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Advanced Mechatronics and Robotics Technologies in Industry 4.0 Era: Intelligence...
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Advanced Mechatronics and Robotics Technologies in Industry 4.0 Era: Intelligence and Automation

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Power Electronics".

Deadline for manuscript submissions: closed (15 January 2024) | Viewed by 9550

Special Issue Editors

Dr. David Valiente

E-Mail Website
Guest Editor
Communications Engineering Department and Institute for Engineering Research (I3E) at Miguel Hernandez University, 03202 Alicante, Spain
Interests: robotics; computer vision; automation; electronics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Nuno Miguel Fonseca Ferreira

E-Mail Website
Guest Editor
Department of Electrical Engineering, Polytechnic of Coimbra, P-3004 516 Coimbra, Portugal
Interests: industrial robotics; automation; cooperative robots
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Robotics has emerged as a paramount area within the field of mechatronics. The latest advances in the design of systems supported by mechanical, electrical and computer engineering have made feasible the enhancement of many manufacturing processes prominent in the new Industry Era 4.0.

This Special Issue aims to promote open-ended applications of mechatronic and robotics systems as novel solutions to challenges in the Industry 4.0, including learning and teaching scopes within the educational framework.

We welcome original research articles and reviews in research areas including (but not limited to) the following:

  • Industrial robots;
  • Industrial automation;
  • Industrial sensors;
  • Modular robots;
  • Intelligent systems;
  • Robot manufacturers;
  • Robot kinematics;
  • Manipulators;
  • Robot perception;
  • Field robotics;
  • Robot control;
  • Localization and mapping;
  • Computer vision;
  • Image processing;
  • Robotics in education.

Dr. David Valiente
Prof. Dr. Nuno Miguel Fonseca Ferreira
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

  • manipulators
  • robotic manufacturing
  • industrial robots
  • industrial automation
  • localization and mapping

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Published Papers (2 papers)

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Research

24 pages, 6957 KiB  
Article
Adaptive Backstepping Hierarchical Sliding Mode Control for 3-Wheeled Mobile Robots Based on RBF Neural Networks
by Son Tung Dang, Xuan Minh Dinh, Thai Dinh Kim, Hai Le Xuan and Manh-Hung Ha
Electronics 2023, 12(11), 2345; https://doi.org/10.3390/electronics12112345 - 23 May 2023
Cited by 16 | Viewed by 3057
Abstract
This paper proposes a new adaptive controller for three-wheeled mobile robots (3WMRs) called the ABHSMC controller. This ABHSMC controller is developed through a cooperative approach, combining a backstepping controller and a Radial Basis Function (RBF) neural network-based Hierarchical Sliding Mode Controller (HSMC). Notably, [...] Read more.
This paper proposes a new adaptive controller for three-wheeled mobile robots (3WMRs) called the ABHSMC controller. This ABHSMC controller is developed through a cooperative approach, combining a backstepping controller and a Radial Basis Function (RBF) neural network-based Hierarchical Sliding Mode Controller (HSMC). Notably, the RBF neural network exhibits the remarkable capability to estimate both the uncertainty components of the model and systematically adapt its parameters, leading to enhanced output trajectory responses. A novel navigational model, constructed by the connection to the adaptive BHSMC controller, Timed Elastic Band (TEB) Local Planner, and A-star (A*) Global Planner, is called ABHSMC navigation stack, and it is applied to effectively solve the tracking issue and obstacle avoidance for the 3-Wheeled Mobile Robot (3WMR). The simulation results implemented in the Matlab/Simulink platform demonstrate that the 3WMRs can precisely follow the desired trajectory, even in the presence of disturbances and changes in model parameters. Furthermore, the controller’s reliability is endorsed on our constructed self-driving car model. The achieved experimental results indicate that the proposed navigational structure can effectively control the actual vehicle model to track the desired trajectory with a small enough error and avoid a sudden obstacle simultaneously. Full article
(This article belongs to the Special Issue Advanced Mechatronics and Robotics Technologies in Industry 4.0 Era: Intelligence and Automation)
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19 pages, 5966 KiB  
Article
Lane Line Detection and Object Scene Segmentation Using Otsu Thresholding and the Fast Hough Transform for Intelligent Vehicles in Complex Road Conditions
by Muhammad Awais Javeed, Muhammad Arslan Ghaffar, Muhammad Awais Ashraf, Nimra Zubair, Ahmed Sayed M. Metwally, Elsayed M. Tag-Eldin, Patrizia Bocchetta, Muhammad Sufyan Javed and Xingfang Jiang
Electronics 2023, 12(5), 1079; https://doi.org/10.3390/electronics12051079 - 21 Feb 2023
Cited by 20 | Viewed by 5283
Abstract
An Otsu-threshold- and Canny-edge-detection-based fast Hough transform (FHT) approach to lane detection was proposed to improve the accuracy of lane detection for autonomous vehicle driving. During the last two decades, autonomous vehicles have become very popular, and it is constructive to avoid traffic [...] Read more.
An Otsu-threshold- and Canny-edge-detection-based fast Hough transform (FHT) approach to lane detection was proposed to improve the accuracy of lane detection for autonomous vehicle driving. During the last two decades, autonomous vehicles have become very popular, and it is constructive to avoid traffic accidents due to human mistakes. The new generation needs automatic vehicle intelligence. One of the essential functions of a cutting-edge automobile system is lane detection. This study recommended the idea of lane detection through improved (extended) Canny edge detection using a fast Hough transform. The Gaussian blur filter was used to smooth out the image and reduce noise, which could help to improve the edge detection accuracy. An edge detection operator known as the Sobel operator calculated the gradient of the image intensity to identify edges in an image using a convolutional kernel. These techniques were applied in the initial lane detection module to enhance the characteristics of the road lanes, making it easier to detect them in the image. The Hough transform was then used to identify the routes based on the mathematical relationship between the lanes and the vehicle. It did this by converting the image into a polar coordinate system and looking for lines within a specific range of contrasting points. This allowed the algorithm to distinguish between the lanes and other features in the image. After this, the Hough transform was used for lane detection, making it possible to distinguish between left and right lane marking detection extraction; the region of interest (ROI) must be extracted for traditional approaches to work effectively and easily. The proposed methodology was tested on several image sequences. The least-squares fitting in this region was then used to track the lane. The proposed system demonstrated high lane detection in experiments, demonstrating that the identification method performed well regarding reasoning speed and identification accuracy, which considered both accuracy and real-time processing and could satisfy the requirements of lane recognition for lightweight automatic driving systems. Full article
(This article belongs to the Special Issue Advanced Mechatronics and Robotics Technologies in Industry 4.0 Era: Intelligence and Automation)
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