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Electronics
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Intelligent Control and Computing in Advanced Robotics
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Intelligent Control and Computing in Advanced Robotics

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

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 7740

Special Issue Editors

Prof. Dr. Byoung-Wook Choi

E-Mail Website
Guest Editor
Department of Electrical and Information Engineering, Seoul National University of Science and Technology, Seoul 01811, Korea
Interests: real-time system design; embedded systems; intelligent robot software
Dr. Raimarius Delgado

E-Mail Website
Guest Editor
Control Technology Group, Kohyoung Technology, Seoul 08588, Korea
Interests: real-time systems; medical and service robotics; embedded systems; systems and software architecture; industrial automation

Special Issue Information

Dear Colleagues,

The continuous advancements in the computer power of microcontrollers and embedded systems have promoted the increasing development of intelligent control and computing systems (ICCS) in multiple application domains. The common goal of such systems is to integrate artificial intelligence with engineering systems, to combine innovative processing and computing with mechatronics, robotics, and industrial automation systems.

This main purpose of this Special Issue is to collect contributions regarding recent advances and trend analysis in the wide spectrum of research fields which include algorithm development with rule-based knowledge AI modeling, neural networks, genetic algorithms, and deep learning. Furthermore, combination of these high-level computing with traditional data-driven control strategies may include trajectory planning, SLAM, and navigation. 

We encourage researchers in this field to contribute their original papers to share their technical achievements with the readers.

Prof. Dr. Byoung-Wook Choi
Dr. Raimarius Delgado
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

  • robot control system
  • machine learning
  • deep learning
  • computer vision
  • object recognition
  • robot navigation
  • SLAM
  • human–robot interface

Published Papers (4 papers)

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Research

22 pages, 1350 KiB  
Article
Causal-Based Approaches to Explain and Learn from Self-Extension—A Review
by Rebeca Marfil, Pablo Bustos and Antonio Bandera
Electronics 2024, 13(7), 1169; https://doi.org/10.3390/electronics13071169 - 22 Mar 2024
Viewed by 708
Abstract
The last decades have seen a revolution in autonomous robotics. Deep learning approaches and their hardware implementations have made it possible to endow robots with extraordinary perceptual capabilities. In addition, they can benefit from advances in Automated Planning, allowing them to autonomously solve [...] Read more.
The last decades have seen a revolution in autonomous robotics. Deep learning approaches and their hardware implementations have made it possible to endow robots with extraordinary perceptual capabilities. In addition, they can benefit from advances in Automated Planning, allowing them to autonomously solve complex tasks. However, on many occasions, the robot still acts without internalising and understanding the reasons behind a perception or an action, beyond an immediate response to a current state of the context. This gap results in limitations that affect its performance, reliability, and trustworthiness. Deep learning alone cannot bridge this gap because the reasons behind behaviour, when it emanates from a model in which the world is a black-box, are not accessible. What is really needed is an underlying architecture based on deeper reasoning. Among other issues, this architecture should enable the robot to generate explanations, allowing people to know why the robot is performing, or has performed, a certain action, or the reasons that may have caused a certain plan failure or perceptual anomaly. Furthermore, when these explanations arise from a cognitive process and are shared, and thus validated, with people, the robot should be able to incorporate these explanations into its knowledge base, and thus use this understanding to improve future behaviour. Our article looks at recent advances in the development of self-aware, self-evolving robots. These robots are designed to provide the necessary explanations to their human counterparts, thereby enhancing their functional capabilities in the quest to gain their trust. Full article
(This article belongs to the Special Issue Intelligent Control and Computing in Advanced Robotics)
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58 pages, 14528 KiB  
Article
Architectural Proposal for Low-Cost Brain–Computer Interfaces with ROS Systems for the Control of Robotic Arms in Autonomous Wheelchairs
by Fernando Rivas, Jesús Enrique Sierra and Jose María Cámara
Electronics 2024, 13(6), 1013; https://doi.org/10.3390/electronics13061013 - 7 Mar 2024
Viewed by 1110
Abstract
Neurodegenerative diseases present significant challenges in terms of mobility and autonomy for patients. In the current context of technological advances, brain–computer interfaces (BCIs) emerge as a promising tool to improve the quality of life of these patients. Therefore, in this study, we explore [...] Read more.
Neurodegenerative diseases present significant challenges in terms of mobility and autonomy for patients. In the current context of technological advances, brain–computer interfaces (BCIs) emerge as a promising tool to improve the quality of life of these patients. Therefore, in this study, we explore the feasibility of using low-cost commercial EEG headsets, such as Neurosky and Brainlink, for the control of robotic arms integrated into autonomous wheelchairs. These headbands, which offer attention and meditation values, have been adapted to provide intuitive control based on the eight EEG signal values read from Delta to Gamma (high and low/medium Gamma) collected from the users’ prefrontal area, using only two non-invasive electrodes. To ensure precise and adaptive control, we have incorporated a neural network that interprets these values in real time so that the response of the robotic arm matches the user’s intentions. The results suggest that this combination of BCIs, robotics, and machine learning techniques, such as neural networks, is not only technically feasible but also has the potential to radically transform the interaction of patients with neurodegenerative diseases with their environment. Full article
(This article belongs to the Special Issue Intelligent Control and Computing in Advanced Robotics)
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14 pages, 5087 KiB  
Article
Feasibility Study for a Python-Based Embedded Real-Time Control System
by Se Yeon Cho, Raimarius Delgado and Byoung Wook Choi
Electronics 2023, 12(6), 1426; https://doi.org/10.3390/electronics12061426 - 16 Mar 2023
Cited by 2 | Viewed by 3474
Abstract
Because of its simplicity and the support of numerous useful libraries, Python has become one of the most popular programming languages for application development, even in embedded systems. However, in existing control systems where specific tasks must meet specific temporal deadlines and support [...] Read more.
Because of its simplicity and the support of numerous useful libraries, Python has become one of the most popular programming languages for application development, even in embedded systems. However, in existing control systems where specific tasks must meet specific temporal deadlines and support schedulability with proper priority assignments, the Python interpreter may not satisfy real-time requirements, owing to features such as the global interpreter lock and garbage collector. This paper addresses these constraints with an approach that executes periodic real-time tasks under the fixed-priority preemptible scheduler of RT-Preempt. First, we implemented a Python real-time module that allows users to create and execute periodic tasks with fixed priorities based on Python. Then, we conducted experiments on an open embedded system, in this case, a Raspberry Pi 4. We evaluated the real-time performance, focusing on test metrics for control systems, such as task periodicity, responsiveness, and interrupt response. The results were then compared to those of conventional real-time tasks developed using the C language to validate the feasibility of the proposed method. Finally, we performed experimental validation by tracking the position of EtherCAT servo motors to demonstrate the feasibility of a Python-based real-time control system in a practical application. Full article
(This article belongs to the Special Issue Intelligent Control and Computing in Advanced Robotics)
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23 pages, 2752 KiB  
Article
Feature-Selection-Based Attentional-Deconvolution Detector for German Traffic Sign Detection Benchmark
by Junho Chung, Sangkyoo Park, Dongsung Pae, Hyunduck Choi and Myotaeg Lim
Electronics 2023, 12(3), 725; https://doi.org/10.3390/electronics12030725 - 1 Feb 2023
Cited by 2 | Viewed by 1703
Abstract
In this study, we propose a novel traffic sign detection algorithm based on the deeplearning approach. The proposed algorithm, which we termed the feature-selection-based attentionaldeconvolution detector (FSADD), is used along with the “you look only once” (YOLO) v5 structure for feature selection. When [...] Read more.
In this study, we propose a novel traffic sign detection algorithm based on the deeplearning approach. The proposed algorithm, which we termed the feature-selection-based attentionaldeconvolution detector (FSADD), is used along with the “you look only once” (YOLO) v5 structure for feature selection. When applying feature selection inside a detection algorithm, the network divides the extracted feature maps after the convolution layer into similar and non similar feature maps. Generally, the feature maps obtained after the convolution layers are the outputs of filters with random weights. Owing to the randomness of the filter, the network obtains various kinds of feature maps with unnecessary components, which degrades the detection performance. However, grouping feature maps with high similarities can increase the relativeness of each feature map, thereby improving the network detection of specific targets from images. Furthermore, the proposed FSADD model has modified sizes of the receptive fields for improved traffic sign detection performance. Many of the available general detection algorithms are unsuitable for the German traffic sign detection benchmark (GTSDB) because of the small sizes of these signs in the images. Experimental comparisons were performed with respect to the GTSDB to show that the proposed FSADD is comparable to the state-of-the-art while detecting 29 kinds of traffic signs with 73.9% accuracy of classification performances. Full article
(This article belongs to the Special Issue Intelligent Control and Computing in Advanced Robotics)
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