Advancing Human-Robot Collaboration in Industry 4.0
A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Robotics, Mechatronics and Intelligent Machines".
Deadline for manuscript submissions: closed (31 October 2024) | Viewed by 4972
Special Issue Editor
Interests: predictive production planning; industrial engineering; process mapping and digitisation
Special Issue Information
Dear Colleagues,
Production process planning and management requires stable actions based on relevant collaborations between human and cobot. The performance and efficiency of complex manufacturing depends on real-time data analytics, flexible algorithms, synchronization, and the coexistence of human and robot. Currently, research is based on intelligent systems, real-time analytics, and relevant manufacturing knowledge. An important part of this research is the predictive modelling and simulation of collaborative workplaces. Crucial knowledge combines interconnected systems and real-time data with multicriterial decision making and optimization. Core metrics such as OEE and throughput are analysed against downtimes and workplace efficiency with relevant attention given to human safety and human–cobot maintenance conditions. The contributions required in this area are connected with dynamic scheduling for production systems, multi-agent systems, and machine learning technologies. The integration of a competitive production environment brings new ideas for collaborative workplace optimisation, and it integrates discrete simulation and adequate data analytics. An integrated blockchain network is required for responsible manufacturing and relevant layout design. Moreover, research-oriented on-time variability, batching modelling, and control strategies are required according to the production profitability of collaborative human–cobot workplaces and production systems.
We are inviting you to contribute papers to this Special Issue on “Advancing Human-Robot Collaboration in Industry 4.0”. This Special Issue represents a contribution to the theoretical and practical approaches for researchers and professionals in the area of advanced concepts of optimal coexistence. The submitted research papers are oriented to the following:
- Data analytics concepts of human–cobot workplace planning;
- Process management of flexibility and multitasking in real time;
- Modelling and simulation of optimal production flow;
- Concepts of collaborative workplace psychology and human–cobot ethics.
Prof. Dr. Felicita Chromjaková
Guest Editor
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. Machines is an international peer-reviewed open access monthly 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
- data
- analytics
- flexibility
- stability
- efficiency
- collaboration
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