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Motion Control for Robots and Automation
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Motion Control for Robots and Automation

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Robotics and Automation".

Deadline for manuscript submissions: 30 June 2025 | Viewed by 5417

Special Issue Editors

Dr. Yi Fang

E-Mail Website
Guest Editor
School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: industrial robots; motion planning and control; multi-objective intelligent optimization
Special Issues, Collections and Topics in MDPI journals
Dr. Yuxin Sun

E-Mail Website
Guest Editor
Associate Professor, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: electromechanical system dynamics modeling and control; high-precision and high-speed CNC equipment and control; signal processing and deep learning algorithms; robotic intelligent manufacturing
Dr. Dongfang Liu

E-Mail Website
Guest Editor
Department of Computer Engineering, Kate Gleason College of Engineering, Rochester Institute of Technology, Rochester, NY 14623, USA
Interests: artificial intelligence and robotics; vision-language intelligence

Special Issue Information

Dear Colleagues,

Motion control is a critical and challenging field of automation that encompasses the systems involved in the moving parts of machines or robots in a controlled manner. The primary purpose of motion control is to achieve precise positioning, speed, and torque control in a wide array of industrial and commercial applications, including robotics, CNC machinery, factory automation, aerospace, and more. Numerous relevant research studies have been developed in the fields of mechanical engineering, electrical engineering, control theory, and computer science. Indeed, a proper control method can be utilized to enhance the execution of tasks in various domains, such as vibration suppression, energy consumption, cycle time, and tracking accuracy. The design of an optimal controller is essential for systems with complex dynamics, especially in the presence of perturbation.

In this Special Issue, we invite researchers to contribute original works and qualified reviews related to motion control for automatic machines and robots, such as medical robots, industrial robots, service robots, mobile robots, bionic robots, micro/nanorobots, CNC machines, multirobot cooperation, cranes, and so on. The scope of this Special Issue includes, but is not limited to, the following: kinematic and dynamic modeling, artificial intelligence, trajectory planning, advanced control, sensors and actuators. Both theoretical and experimental studies are welcome.

Dr. Yi Fang
Dr. Yuxin Sun
Dr. Dongfang Liu
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. Applied Sciences 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

  • motion control
  • motion planning
  • trajectory planning
  • kinematic and dynamic modeling
  • servomotor
  • AI in motion control
  • vibration suppression
  • precision manufacturing
  • motion profile design
  • feedforward control
  • feedback control
  • intelligent robots and machines
  • advanced control

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Published Papers (6 papers)

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Research

21 pages, 8119 KiB  
Article
Reducing Safety Risks in Construction Tower Crane Operations: A Dynamic Path Planning Model
by Binqing Cai, Zhukai Ye, Shiwei Chen and Xun Liang
Appl. Sci. 2024, 14(22), 10599; https://doi.org/10.3390/app142210599 - 17 Nov 2024
Viewed by 425
Abstract
Tower cranes are the most used equipment in construction projects, and the path planning of tower crane operations directly affects the safety performance of construction projects. Traditional tower crane operations rely on only the driving experience and manual path planning of crane operators. [...] Read more.
Tower cranes are the most used equipment in construction projects, and the path planning of tower crane operations directly affects the safety performance of construction projects. Traditional tower crane operations rely on only the driving experience and manual path planning of crane operators. Poor judgement and bad path planning may increase safety risks and even cause severe construction safety accidents. To reduce safety risks in construction tower crane operations, this research proposes a dynamic path planning model for tower crane operations based on computer vision technology and dynamic path planning algorithms. The proposed model consists of three modules: first, a path information collection module preprocessing the video data to capture relevant operational path information; second, a path safety risk evaluation module employing You Only Look Once version 8 (YOLOv8) instance segmentation to identify potential risk factors along the operational path, e.g., potential drop zones and the positions of nearby workers; and finally, a path planning module utilizing an improved Dynamic Window Approach for tower cranes (TC-DWA) to avoid risky areas and optimize the operational path for enhanced safety. A prototype based on the theoretical model was constructed and tested on actual construction projects. Through experimental scenarios, it was found that each tower crane operation poses safety risks to 3–4 workers on average, and the proposed prototype can significantly reduce the safety risks of dropped loads from tower crane operations affecting ground workers and important equipment. A comparison between the proposed model and other regular algorithms was also conducted, and the results show that compared with traditional RRT and APF algorithms, the proposed model reduces the average maximum collision times by 50. This research provides a theoretical model and a preliminary prototype to provide dynamic path planning and reduce safety risks in tower crane operations. Future research will be conducted from the aspects of multiple device monitoring and system optimization to increase the analysis speed and accuracy, as well as on human–computer interactions between tower crane operators and the path planning guidance model. Full article
(This article belongs to the Special Issue Motion Control for Robots and Automation)
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20 pages, 2552 KiB  
Article
Study on Image Classification Algorithm Based on Multi-Scale Feature Fusion and Domain Adaptation
by Yu Guo, Ziyi Cheng, Yuanlong Zhang, Gaoxuan Wang and Jundong Zhang
Appl. Sci. 2024, 14(22), 10531; https://doi.org/10.3390/app142210531 - 15 Nov 2024
Viewed by 319
Abstract
This paper introduces the MMTADAN, an innovative algorithm designed to enhance cross-domain image classification. By integrating multi-scale feature extraction with Taylor series-based detail enhancement and adversarial domain adaptation, the MMTADAN effectively aligns features between the source and target domains. The proposed approach addresses [...] Read more.
This paper introduces the MMTADAN, an innovative algorithm designed to enhance cross-domain image classification. By integrating multi-scale feature extraction with Taylor series-based detail enhancement and adversarial domain adaptation, the MMTADAN effectively aligns features between the source and target domains. The proposed approach addresses the critical challenge of generalizing classification models across diverse datasets, demonstrating significant improvements in performance. The findings suggest that retaining essential image details through multi-scale extraction and Taylor series enhancement can lead to better classification outcomes, making the MMTADAN a valuable contribution to the field of image classification. Full article
(This article belongs to the Special Issue Motion Control for Robots and Automation)
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26 pages, 2358 KiB  
Article
Imbalanced Data Parameter Optimization of Convolutional Neural Networks Based on Analysis of Variance
by Ruiao Zou and Nan Wang
Appl. Sci. 2024, 14(19), 9071; https://doi.org/10.3390/app14199071 - 8 Oct 2024
Viewed by 843
Abstract
Classifying imbalanced data is important due to the significant practical value of accurately categorizing minority class samples, garnering considerable interest in many scientific domains. This study primarily uses analysis of variance (ANOVA) to investigate the main and interaction effects of different parameters on [...] Read more.
Classifying imbalanced data is important due to the significant practical value of accurately categorizing minority class samples, garnering considerable interest in many scientific domains. This study primarily uses analysis of variance (ANOVA) to investigate the main and interaction effects of different parameters on imbalanced data, aiming to optimize convolutional neural network (CNN) parameters to improve minority class sample recognition. The CIFAR-10 and Fashion-MNIST datasets are used to extract samples with imbalance ratios of 25:1, 15:1, and 1:1. To thoroughly assess model performance on imbalanced data, we employ various evaluation metrics, such as accuracy, recall, F1 score, P-mean, and G-mean. In highly imbalanced datasets, optimizing the learning rate significantly affects all performance metrics. The interaction between the learning rate and kernel size significantly impacts minority class samples in moderately imbalanced datasets. Through parameter optimization, the accuracy of the CNN model on the 25:1 highly imbalanced CIFAR-10 and Fashion-MNIST datasets improves by 14.20% and 5.19% compared to the default model and by 8.21% and 3.87% compared to the undersampling model, respectively, while also enhancing other evaluation metrics for minority classes. Full article
(This article belongs to the Special Issue Motion Control for Robots and Automation)
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16 pages, 2885 KiB  
Article
Trajectory Planning of Robotic Arm Based on Particle Swarm Optimization Algorithm
by Nengkai Wu, Dongyao Jia, Ziqi Li and Zihao He
Appl. Sci. 2024, 14(18), 8234; https://doi.org/10.3390/app14188234 - 12 Sep 2024
Viewed by 1184
Abstract
Achieving vibration-free smooth motion of industrial robotic arms in a short period is an important research topic. Existing path planning algorithms often sacrifice smoothness in pursuit of efficient motion. A robotic trajectory planning particle swarm optimization algorithm (RTPPSO) is introduced for optimizing joint [...] Read more.
Achieving vibration-free smooth motion of industrial robotic arms in a short period is an important research topic. Existing path planning algorithms often sacrifice smoothness in pursuit of efficient motion. A robotic trajectory planning particle swarm optimization algorithm (RTPPSO) is introduced for optimizing joint angles or paths of mechanical arm movements. The RTPPSO algorithm is enhanced through the introduction of adaptive weight strategies and random perturbation terms. Subsequently, the RTPPSO algorithm is utilized to plan selected parameters of an S-shaped velocity profile, iterating to obtain the optimal solution. Experimental results demonstrate that this velocity planning algorithm significantly improves the acceleration of the robotic arm, surpassing traditional trial-and-error velocity planning methods. Full article
(This article belongs to the Special Issue Motion Control for Robots and Automation)
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14 pages, 3847 KiB  
Article
E-GTN: Advanced Terrain Sensing Framework for Enhancing Intelligent Decision Making of Excavators
by Qianyou Zhao, Le Gao, Duidi Wu, Xinyao Meng, Jin Qi and Jie Hu
Appl. Sci. 2024, 14(16), 6974; https://doi.org/10.3390/app14166974 - 8 Aug 2024
Viewed by 1040
Abstract
The shift towards autonomous excavators in construction and mining is a significant leap towards enhancing operational efficiency and ensuring worker safety. However, it presents challenges, such as the need for sophisticated decision making and environmental perception due to complex terrains and diverse conditions. [...] Read more.
The shift towards autonomous excavators in construction and mining is a significant leap towards enhancing operational efficiency and ensuring worker safety. However, it presents challenges, such as the need for sophisticated decision making and environmental perception due to complex terrains and diverse conditions. Our study introduces the E-GTN framework, a novel approach tailored for autonomous excavation that leverages advanced multisensor fusion and a custom-designed convolutional neural network to address these challenges. Results demonstrate that GridNet effectively processes grid data, enabling the reinforcement learning algorithm to make informed decisions, thereby ensuring efficient and intelligent autonomous excavator performance. The study concludes that the E-GTN framework offers a robust solution for the challenges in unmanned excavator operations, providing a valuable platform for future advancements in the field. Full article
(This article belongs to the Special Issue Motion Control for Robots and Automation)
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15 pages, 2626 KiB  
Article
FES Control of a Finger MP Joint with a Proxy-Based Super-Twisting Algorithm
by Hua Chen, Xiaogang Xiong, Koki Honda, Shouta Okunami and Motoji Yamamoto
Appl. Sci. 2024, 14(11), 4905; https://doi.org/10.3390/app14114905 - 5 Jun 2024
Viewed by 879
Abstract
To improve motion accuracy through functional electrical stimulation (FES) of forearm muscles, feedback control laws are applied to the index finger’s metacarpophalangeal (MP) joint. This paper introduces a proxy-based super-twisting algorithm (PSTA) for precise servo control of MP joints via FES. The PSTA [...] Read more.
To improve motion accuracy through functional electrical stimulation (FES) of forearm muscles, feedback control laws are applied to the index finger’s metacarpophalangeal (MP) joint. This paper introduces a proxy-based super-twisting algorithm (PSTA) for precise servo control of MP joints via FES. The PSTA combines first-order sliding mode control with a second-order super-twisting algorithm, effectively preventing windup during FES saturation and ensuring robust, accurate control. An implicit Euler method minimizes numerical chattering in the digital implementation. Experiments with Arduino and volunteers confirm the algorithm’s effectiveness. Full article
(This article belongs to the Special Issue Motion Control for Robots and Automation)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Research on Positioning Error Compensation of Rock Drilling Manipulator Based on ISBOA-BP Neural Network
Author: Ju
Highlights: 1. The ISBOA, proposed through improvements to the SBOA, improves the algorithm's convergence speed. 2. ISBOA optimizes the neural network, effectively addressing the BP neural network's tendency to fall into local optima. 3. A neural network mapping between joint values and end-positioning error enables effective error compensation and significantly improves the positioning accuracy for the rock drilling robotic arm.

Title: High-Performance Silicon Retina with 7nm FinFET for Advanced Motion Control in Robotics Automation: A Hybrid Model and Image Analysis Approach
Author: ISLAM
Highlights: Intelligent Robotics & AI Vision Systems: High-performance silicon retina design enhances object and self-motion recognition in robotics; YOLOv5 & MiDaS Integration: Combined object detection and depth estimation enable real-time awareness for autonomous vehicles; Looming Detection: Advanced control for dynamic hazard recognition; 7nm FinFET Technology: Ensures efficient, rapid response for critical motion & threat detection in AI-driven applications.

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