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Machines
Special Issues
Applications and Challenges of Intelligent Robotic Technology in Industrial Automation
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Applications and Challenges of Intelligent Robotic Technology in Industrial Automation

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Automation and Control Systems".

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 2099

Special Issue Editors

Dr. Pengwen Xiong

E-Mail Website
Guest Editor
School of Advanced Manufacturing, Nanchang University, Nanchang 330031, China
Interests: human–robot interaction; robotic sensing and controlling
Prof. Dr. Zhijun Li

grade E-Mail Website
Guest Editor
Institute of Advanced Technology/School of Information Science and Technology, University of Science and Technology of China, Hefei 230022, China
Interests: wearable robotics and autonomous systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the continuous improvement of industrial automation, more and more companies are using intelligent robotic technology to improve their production efficiency and quality. Intelligent robotic technology can not only complete simple repetitive tasks, but can also handle more complex tasks with higher accuracy and reliability. However, in practical applications, intelligent robotic technology faces many challenges such as environmental perception, path planning, and cooperative control. Therefore, studying the applications and challenges of intelligent robotic technology in industrial automation is of great significance.

This Special Issue invites scholars, engineers, and researchers to share their latest research on the application of intelligent robotic technology in industrial automation.

Research topics that are of interest for this Special Issue include, but are not limited to:

  • The design and development of intelligent robotic systems;
  • Machine vision, sensors, and control technology;
  • Robot path planning and motion control;
  • Robot collaboration and coordination control;
  • Applications of intelligent manufacturing and IoT technology.

Dr. Pengwen Xiong
Prof. Dr. Zhijun Li
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. Machines is an international peer-reviewed open access monthly 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

  • artificial intelligence
  • automation
  • control systems
  • cyber–physical systems
  • human–robot interaction
  • machine learning
  • mechatronics
  • perception and sensing
  • internet of things (IoT)

Published Papers (1 paper)

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Research

22 pages, 4432 KiB  
Article
Enhancing 6-DoF Object Pose Estimation through Multiple Modality Fusion: A Hybrid CNN Architecture with Cross-Layer and Cross-Modal Integration
by Zihang Wang, Xueying Sun, Hao Wei, Qing Ma and Qiang Zhang
Machines 2023, 11(9), 891; https://doi.org/10.3390/machines11090891 - 6 Sep 2023
Cited by 1 | Viewed by 1811
Abstract
Recently, applying the utilization of RGB-D data for robot perception tasks has garnered significant attention in domains like robotics and autonomous driving. However, a prominent challenge in this field lies in the substantial impact of feature robustness on both segmentation and pose estimation [...] Read more.
Recently, applying the utilization of RGB-D data for robot perception tasks has garnered significant attention in domains like robotics and autonomous driving. However, a prominent challenge in this field lies in the substantial impact of feature robustness on both segmentation and pose estimation tasks. To tackle this challenge, we proposed a pioneering two-stage hybrid Convolutional Neural Network (CNN) architecture, which connects segmentation and pose estimation in tandem. Specifically, we developed Cross-Modal (CM) and Cross-Layer (CL) modules to exploit the complementary information from RGB and depth modalities, as well as the hierarchical features from diverse layers of the network. The CM and CL integration strategy significantly enhanced the segmentation accuracy by effectively capturing spatial and contextual information. Furthermore, we introduced the Convolutional Block Attention Module (CBAM), which dynamically recalibrated the feature maps, enabling the network to focus on informative regions and channels, thereby enhancing the overall performance of the pose estimation task. We conducted extensive experiments on benchmark datasets to evaluate the proposed method and achieved exceptional target pose estimation results, with an average accuracy of 94.5% using the ADD-S AUC metric and 97.6% of ADD-S smaller than 2 cm. These results demonstrate the superior performance of our proposed method. Full article
(This article belongs to the Special Issue Applications and Challenges of Intelligent Robotic Technology in Industrial Automation)
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