Special Issue "Recent Advances in Industrial Robots"

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

Deadline for manuscript submissions: 31 October 2022 | Viewed by 632

Special Issue Editors

Dr. Pei-Chi Huang
E-Mail Website
Guest Editor
Department of Computer Science, University of Nebraska at Omaha, Omaha, NE 68182, USA
Interests: cyber-physical systems; real-time systems; robotics; machine learning; wireless communication/networking systems
Dr. Wei Fang
E-Mail Website
Guest Editor
School of Automation, Beijing University of Posts and Telecommunications, Beijing 100876, China
Interests: robotic vision; robotic sensing and navigation; industrial augmented/mixed reality

Special Issue Information

Dear Colleagues,

Today’s industrial robots work in a wide range of industries for different applications, from metal forging and plastic forming to the production of semiconductors and automobiles, and still continue to rapidly evolve. This growth is driven largely by manufacturers that plan to use robots to tackle the looming shortage of skilled labor. Therefore, the recent advances in the development of industrial robotics demonstrated unique features, such as advanced actuators, advanced senses and perception, improved batteries, lightweight body materials, and control algorithms, as well as data processing capabilities along with artificial intelligence (AI), machine learning, and the Internet of Things technologies. These features give rise to a broad range of spectacular developments and lead to the development of a new version of industrial robots. To perform tasks in unstructured environments, robots can efficiently coordinate distinct actions in intelligent ways to carry out unseen and long horizon tasks.

In this Special Issue, we are particularly interested in the emerging trends in the development of industrial robots that will have a significant impact on the manufacturing, construction, and industrial sectors.  This Special Issue aims to continue to highlight recent advances in industrial robots. Topics include but are not limited to:

  • Intelligent industrial robotics;
  • AI-enabled robotics;
  • Cloud robots;
  • Collaborative robots;
  • Industrial Internet of Things (IIoT);
  • Robotics and automation;;
  • Computer architecture for robotics
  • Computer vision for automation and manufacturing;
  • Advanced sensing and navigation;
  • Motion and path planning;
  • Planning, scheduling and coordination;
  • Human–robot collaborations;
  • AR /MR in robotics;
  • Digital twin for industrial robotics.

Dr. Pei-Chi Huang
Dr. Wei Fang
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 2000 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.


  • intelligent industrial robotics
  • AI-enabled robotics
  • cloud robots
  • collaborative robots
  • Industrial Internet of Things (IIoT)
  • robotics and automation
  • computer architecture for robotics computer vision for automation and manufacturing
  • advanced sensing and navigation
  • motion and path planning
  • planning, scheduling and coordination
  • human–robot collaborations
  • AR /MR in robotics
  • digital twin for industrial robotics

Published Papers (1 paper)

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A Fusion Model for Saliency Detection Based on Semantic Soft Segmentation
Electronics 2022, 11(17), 2712; https://doi.org/10.3390/electronics11172712 - 29 Aug 2022
Viewed by 253
With the rapid development of neural networks in recent years, saliency detection based on deep learning has made great breakthroughs. Most deep saliency detection algorithms are based on convolutional neural networks, which still have great room for improvement in the edge accuracy of [...] Read more.
With the rapid development of neural networks in recent years, saliency detection based on deep learning has made great breakthroughs. Most deep saliency detection algorithms are based on convolutional neural networks, which still have great room for improvement in the edge accuracy of salient objects recognition, which may lead to fuzzy results in practical applications such as image matting. In order to improve the accuracy of detection, a saliency detection model based on semantic soft segmentation is proposed in this paper. Firstly, the semantic segmentation module combines spectral extinction and residual network model to obtain low-level color features and high-level semantic features, which can clearly segment all kinds of objects in the image. Then, the saliency detection module locates the position and contour of the main body of the object, and the edge accurate results are obtained after the processing of the two modules. Finally, compared with the other 11 algorithms on the DUTS-TEST data set, the weighted F-measure value of the proposed algorithm ranked first, which was 5.8% higher than the original saliency detection algorithm, and the accuracy was significantly improved. Full article
(This article belongs to the Special Issue Recent Advances in Industrial Robots)
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