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Machines
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Industry 4.0: Intelligent Robots in Smart Manufacturing
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Industry 4.0: Intelligent Robots in Smart Manufacturing

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Robotics, Mechatronics and Intelligent Machines".

Deadline for manuscript submissions: 31 May 2025 | Viewed by 2653

Special Issue Editors

Dr. Yong Tao

E-Mail Website
Guest Editor
School of Mechanical Engineering and Automation, Beihang University, Beijing 100191, China
Interests: integration of digital design and manufacturing of complex products; intelligent control method of “perception-decision” for man–machine cooperative robots; intelligent robot integrated application; embedded electromechanical control and aircraft-flexible assembly manufacturing technology
Prof. Dr. Hongxing Wei

E-Mail Website
Guest Editor
School of Mechanical Engineering and Automation, Beihang University, Beijing 100191, China
Interests: robot modular technology; robot operating systems; embedded electromechanical control technology
Dr. Haiyuan Li

E-Mail Website
Guest Editor
School of Automation, Beijing University of Posts and Telecommunications, Beijing 100876, China
Interests: surgical robot; modular robotics; robot hand and robot manipulation
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Guangzhe Zhao

E-Mail Website
Guest Editor
College of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing 102616, China
Interests: image processing; pattern recognition; deep learning; machine vision; manipulator

Special Issue Information

Dear Colleagues,

The fourth industrial revolution, Industry 4.0, has led to significant advancements in the integration of advanced technologies in manufacturing processes. Intelligent robots are increasingly being integrated into various aspects of manufacturing, from production automation to quality control and human–robot collaboration. The convergence of artificial intelligence, robotics, big data analytics, and the Internet of Things (IoT) has paved the way for the development of more complex, adaptive, and cognitive robotics for smart manufacturing. This Special Issue aims to provide an in-depth exploration of the latest developments and potential future directions associated with intelligent robots in smart manufacturing and facilitate knowledge exchange and interdisciplinary collaborations among researchers and practitioners. Authors are encouraged to submit original research articles, review papers, etc.

Contributions to this Special Issue can encompass a diverse range of topics, including but not limited to:

  • Intelligent robot design and development for smart manufacturing processes;
  • Cognitive and adaptive robotics approaches in industrial settings;
  • The integration of robotic systems with IoT infrastructure for real-time monitoring and control;
  • Machine learning and artificial intelligence algorithms for enhancing robot capabilities and decision-making;
  • Collaborative robotics for human–robot interaction and cooperation;
  • The integrated application of robot technology;
  • The control and application of collaborative robots;
  • The cross-application of machine vision and industrial robot fusion;
  • Flexible robotic grasping;
  • The core technologies and applications of mobile collaborative robots.

Dr. Yong Tao
Prof. Dr. Hongxing Wei
Dr. Haiyuan Li
Prof. Dr. Guangzhe Zhao
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

  • intelligent robots
  • smart manufacturing
  • collaborative robots
  • industrial robots

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Published Papers (3 papers)

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Research

14 pages, 1193 KiB  
Article
Hyper CLS-Data-Based Robotic Interface and Its Application to Intelligent Peg-in-Hole Task Robot Incorporating a CNN Model for Defect Detection
by Fusaomi Nagata, Ryoma Abe, Shingo Sakata, Keigo Watanabe and Maki K. Habib
Machines 2024, 12(11), 757; https://doi.org/10.3390/machines12110757 - 26 Oct 2024
Viewed by 490
Abstract
Various types of numerical control (NC) machine tools can be standardly operated and controlled based on NC data that can be easily generated using widespread CAD/CAM systems. On the other hand, the operation environments of industrial robots still depend on conventional teaching and [...] Read more.
Various types of numerical control (NC) machine tools can be standardly operated and controlled based on NC data that can be easily generated using widespread CAD/CAM systems. On the other hand, the operation environments of industrial robots still depend on conventional teaching and playback systems provided by the makers, so it seems that they have not been standardized and unified like NC machine tools yet. Additionally, robotic functional extensions, e.g., the easy implementation of a machine learning model, such as a convolutional neural network (CNN), a visual feedback controller, cooperative control for multiple robots, and so on, has not been sufficiently realized yet. In this paper, a hyper cutter location source (HCLS)-data-based robotic interface is proposed to cope with the issues. Due to the HCLS-data-based robot interface, the robotic control sequence can be visually and unifiedly described as NC codes. In addition, a VGG19-based CNN model for defect detection, whose classification accuracy is over 99% and average time for forward calculation is 70 ms, can be systematically incorporated into a robotic control application that handles multiple robots. The effectiveness and validity of the proposed system are demonstrated through a cooperative pick and place task using three small-sized industrial robot MG400s and a peg-in-hole task while checking undesirable defects in workpieces with a CNN model without using any programmable logic controller (PLC). The specifications of the PC used for the experiments are CPU: Intel(R) Core(TM) i9-10850K CPU 3.60 GHz, GPU: NVIDIA GeForce RTX 3090, Main memory: 64 GB. Full article
(This article belongs to the Special Issue Industry 4.0: Intelligent Robots in Smart Manufacturing)
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17 pages, 11776 KiB  
Article
An Asymmetric Independently Steerable Wheel for Climbing Robots and Its Motion Control Method
by Meifeng Lv, Xiaoshun Liu, Lei Xue, Ke Tan, Junhui Huang and Zeyu Gong
Machines 2024, 12(8), 536; https://doi.org/10.3390/machines12080536 - 6 Aug 2024
Viewed by 659
Abstract
Climbing robots, with their expansive workspace and flexible deployment modes, have the potential to revolutionize the manufacturing processes of large and complex components. Given that the surfaces to be machined typically exhibit variable curvature, good surface adaptability, load capacity, and motion accuracy are [...] Read more.
Climbing robots, with their expansive workspace and flexible deployment modes, have the potential to revolutionize the manufacturing processes of large and complex components. Given that the surfaces to be machined typically exhibit variable curvature, good surface adaptability, load capacity, and motion accuracy are essential prerequisites for climbing robots in manufacturing tasks. This paper addresses the manufacturing requirements of climbing robots by proposing an asymmetric independently steerable wheel (AISW) for climbing robots, along with the motion control method. Firstly, for the adaptability issue of the locomotion mechanism on curved surfaces under heavy load, an asymmetric independently steerable wheel motion module is proposed, which improves the steering difficulty of the traditional independently steerable wheel (ISW) based on the principle of steering assisted by wheels. Secondly, a kinematic model of the AISW chassis is established and, on this basis, a trajectory tracking method based on feedforward and proportional–integral feedback is proposed. Comparative experimental results on large, curved surface components show that the asymmetric independently steerable wheel has lower steering resistance and higher motion accuracy, significantly enhancing the reachability of climbing robots and facilitating their application in the manufacturing of large and complex components. Full article
(This article belongs to the Special Issue Industry 4.0: Intelligent Robots in Smart Manufacturing)
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19 pages, 2956 KiB  
Article
Industrial Robot Trajectory Optimization Based on Improved Sparrow Search Algorithm
by Fei Ma, Weiwei Sun, Zhouxiang Jiang, Shuangfu Suo, Xiao Wang and Yue Liu
Machines 2024, 12(7), 490; https://doi.org/10.3390/machines12070490 - 20 Jul 2024
Cited by 1 | Viewed by 908
Abstract
This paper proposes an enhanced multi-strategy sparrow search algorithm to optimize the trajectory of a six-axis industrial robot, addressing issues of low efficiency and high vibration impact on joints during operation. Initially, the improved D-H parametric method is employed to establish both forward [...] Read more.
This paper proposes an enhanced multi-strategy sparrow search algorithm to optimize the trajectory of a six-axis industrial robot, addressing issues of low efficiency and high vibration impact on joints during operation. Initially, the improved D-H parametric method is employed to establish both forward and inverse kinematic models of the robot. Subsequently, a 3-5-3 mixed polynomial interpolation trajectory planning approach is applied to the robot. Building upon the conventional sparrow algorithm, a two-dimensional Logistic chaotic system initializes the population. Additionally, a Levy flight strategy and nonlinear adaptive weighting are introduced to refine the discoverer position update operator, while an inverse learning strategy enhances the vigilante position update operator. These modifications boost both the local and global search capabilities of the algorithm. The improved sparrow algorithm, based on 3-5-3 hybrid polynomial trajectory planning, is then used for the time-optimal trajectory planning of the robot. This is compared with traditional sparrow search algorithm and particle swarm algorithm optimization results. The findings indicate that the proposed enhanced sparrow search algorithm outperforms both the standard sparrow algorithm and the particle swarm algorithm in terms of convergence speed and accuracy for robot trajectory optimization. This can lead to the increased work efficiency and performance of the robot. Full article
(This article belongs to the Special Issue Industry 4.0: Intelligent Robots in Smart Manufacturing)
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