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Motion Control for Robots and Automation

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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 14809

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Special Issue Editors

Dr. Yi Fang

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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

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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

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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 (12 papers)

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Research

17 pages, 1587 KiB

Open AccessArticle

Dynamic Obstacle Avoidance for Robotic Arms Using Deep Reinforcement Learning with Adaptive Reward Mechanisms

by Sen Yan, Yanping Zhu, Wenlong Chen, Jianqiang Zhang, Chenyang Zhu and Qi Chen

Appl. Sci. 2025, 15(8), 4496; https://doi.org/10.3390/app15084496 - 18 Apr 2025

Viewed by 268

Abstract

To address the challenges of robotic arm path-planning in dynamic environments, this study proposes a reinforcement learning-based dynamic obstacle avoidance method. The study concerns a robot with six rotational degrees of freedom when moving outside of singular configurations, enabling more flexible and precise motion-planning. First, a dynamic exploration guidance mechanism is designed to enhance learning efficiency and reduce ineffective exploration. Second, an adaptive reward function is developed to enable real-time path-planning while avoiding obstacles. A simulation environment is constructed using CoppeliaSim software, and the experiment is performed with two cylindrical obstacles that move randomly within the workspace. The experimental results demonstrate that the improved method significantly outperforms traditional algorithms in terms of convergence speed, reward value, and success rate. Full article

(This article belongs to the Special Issue Motion Control for Robots and Automation)

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15 pages, 8591 KiB

Open AccessArticle

Research on a Point Cloud Registration Method Based on Dynamic Neighborhood Features

by Xinrui Liu, Rutao Wang and Zongsheng Wang

Appl. Sci. 2025, 15(7), 4036; https://doi.org/10.3390/app15074036 - 7 Apr 2025

Viewed by 306

Abstract

This paper introduces a method that can enhance the accuracy and efficiency of point cloud data registration. This method selects the centroid of the point cloud as the feature point and uses the projected distance of this feature point within the dynamic neighborhood to other points as the feature information. Through this feature information, it accomplishes the registration of two sets of point cloud data. This method increases the density and integrity of point cloud data, improves the accuracy and robustness of point cloud registration, and the selection of feature points reduces the computational load thereby enhancing processing efficiency. The introduction of the dynamic neighborhood enables the method to flexibly handle point cloud data of different scales and densities. Experimental results show that the proposed method has good performance in terms of accuracy and efficiency for achieving point cloud data registration and dealing with data under various complex conditions and can effectively improve the effect of point cloud data registration and fusion. Full article

(This article belongs to the Special Issue Motion Control for Robots and Automation)

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20 pages, 6467 KiB

Open AccessArticle

A Lightweight TA-YOLOv8 Method for the Spot Weld Surface Anomaly Detection of Body in White

by Weijie Liu, Miao Jia, Shuo Zhang, Siyu Zhu, Jin Qi and Jie Hu

Appl. Sci. 2025, 15(6), 2931; https://doi.org/10.3390/app15062931 - 8 Mar 2025

Viewed by 713

Abstract

The deep learning architecture YOLO (You Only Look Once) has demonstrated its superior visual detection performance in various computer vision tasks and has been widely applied in the field of automatic surface defect detection. In this paper, we propose a lightweight YOLOv8-based method for the quality inspection of car body welding spots. We developed a TA-YOLOv8 network structure which has an improved Task-Aligned (TA) head detection, designed to handle a small sample size, imbalanced positive and negative samples, and high-noise characteristics of Body-in-White welding spot data. By learning with fewer parameters, the model achieves more efficient and accurate classification. Additionally, our algorithm framework can perform anomaly segmentation and classification on our open-world raw datasets obtained from actual production environments. The experimental results show that the lightweight module improves the processing speed by an average of 2.8%, with increases in detection the mAP@50-95 and recall rate of 1.35% and 0.1226, respectively. Full article

(This article belongs to the Special Issue Motion Control for Robots and Automation)

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25 pages, 13369 KiB

Open AccessArticle

Three-Dimensional Path-Following with Articulated 6DoF Robot and ToF Sensors

by Tymon F. Wawrzyniak, Ignacy D. Orłowski and Marek A. Galewski

Appl. Sci. 2025, 15(6), 2917; https://doi.org/10.3390/app15062917 - 7 Mar 2025

Viewed by 790

Abstract

This paper presents an algorithm for 3D path-following using an articulated 6-Degree-of-Freedom (DoF) robot as well as experimental verification of the proposed approach. This research extends the classic line-following concept, typically applied in 2D spaces, into a 3D space. This is achieved by equipping a standard industrial robot with a path detection tool featuring six low-cost Time-of-Flight (ToF) sensors. The primary objective is to enable the robot to follow a physically existing path defined in 3D space. The developed algorithm allows for step-by-step detection of the path’s orientation and calculation of consecutive positions and orientations of the detection tool that are necessary for the robot arm to follow the path. Experimental tests conducted using a Nachi MZ04D robot demonstrated the reliability and effectiveness of the proposed solution. Full article

(This article belongs to the Special Issue Motion Control for Robots and Automation)

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20 pages, 12929 KiB

Open AccessArticle

Employing Fuzzy Adaptive and Event-Triggered Approaches to Achieve Formation Control of Nonholonomic Mobile Robots Under Complete State Constraints

by Kai Wang, Jinnan Lu and Haodong Zhou

Appl. Sci. 2025, 15(5), 2827; https://doi.org/10.3390/app15052827 - 5 Mar 2025

Viewed by 528

Abstract

This article delves into the problem of fuzzy adaptive event-triggered (ET) formation control for nonholonomic mobile robots (NMRs) subject to full-state constraints. Fuzzy logic systems (FLSs) are employed to identify the unknown nonlinear functions within the system. To guarantee that all system states remain within their constraint boundaries, barrier Lyapunov functions (BLFs) are meticulously constructed. Subsequently, within the framework of the backstepping control design algorithm, we propose a novel fuzzy adaptive ET formation controller. Our ET mechanism can achieve an overall resource-saving rate of 88.17% for the four robots. Rigorous theoretical analysis demonstrates that the designed strategy not only ensures the stability of the controlled NMRs but also enables the formation tracking errors to converge to a small neighborhood around zero. Notably, the BLFs-based control approach presented herein endows the system with the capacity to avoid collisions to a certain degree, enhancing the overall safety and reliability of the robot formation. Finally, a simulation example is provided. The results vividly illustrate the effectiveness and practicality of the proposed theory, validating its potential for real-world applications in the field of nonholonomic mobile robot formation control. Full article

(This article belongs to the Special Issue Motion Control for Robots and Automation)

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23 pages, 37944 KiB

Open AccessArticle

Residual Vibration Reduction in Flexible Systems Based on Trapezoidal Velocity Profiles

by Xining Cui, Yi Fang and Chaochen Gu

Appl. Sci. 2025, 15(4), 1791; https://doi.org/10.3390/app15041791 - 10 Feb 2025

Viewed by 731

Abstract

Industrial parts are increasingly being designed to be more lightweight in modern manufacturing for energy saving and material cost reduction. However, the high-speed motion of flexible systems tends to excite severe residual vibrations that result in positioning accuracy degradation and loss of productivity. This study proposes a closed-form trajectory optimization method for vibration suppression based on trapezoidal velocity profiles, which are most widely used in industrial robots and machines. First, the formulation and minimum time solution under actuator limits of the motion profile are defined. Then, the relationship between the trajectory parameters and the vibration response is investigated. It is shown that residual vibration can be eliminated by properly tuning the acceleration/deceleration switching times according to the natural frequency. Based on the derived vibration suppression conditions, a tuning procedure for time parameters compliant with actuator limits is established to generate fast and precise movement. A main advantage of the proposed method is easy implementation for general machines without requiring extra computational resources or modification to the control system. The effectiveness and practicality of the proposed approach are verified through experiments conducted on a robot. The experimental results show that the optimized trajectory achieves superior residual vibration reduction performance. Full article

(This article belongs to the Special Issue Motion Control for Robots and Automation)

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20 pages, 8119 KiB

Open AccessArticle

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

Cited by 1 | Viewed by 1700

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. 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

Open AccessArticle

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 1040

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 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

Open AccessArticle

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 1688

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 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

Open AccessArticle

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

Cited by 5 | Viewed by 2766

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 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

Open AccessArticle

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 1442

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. 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

Open AccessArticle

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 1242

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 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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