with interdisciplinary experts working together. An observational eye tracking study with retrospective think-aloud interviews (N = 3) provides empirical evidence for all cognitive processes proposed in the framework, such as regular process monitoring and—in case of a detected anomaly—diagnosis, problem solving, and resolution. Moreover, the role of situation awareness, individual expertise and (cognitive) team processes is analyzed and described. Further, implications regarding a human-centered development of future production systems are discussed. The present research provides a starting point for understanding and supporting cognitive (team) processes during intelligent manufacturing that will dominate the production landscape within Industry 5.0. Full article
(This article belongs to the Special Issue Intelligent Robotics in the Era of Industry 5.0)
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