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14th International Workshop of Advanced Manufacturing and Automation, IWAMA 2024

作者： Yang, Shuai Wang, Chen Zhou, Lin College of Mechanical Engineering Hubei University of Automotive Technology Shiyan442002 China China Academy of Engineering Science and Technology Shiyan Industrial Technology Research Institute Shiyan442000 China Shanghai Key Laboratory of Intelligent Manufacturing and Robotics Shanghai University Shanghai200072 China

ISBN: (纸本)9789819626243

The quality of the entire mechanical system is directly impacted by the quality of metal gears, an essential transmission component. As such, detecting defects on the end face of metal gears accurately holds significant practical importance with the advancement of industrial automation. Traditional methods for defect detection often involve manual visual inspection or basic image processing techniques, leading to inefficiencies and inaccuracies. In this study, we introduce a novel model for detecting defects on metal gear endfaces utilizing deep learning technology. Through comparison with current mainstream algorithms, our model demonstrated superior performance in terms of accuracy and efficiency, highlighting the vast potential of deep learning in enhancing metal gear endface defect detection. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Gear transmissions

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Shifting Technique for Cogging Torque Suppression in Yokeless and Segmented Armature Machines 3

Shifting Technique for Cogging Torque Suppression in Yokeles...

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3rd International Conference on Power Systems and Electrical technology, PSET 2024

作者： Yi, Feng Zhang, Chi Qiu, Shuheng Chen, Jinhua Liu, Wei Shu, Xindong Ningbo Institute of Materials & Technology and Engineering Chinese Academy of Sciences College of Materials Science and Opto-Electronic Technology Ningbo China Ningbo Institute of Materials & Technology and Engineering Chinese Academy of Sciences Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology Ningbo China

ISBN: (纸本)9798350353204

This paper proposed the shifting technique for cogging torque suppression in yokeless and segmented armature (YASA) machines. This shifting technique includes the rotor, stator, and hybrid shifting. The bilateral cogging torques produced from the lower and upper air gaps can be compensated by regulating the rotor and stator shifting values. Subsequently, an analytical model is formulated based on the superstition principle, which yields the optimal shifting value for cogging torque minimization. The impact of the rotor, stator, and hybrid shifting techniques on cogging torque suppression is analyzed, respectively. In addition, comprehensive comparisons of electromagnetic performance are conducted, in terms of the air-gap flux density, back-EMF, cogging torque, and electromagnetic torque. The comparative results demonstrate a reduction in cogging torque by 15.8%, 17.1%, and 11.1% with the rotor, stator, and hybrid shifting, respectively, without compromising output performance. © 2024 IEEE.

关键词： Stators

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Local Path Planning of Unmanned Surface Vehicle Based on Improved DWA Algorithm

Local Path Planning of Unmanned Surface Vehicle Based on Imp...

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International Conference on Guidance, Navigation and Control, ICGNC 2024

作者： Ji, Chenjia Zhang, Lanyong College of Intelligent Systems Science and Engineering Harbin Engineering University Harbin150001 China State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin150001 China

ISBN: (纸本)9789819622351

An improved dynamic window method is proposed to solve the obstacle avoidance problem of intelligent ships in offshore waters that cannot effectively avoid when encountering unknown dynamic obstacles. Firstly, an autonomous collision avoidance decision-making model was constructed to judge whether the unmanned boat had a collision hazard, and the encounter situation was reclassified. To ensure that the USV can safely pass through the moving obstacle vessel, a collision risk assessment function considering the velocity of dynamic obstacles is introduced. Moreover, a collision avoidance rule function is incorporated into the evaluation function of the collision avoidance algorithm to ensure that the USV adheres to collision avoidance regulations. Ultimately, the simulation results confirm that the enhanced dynamic window method can safely avoid collisions while adhering to collision avoidance rules. This paper proposes an effective solution for collision avoidance behavior of intelligent vessels in near shore waters when facing unknown dynamic obstacles, providing insightful recommendations for subsequent research in maritime sectors. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Unmanned surface vehicles

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A Novel Trajectory Tracking Method for Quad-Steering Wheel Chassis Based on MPC 17th

A Novel Trajectory Tracking Method for Quad-Steering Wheel ...

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17th International Conference on intelligent robotics and Applications, ICIRA 2024

作者： Han, Yu Fan, Liting Zhang, Yang Yan, Teng Jiang, Boyu Cheng, Zhongjiang School of Mechanical Engineering Shenyang Jianzhu University Liaoning Province Shenyang110168 China Sino-German College of Intelligent Manufacturing Shenzhen Technology University Shenzhen518118 China Mechanical Industry Key Laboratory of Intelligent Robotics Technology for 3C Shenzhen Technology University Shenzhen518118 China

ISBN: (纸本)9789819607761

In transportation, autonomous driving, and road line marking operations, one of the crucial technologies is the trajectory tracking of steering wheels. Addressing the issue of trajectory tracking in quad-steering wheel mobile robots, this paper presents a method for tracking the trajectory of a quad-steering wheel chassis, which is based on a kinematic model and employs a Model Predictive Control (MPC) algorithm to study the trajectory tracking controller for the mobile robot. In this approach, the state error equation is first employed as the prediction model, which is then linearized and discretized to obtain a linear time-varying model. Terminal constraints are incorporated into the cost function, and appropriate weighting coefficients and terminal sets are designed to ensure that the reference trajectory is tracked within a finite time horizon. Subsequently, trajectory tracking simulations under varying prediction horizons and different paths are conducted for comparative analysis, leading to the determination of an optimal prediction horizon. Finally, the trajectory tracking experimental effectiveness is validated by having the mobile robot draw lines, with results demonstrating that the algorithm can effectively accomplish the trajectory tracking task for quad-steering wheel robots. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Mobile robots

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Scoliosis detection method based on fringe projection profilometry and deep learning

Scoliosis detection method based on fringe projection profil...

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2024 International Conference on Optical and Photonic engineering, icOPEN 2024

作者： Wang, Meng He, Dong Xu, Shaolei Tang, Qijian Peng, Xiang Liu, Xiaoli State Key Laboratory of Radio Frequency Heterogeneous Integration College of Physics and Optoelectronic Engineering Shenzhen University Guangdong China Key Laboratory of Intelligent Optical Measurement and Detection of Shenzhen Shenzhen Institute of Information Technology Shenzhen Guangdong China School of Sino-German Robotics Shenzhen Institute of Information Technology Shenzhen Guangdong China

ISBN: (数字)9781510688117

ISBN: (纸本)9781510688100

In this paper, a fringe projection profilometry (FPP) system for three-dimensional (3D) imaging of the human back is proposed to assess scoliosis, which has developed into one of the top four health concerns affecting adolescents. The FPP system includes a binocular stereo vision system and an infrared projector, with a Field Programmable Gate Array (FPGA) based synchronization control system enabling a 240Hz acquisition frame rate. This high frame rate mitigates the impact of involuntary body movement on 3D image quality. Various features can be extracted from the generated 3D model of the back for quantitative postural assessment. Specifically, a deep learning approach is employed to analyze the extracted trunk rotation angle curve for scoliosis detection. Experimental results demonstrate a correlation between this method and X-ray detection, offering a promising exploratory approach to reduce reliance on X-ray imaging in scoliosis monitoring. © 2025 SPIE.

关键词： Stereo image processing

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Event-triggered impulsive synchronization of heterogeneous neural networks

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Science China(Information Sciences) 2024年第2期67卷 331-332页

作者： Zhengxin WANG Chongfang JIN Wangli HE Min XIAO Guo-Ping JIANG Jinde CAO College of Science Nanjing University of Posts and Telecommunications Jiangsu Engineering Lab for IOT Intelligent Robots (IOTRobot) Nanjing University of Posts and Telecommunications Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of EducationEast China University of Science and Technology School of Automation Nanjing University of Posts and Telecommunications School of Mathematics Southeast University Yonsei Frontier Lab Yonsei University

Synchronization is one of the most important dynamics in neural networks. Several scholars have studied the quasi-synchronization of heterogeneous neural networks [1],wherein external controllers play a significant role. Impulsive control, a kind of energy-saving control, is activated instantaneously only at specific discrete moments.

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Detection of Positional Accuracy Degradation of Industrial Robots Based on Kinematic Analysis

Detection of Positional Accuracy Degradation of Industrial R...

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2023 IEEE International Conference on Electrical, Automation and Computer engineering, ICEACE 2023

作者： Yang, Kongping Qi, Yuming Xie, Bing Deng, Sanpeng Mo, Kaidi Institute of Robotics and Intelligent Equipment Tianjin Key Laboratory of Intelligent Robot Technology and Application Tianjin University of Technology and Education Tianjin China College of Mechanical Engineering Tianjin University of Science and Technology Tianjin China School of Technology Beijing Forestry University Beijing China

ISBN: (纸本)9798350309614

Aiming at the problem of positional accuracy degradation of industrial robots in long-term operation, a positional accuracy degradation detection method of industrial robots based on kinematic analysis is proposed. Firstly, the accuracy of the joint angles collected from the robot control cabinet and external high-precision sensors is compared and analyzed based on the MD- H method;secondly, the positional degradation detection model of the industrial robot is established based on the principle of differential kinematics;and finally, an experimental platform is built based on the KUKA KR 3 R540 industrial robot and experimental validation is carried out. The experimental results show that the root-mean-square error between the detected position error and the actual position error is 0.049 mm, and the method can effectively detect the position accuracy degradation of the robot. © 2023 IEEE.

关键词： Accuracy degradation Industrial robot MD-H method Position error

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Force Sensor-Based Linear Actuator Stiffness Rendering Control 16th

Force Sensor-Based Linear Actuator Stiffness Rendering Contr...

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16th International Conference on intelligent robotics and Applications, ICIRA 2023

作者： Chen, Han Dai, Junjie Chen, Chin-Yin An, Yongfeng Huang, Bing College of Mechanical Engineering Zhejiang University of Technology Hangzhou310023 China Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology Ningbo Institute of Materials Technology and Engineering CAS Ningbo315201 China Ningbo Zhongda Leader Intelligent Transmission Co. Ltd. Ningbo China

ISBN: (纸本)9789819964918

Different stiffness on the actuator can broaden its range of applications and improve its motion control performance. Precise stiffness rendering is advantageous for improving collision safety. However, there are mechanical impedance and friction, which will affect system stiffness. This study applies a force sensor to measure them directly and compensate them by the motor. To make the actuator exhibit ideal stiffness, a PD controller is introduced for stiffness rendering. Simulation and experimental results verify that this method can accurately render stiffness less than the mechanical stiffness for parallel-structured linear actuators. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Stiffness

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Minimizing Data Collection Latency for Coexisting Time-Critical Wireless Networks with Tree Topologies

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IEEE Transactions on Network and Service Management 2025年第3期22卷 2354-2369页

作者： Zhang, Jialin Liang, Wei Yang, Bo Shi, Huaguang Liang, Ying-Chang Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang Institute of Automation Shenyang110016 China Chinese Academy of Sciences Key Laboratory of Networked Control System Shenyang Institute of Automation Shenyang110016 China Northwest A and F University College of Information Engineering Yangling712100 China Henan University School of Artificial Intelligence Zhengzhou450046 China University of Electronic Science and Technology of China Center for Intelligent Networking and Communications Chengdu611731 China

Time-Critical Wireless Network (TCWN) is a promising communication technology that can satisfy the low latency, high reliability, and deterministic requirements of mission-critical applications. Multiple TCWNs required by various applications inevitably coexist with each other. Most existing works aim to achieve acceptable latency or consider the simplest topology (i.e., line topology). As latency requirements become more stringent, exploring the minimum data collection latency becomes an interesting problem. In this paper, the coexisting system consists of multiple tree-topology-based TCWNs. We first establish a conversion framework to convert an arbitrary tree topology into multiple analogous line topologies to reduce the analysis complexity. We then propose a Time-Critical wireless network Scheduling (TCS) algorithm to minimize the data collection latency of coexisting TCWNs. The TCS algorithm consists of two phases. In the internetwork scheduling phase, we strictly derive a general expression to characterize the practical network requirements. In the intranetwork scheduling phase, we design two levels of priority assignment algorithms to accurately characterize the critical states and resource requirements of different nodes. We conduct extensive simulations to verify the effectiveness of the TCS algorithm. The evaluation results show that the TCS algorithm can achieve minimum data collection latency in more than 99.956% cases, and the maximum difference compared to the optimal value is one time slot. © 2004-2012 IEEE.

关键词： Scheduling algorithms

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Knowledge-guided temperature correction method for soluble solids content detection of watermelon based on Vis/NIR spectroscopy

Artificial Intelligence in Agriculture

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Artificial Intelligence in Agriculture 2025年第1期15卷 88-97页

作者： Zhizhong Sun Jie Yang Yang Yao Dong Hu Yibin Ying Junxian Guo Lijuan Xie College of Biosystems Engineering and Food Science Zhejiang UniversityHangzhou 310058PR China College of Chemistry and Materials Engineering Zhejiang A&F UniversityHangzhou 311300PR China Key Laboratory of Intelligent Equipment and Robotics for Agriculture of Zhejiang Province Hangzhou 310058PR China The National Key Laboratory of Agricultural Equipment Technology Beijing 100083PR China Department of Bioproducts and Biosystems Engineering University of MinnesotaSaint PaulMN 55108USA Zhejiang University School of Medicine Division of Scientific Research(ZJU-QILU joint research institute)Hangzhou 310058PR China College of Optical Mechanical and Electrical EngineeringZhejiang A&F UniversityHangzhou 311300PR China College of Mechanical and Electrical Engineering Xinjiang Agricultural UniversityUrumqi 830052PR China

Visible/near-infrared(Vis/NIR)spectroscopy technology has been extensively utilized for the determination of soluble solids content(SSC)in ***,the spectral distortion resulting from temperature variations in the sample leads to a decrease in detection *** mitigate the influence of temperature fluctuations on the accuracy of SSC detection in fruits,using watermelon as an example,this study presents a knowledge-guided temperature correction method utilizing one-dimensional convolutional neural networks(1D-CNN).This method consists of two stages:the first stage involves utilizing 1D-CNN models and gradient-weighted class activation mapping(Grad-CAM)method to acquire gradient-weighted features correlating with *** second stage involves mapping these features and integrating them with the original Vis/NIR spectrum,and then train and test the partial least squares(PLS)*** knowledge-guided method can identify wavelength bands with high temperature correlation in the Vis/NIR spectra,offering valuable guidance for spectral data *** performance of the PLS model constructed using the 15℃ spectrum guided by this method is superior to that of the global model,and can reduce the root mean square error of the prediction set(RMSEP)to 0.324°Brix,which is 32.5%lower than the RMSEP of the global model(0.480°Brix).The method proposed in this study has superior temperature correction effects than slope and bias correction,piecewise direct standardization,and external parameter orthogonalization correction *** results indicate that the knowledge-guided temperature correction method based on deep learning can significantly enhance the detection accuracy of SSC in watermelon,providing valuable reference for the development of PLS calibration methods.

关键词： Visible/near-infrared spectroscopy Temperature correction Watermelon Knowledge-guided Convolutional neural network

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