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Electronics
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Intelligence Control and Applications of Intelligence Robotics
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Intelligence Control and Applications of Intelligence Robotics

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Systems & Control Engineering".

Deadline for manuscript submissions: 20 October 2024 | Viewed by 6889

Special Issue Editors

Dr. Bumyong Park

E-Mail Website
Guest Editor
The Division of Electrical Engineering, Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi 39177, Republic of Korea
Interests: robust control; embedded system; robot control
Prof. Dr. Won Il Lee

E-Mail Website
Guest Editor
School of Electronic Engineering, Kumoh National Institute of Technology, Gumi 39177, Republic of Korea
Interests: delayed systems; artificial intelligence; robust control theory.

Special Issue Information

Dear Colleagues,

With the advancements made in artificial intelligence and robotics, intelligent control has become an essential area of research. Intelligent control deals with the development of algorithms that can control a system's behavior in order to achieve the desired objectives, while adapting to changing environments.

In recent years, there has been a significant increase in the application of intelligent control in robotics. Robotics has become an integral part of our lives, and intelligent robots are being used in various fields, including manufacturing, healthcare, and agriculture, among others. The integration of intelligent control in robotics has led to the development of intelligent robots that are capable of performing complex tasks and adapting to dynamic environments.

This Special Issue focuses on the recent advancements in intelligent control and its applications in the field of robotics. The issue includes research papers that provide insights into the current state-of-the-art techniques in intelligent control and their applications in robotics. The topics covered in this Special Issue include, but are not limited to, the following:

  • Intelligent control theory for robotic systems
  • Learning-based approaches for intelligent control
  • Multi-agent intelligent control for robotics
  • Applications of intelligent control in autonomous robots
  • Intelligent control for collaborative robots
  • Human–robot interaction with intelligent control
  • Intelligent control for swarm robotics
  • Robust control theory
  • Network control system

Dr. Bumyong Park
Prof. Dr. Won Il Lee
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

  • intelligent control
  • robotics
  • artificial intelligence
  • learning-based approaches
  • autonomous robots
  • multi-agent control
  • collaborative robots
  • human–robot interaction
  • swarm robotics
  • industrial robots
  • robust control
  • network control system

Published Papers (4 papers)

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Research

20 pages, 1775 KiB  
Article
Real-Time Traffic Light Recognition with Lightweight State Recognition and Ratio-Preserving Zero Padding
by Jihwan Choi and Harim Lee
Electronics 2024, 13(3), 615; https://doi.org/10.3390/electronics13030615 - 1 Feb 2024
Viewed by 717
Abstract
As online shopping is becoming mainstream, driven by the social impact of Coronavirus disease-2019 (COVID-19) as well as the development of Internet services, the demand for autonomous delivery mobile robots is rapidly increasing. This trend has brought the autonomous mobile robot market to [...] Read more.
As online shopping is becoming mainstream, driven by the social impact of Coronavirus disease-2019 (COVID-19) as well as the development of Internet services, the demand for autonomous delivery mobile robots is rapidly increasing. This trend has brought the autonomous mobile robot market to a new turning point, with expectations that numerous mobile robots will be driving on roads with traffic. To achieve these expectations, autonomous mobile robots should precisely perceive the situation on roads with traffic. In this paper, we revisit and implement a real-time traffic light recognition system with a proposed lightweight state recognition network and ratio-preserving zero padding, which is a two-stage system consisting of a traffic light detection (TLD) module and a traffic light status recognition (TLSR) module. For the TLSR module, this work proposes a lightweight state recognition network with a small number of weight parameters, because the TLD module needs more weight parameters to find the exact location of traffic lights. Then, the proposed effective and lightweight network architecture is constructed by using skip connection, multifeature maps with different sizes, and kernels of appropriately tuned sizes. Therefore, the network has a negligible impact on the overall processing time and minimal weight parameters while maintaining high performance. We also propose to utilize a ratio-preserving zero padding method for data preprocessing for the TLSR module to enhance recognition accuracy. For the TLD module, extensive evaluations with varying input sizes and backbone network types are conducted, and then appropriate values for those factors are determined, which strikes a balance between detection performance and processing time. Finally, we demonstrate that our traffic light recognition system, utilizing the TLD module’s determined parameters, the proposed network architecture for the TLSR module, and the ratio-preserving zero padding method can reliably detect the location and state of traffic lights in real-world videos recorded in Gumi and Deagu, Korea, while maintaining at least 30 frames per second for real-time operation. Full article
(This article belongs to the Special Issue Intelligence Control and Applications of Intelligence Robotics)
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19 pages, 3908 KiB  
Article
Neuro-Robotic Synergy: Crafting the Secure Future of Industries in the Post Pandemic Era
by Thierno Gueye, Asif Iqbal, Yanen Wang, Ray Tahir Mushtaq and Muhammad S. Abu Bakar
Electronics 2023, 12(19), 4137; https://doi.org/10.3390/electronics12194137 - 4 Oct 2023
Cited by 1 | Viewed by 867
Abstract
In recent years, ICSs have become increasingly commonplace in virtually every industry. The abbreviation “ICSs” refers to industrial control systems. These are specially designed computers used for monitoring, managing, and controlling procedures and tasks across a wide range of industries and vital infrastructure [...] Read more.
In recent years, ICSs have become increasingly commonplace in virtually every industry. The abbreviation “ICSs” refers to industrial control systems. These are specially designed computers used for monitoring, managing, and controlling procedures and tasks across a wide range of industries and vital infrastructure sectors. Production, power, disinfection of water, transport, and other sectors all greatly benefit from ICS use. The authors of this paper aim to detect ICS cyber hazards in industry. This article is the result of the writers’ extensive research on ICS programs and the impact of cyberattacks on them as well. The study narrowed its attention to just three ICS applications because there are simply too many to count: power plants, water reservoirs, and gas pipelines. The present paper focuses on the development and evaluation of neural networks for use in cyberattacks. An early form of neural network, the residual system, came first in the field. When a breach is detected in the ICS, the neural network sorts it into one of several categories. The produced datasets must not compromise users’ privacy or cause harm to the relevant industry if they fall into the wrong hands. An encoding device, decoder, pseudo-encoder, and critical model neural networks work together to generate random data. Finally, a set of trials is conducted in which a residual neural network is utilized to classify cyberattacks based on both the created and original datasets. Results from a series of studies indicate that using the created dataset is an effective technique to train high-quality neural networks for use in cybersecurity on a large amount of data without sacrificing the accuracy of the models. The Kullback-Leibler and Jensen-Shannon divergences also serve as the theoretical foundation and technique, respectively. In particular, the paper recommends operational and maintenance cybersecurity standards for ICS. This entails such things as secure password practices, patch management, and anti-malware defense. Physical safeguards for ICS is another topic that is covered. Full article
(This article belongs to the Special Issue Intelligence Control and Applications of Intelligence Robotics)
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12 pages, 2681 KiB  
Article
Design of a Four-Wheel Steering Mobile Robot Platform and Adaptive Steering Control for Manual Operation
by Beomsu Bae and Dong-Hyun Lee
Electronics 2023, 12(16), 3511; https://doi.org/10.3390/electronics12163511 - 19 Aug 2023
Cited by 3 | Viewed by 3400
Abstract
The recent advancementsin autonomous driving technology have led to an increased utilization of mobile robots across various industries. Notably, four-wheel steering robots have gained significant attention due to their robustness and agile maneuvering capabilities. This paper presents a novel four-wheel steering robot platform [...] Read more.
The recent advancementsin autonomous driving technology have led to an increased utilization of mobile robots across various industries. Notably, four-wheel steering robots have gained significant attention due to their robustness and agile maneuvering capabilities. This paper presents a novel four-wheel steering robot platform for research purposes and an adaptive four-wheel steering control algorithm for efficient manual operation. The proposed robot platform is specifically designed as a simple and compact research-oriented platform for developing navigation and manual operation of four-wheel steering robots. The compact design of the robot platform allows for additional space utilization, while the horizontal independent steering system provides precise control and enhanced maneuverability. The adaptive four-wheel steering control algorithm aims to offer efficient and intuitive manual operation of the four-wheel steering robot, aligning with the intentions of the human operator. It enables the platform to utilize front-wheel steering under normal circumstances and efficiently reduce the turning radius by employing rear wheel steering when additional steering input is required. Experimental results demonstrated the accurate steering performance of the robot platform and effectiveness of the adaptive steering algorithm. The developed four-wheel steering robot platform and the adaptive steering control algorithm serve as valuable tools for further research and development in the fields of autonomous driving and steering algorithms. Full article
(This article belongs to the Special Issue Intelligence Control and Applications of Intelligence Robotics)
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12 pages, 5023 KiB  
Communication
A Novel NLMS Algorithm for System Identification
by Jinwoo Yoo, Bum Yong Park, Won Il Lee and JaeWook Shin
Electronics 2023, 12(14), 3159; https://doi.org/10.3390/electronics12143159 - 20 Jul 2023
Cited by 1 | Viewed by 1419
Abstract
In this paper, we propose a novel normalized least mean squares (NLMS) algorithm for system identification applications. Our approach involves analyzing the mean squared deviation performance of the NLMS algorithm using a random walk model to select two optimal parameters, the step size [...] Read more.
In this paper, we propose a novel normalized least mean squares (NLMS) algorithm for system identification applications. Our approach involves analyzing the mean squared deviation performance of the NLMS algorithm using a random walk model to select two optimal parameters, the step size and regularization parameters, for the rapid convergence of the colored input signals. We verified that the proposed algorithm exhibited faster convergence than existing algorithms, even in scenarios of sudden system changes. Full article
(This article belongs to the Special Issue Intelligence Control and Applications of Intelligence Robotics)
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