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Applied Sciences
Special Issues
Human-Robot Collaboration in Industry 4.0
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Human-Robot Collaboration in Industry 4.0

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

Deadline for manuscript submissions: closed (20 October 2023) | Viewed by 1638

Special Issue Editors

Prof. Dr. Angel Valera

E-Mail Website
Guest Editor
Higher Technical School of Computer Engineering, Polytechnic University of Valencia, Camino de Vera, s/n, 46022 Valencia, Spain
Interests: human–robot collaboration; Industry 4.0; position and force robot control; artificial intelligence; advanced robotics; industrial applications; safety systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Marina Valles

E-Mail Website
Guest Editor
Departamento de Ingeniería de Sistemas y Automática, Instituto de Automática e Informática Industrial, Universitat Politècnica de València, 46022 Valencia, Spain
Interests: human–robot collaboration; Industry 4.0; position and force robot control; artificial intelligence; advanced robotics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We can consider Industry 4.0 as the trend towards automation and data sharing in manufacturing technologies and processes. It conceptualizes rapid change to technology, industries, and societal patterns and processes in the 21st century due to increasing interconnectivity and smart automation, including cyber-physical systems, IoT, industrial internet of things, cloud computing, cognitive computing and artificial intelligence.

In the present era of Industry 4.0, manufacturing automation is moving toward mass production and mass customization through human–robot collaboration (HRC). It is a new trend in the field of industrial and service robotics as part of the Industry 4.0 strategy. The main objective of this innovative strategy is to create an environment of safe collaboration between humans and robots.

Robots help humans with non-ergonomic, repetitive, uncomfortable or even dangerous operations with high precision and repeatability. Humans can quickly identify hazards and apply them in decision making thanks to their intelligence and flexibility. The integration of the human and robot characteristics can build an efficient collaborative system to bring an enormous improvement in flexibility.

This Special Issue aims to publish high-quality papers and disseminate the latest research achievements, findings, and ideas in the field of human–robot collaboration in Industry 4.0. The recommended topics include, but are not limited to:

  • Human–robot collaboration and interaction for industrial applications.
  • Collaborative and cooperative robots.
  • Industry 4.0: digital manufacturing systems and applications areas.
  • Used technologies.
  • Case studies.
  • Advantages of the solution.

Prof. Dr. Angel Valera
Prof. Dr. Marina Valles
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

  • human–robot collaboration
  • Industry 4.0
  • position and force robot control
  • artificial intelligence
  • advanced robotics
  • industrial applications
  • safety systems

Published Papers (1 paper)

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Research

11 pages, 2409 KiB  
Article
Test–Retest Repeatability of Human Gestures in Manipulation Tasks
by Elisa Digo, Elena Caselli, Michele Polito, Mattia Antonelli, Laura Gastaldi and Stefano Pastorelli
Appl. Sci. 2023, 13(13), 7808; https://doi.org/10.3390/app13137808 - 2 Jul 2023
Viewed by 860
Abstract
The importance of performance excellence and operator’s safety is fundamental not only when operators perform repetitive and controlled industrial tasks, but also in case of abrupt gestures due to inattention and unexpected circumstances. Since optical systems work at frequencies that are too low [...] Read more.
The importance of performance excellence and operator’s safety is fundamental not only when operators perform repetitive and controlled industrial tasks, but also in case of abrupt gestures due to inattention and unexpected circumstances. Since optical systems work at frequencies that are too low and they are not able to detect gestures as early as possible, combining the use of wearable magneto-inertial measurement units (MIMUs) with the adoption of deep learning techniques can be useful to instruct the machine about human motion. To improve the initial training phase of neural networks for high classification performance, gesture repeatability over time has to be verified. Since the test–retest approach has been poorly applied based on MIMUs signals in a context of human–machine interaction, the aim of this work was to evaluate the repeatability of pick-and-place gestures composed of both normal and abrupt movements. Overall, results demonstrated an excellent test–retest repeatability for normal movements and a fair-to-good test–retest repeatability for abrupt movements. In addition, results suggested important information about the application of deep learning to identify the types of movements: the test showed how to improve reinforcement learning for the identification of onset gestures, whereas the retest allowed for defining the time necessary to retrain the network. Full article
(This article belongs to the Special Issue Human-Robot Collaboration in Industry 4.0)
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