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Automation
Special Issues
Automation: 5th Anniversary Feature Papers
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Automation: 5th Anniversary Feature Papers

A special issue of Automation (ISSN 2673-4052).

Deadline for manuscript submissions: closed (31 July 2025) | Viewed by 18835

Special Issue Editor

Prof. Dr. Eyad H. Abed

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering and the Institute for Systems Research, University of Maryland, College Park, MD 20742, USA
Interests: system and control theory; electric power system dynamics and control; systems engineering; the analysis and design of complex networks; communication networks; social networks; aerospace control systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The journal Automation was founded in 2020. During the past 5 years, Automation has published over 5 Special Issues and 100 papers and welcomed breakthrough and innovative papers in automation, control, and allied topics.

Automation covers a broad range of areas, such as studies related to the following topics: control theory, optimization algorithms, networked systems, condition monitoring, manufacturing systems, energy management systems, aerospace control systems, learning systems, intelligent control systems, artificial neural networks, motion control and sensing, supervisory control and data acquisition, process automation and monitoring, fault detection and diagnosis, robotics and applications, human–machine systems and interfaces, and automation-related devices and sensors.

The year 2024 marks the journal’s 5th anniversary as well as its acceptance for coverage in the Emerging Sources Citations Index (ESCI) in Web of Science. We are thus excited to celebrate Automation’s 5th anniversary with a Special Issue.

This Special Issue welcomes both research and review papers presenting innovative developments and insightful reviews on automation, control, and allied topics.

Prof. Dr. Eyad H. Abed
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 250 words) can be sent to the Editorial Office for assessment.

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. Automation is an international peer-reviewed open access quarterly 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 1200 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

  • automation
  • control theory
  • optimization algorithms

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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

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32 pages, 7156 KB  
Article
FEA-Guided Toolpath Compensation for Robotic Machining: An Integrated CAD/CAM/CAE Framework for Enhanced Accuracy
by Vasileios D. Sagias, Michail Koutroumpousis, Constantinos Stergiou, Antonios Tsolakis, George Kioroglou and Paraskevi Zacharia
Automation 2025, 6(4), 73; https://doi.org/10.3390/automation6040073 - 11 Nov 2025
Viewed by 414
Abstract
Industrial robots offer flexibility and cost advantages in machining applications but suffer from limited structural stiffness and dynamic instability, leading to significant positional errors. This study presents a simulation-driven framework for automated toolpath compensation in robotic machining, integrating computer-aided design, manufacturing, and engineering [...] Read more.
Industrial robots offer flexibility and cost advantages in machining applications but suffer from limited structural stiffness and dynamic instability, leading to significant positional errors. This study presents a simulation-driven framework for automated toolpath compensation in robotic machining, integrating computer-aided design, manufacturing, and engineering environments. Finite Element Analysis is employed to predict stress, deformation, and reaction forces during machining. These predictions guide dynamic adjustments to key process parameters, such as feed rate and spindle speed, to optimize performance and accuracy. An automated optimization procedure streamlines this process, enhancing toolpath efficiency and safety. The framework is validated through a case study involving the machining of an aluminum support bracket using a KUKA KR3 robot. Simulation results demonstrate significant improvements in path accuracy, shorter machining time and enhanced surface quality. The enhanced toolpath achieves a 10–15% reduction in non-cutting movements, a 5–10% improvement in surface finish and a 15–25% decrease in machining time compared to the initial configuration. This approach eliminates the need for hardware modifications or real-time sensors, providing a flexible and modular solution for achieving high precision outcomes in robotic machining. The work presents an automated methodology for compensating multi-source errors, bridging the gap between virtual analysis and physical execution. Full article
(This article belongs to the Special Issue Automation: 5th Anniversary Feature Papers)
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22 pages, 270 KB  
Article
Humanoid Robots like Tesla Optimus and the Future of Supply Chains: Enhancing Efficiency, Sustainability, and Workforce Dynamics
by Mohammad Shamsuddoha, Tasnuba Nasir and Mohammad Saifuddoha Fawaaz
Automation 2025, 6(1), 9; https://doi.org/10.3390/automation6010009 - 20 Feb 2025
Cited by 4 | Viewed by 16025
Abstract
Integrating futuristic humanoids like Tesla Optimus into supply chain operations represents groundbreaking automation and workforce efficiency innovation. This study investigates the potential of humanoids to address critical supply chain challenges, such as labor shortages, rising operational costs, and the demand for sustainable practices. [...] Read more.
Integrating futuristic humanoids like Tesla Optimus into supply chain operations represents groundbreaking automation and workforce efficiency innovation. This study investigates the potential of humanoids to address critical supply chain challenges, such as labor shortages, rising operational costs, and the demand for sustainable practices. Considering its ability to handle worker-intensive, hazardous, and repetitive duties, humanoids could offer an alternative to business challenges like inefficient operations, health and safety concerns, and worker shortages. Intelligent robotics plays an essential role in improving productivity, supporting sustainability, and transforming workforce dynamics as supply chains become increasingly complex. The study examines the effects of humanoids on workforce reallocation, manufacturing sustainability, and supply chain productivity. The current research reviews the usefulness, advantages, and downsides of integrating humanoids into supply chains. This study uses a mixed-method approach, incorporating case studies, qualitative productivity data, and expert interviews. According to Tesla, Optimus could significantly enhance supply chain efficiency by reducing error rates, streamlining workflows, and enabling 24/7 operations. It could also help meet sustainability goals by lowering waste and energy consumption. The study limits Tesla’s experience, modern technologies, and inadequate information from various industrial and geographical contexts. However, this study will be eye-opening for industries requiring such humanoid robots for their operations. Additional studies need to deal with factors like high implementation expenses, potential job displacement, and flexibility in changing supply chain demands. While focused on Tesla, this study provides insights that can inform broader applications of humanoid robotics in supply chains across industries. This study presents an in-depth review of humanoid involvement in developing future supply chain models. It also offers helpful knowledge that will assist industries in considering adopting comparable robotic integration as a strategic decision. Full article
(This article belongs to the Special Issue Automation: 5th Anniversary Feature Papers)

Review

Jump to: Research

59 pages, 648 KB  
Review
Survey on Graph-Based Reinforcement Learning for Networked Coordination and Control
by Yifan Liu, Dalei Wu and Yu Liang
Automation 2025, 6(4), 65; https://doi.org/10.3390/automation6040065 - 3 Nov 2025
Viewed by 1073
Abstract
A networked system consists of a collection of interconnected autonomous agents that communicate and interact through a shared communication infrastructure. These agents collaborate to pursue common objectives or exhibit coordinated behaviors that would be difficult or impossible for a single agent to achieve [...] Read more.
A networked system consists of a collection of interconnected autonomous agents that communicate and interact through a shared communication infrastructure. These agents collaborate to pursue common objectives or exhibit coordinated behaviors that would be difficult or impossible for a single agent to achieve alone. With widespread applications in domains such as robotics, smart grids, and communication networks, the coordination and control of networked systems have become a vital research focus—driven by the complexity of distributed interactions and decision-making processes. Graph-based reinforcement learning (GRL) has emerged as a powerful paradigm that combines reinforcement learning with graph signal processing and graph neural networks (GNNs) to develop policies that are relationally aware, scalable, and adaptable to diverse network topologies. This survey aims to advance research in this evolving area by providing a comprehensive overview of GRL in the context of networked coordination and control. It covers the fundamental principles of reinforcement learning and graph neural networks, examines state-of-the-art GRL models and algorithms, reviews training methodologies, discusses key challenges, and highlights real-world applications. By synthesizing theoretical foundations, empirical insights, and open research questions, this survey serves as a cohesive and structured resource for the study and advancement of GRL-enabled networked systems. Full article
(This article belongs to the Special Issue Automation: 5th Anniversary Feature Papers)
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