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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Hui Zhang Chi Zhang Feng Yi Qinghao Du Pingping Lv Shupei Li Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences College of Materials Science and Opto-Electronic Technology University of Chinese Academy of Sciences 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 Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo China

ISBN: (数字)9798350360868

ISBN: (纸本)9798350360875

Driving joints are the core components of collaborative robots, but existing joints have low torque density and poor dynamic response performance. This article proposes a novel integrated joint composed of a hollow AFPM motor and an embedded planetary gear reducer, which enhance the output performance, i.e., high torque density and efficiency, high dynamic response. Aiming at achieving the superior performance, a matching-oriented design method is proposed for the integrated joint. This design method reveals the impacts mechanisms between the structural parameters (including AFPM motor and planetary reducer) and the output performance. The multi-objective optimization is conducted to obtain the optimal design parameters by using NSGA-II. The optimized design is compared with the initial one, which indicates that the weight of the optimized joint is reduced by 49g, the joint torque density is increased by 36.8%, and the dynamic response performance is improved by 31.5%.

关键词： Industrial electronics Torque Gears Design methodology Collaborative robots Motors Structural engineering

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A Mixed Method for Estimating Parameters of Three-Parameter Weibull Distribution

A Mixed Method for Estimating Parameters of Three-Parameter ...

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International Symposium on System and Software Reliability (ISSSR)

作者： Renyan Jiang Fengping Li Wei Xue Xiao Li Kunpeng Zhang Institute of Laser and Optoelectronic Intelligent Manufacturing Wenzhou University Wenzhou China China International Science & Technology Cooperation Base for Laser Processing Robotics Wenzhou University Wenzhou China Provincial Key Laboratory of Laser Processing Robotics College of Mechanical and Electrical Engineering Wenzhou University Wenzhou China

The three-parameter Weibull distribution has been widely used for modeling component lifetime and material strength. The parameter estimates obtained from the maximum likelihood estimation method (MLE) and maximum product of spacing (MPS) method can be biased or inexistent. Though many other approaches have been developed, none is always efficient. Therefore, it is still a challenging issue to develop more efficient estimation methods. This paper aims to address this issue by proposing a mixed approach. The proposed approach is based on a modified Weibull transformations and involves three main steps. The first step determines two reference points from the empirical distribution function. In the second step, the location parameter is estimated based on the ratios of the difference between the reference point and data points on the modified Weibull probability paper (MWPP) plot. The third step estimates the shape and scale parameters based on the linear relation of the MWPP plot. The accuracy and robustness of the proposed approach is illustrated by a numerical experiment and its usefulness is illustrated by a real-world example. The results shows that the proposed approach provides more accurate and robust estimates than the MLE and MPS.

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Wheel-Terrain Contact Geometry Estimation using the Aside-Wheel RGB-D Sensor over Soft Terrain

Wheel-Terrain Contact Geometry Estimation using the Aside-Wh...

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robotics, Automation and Artificial Intelligence (RAAI), IEEE International Conference on

作者： Pengxuan Zhana Chen Yao Longteng Hu Feng Xue Peichen Wang Zheng Zhu Zhenzhong Jia Department of Mechanical and Energy Engineering Southern University of Science and Technology Shenzhen China Department of Mechanical and Energy Engineering Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Southern University of Science and Technology (SUSTech) Shenzhen China SUSTech Guang-dong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Shenzhen China Harbin of Institute of Technology (HIT) Harbin China College of Engineering University of Michigan Ann Arbor Michigan USA School of Electronics Harbin Institute of Technology Harbin China

ISBN: (数字)9798350307566

ISBN: (纸本)9798350307573

Wheeled mobile robots (WMRs) have exhibited notable competitiveness in various applications, including geo-graphic and planetary exploration. Nonetheless, when traversing on soft terrains, their wheels might encounter sinkage and slippage, leading to mission failure. Consequently, real-time estimation of contact parameters such as sinkage and slip ratio holds significant importance for rover operations. In this paper, we introduce a novel algorithm that employs only one aside-wheel RGB-D camera for the simultaneous detection of wheel sinkage and slip ratio on deformable terrains in real-time. We estimate sinkage through the depth module by processing point cloud data, and we estimate slip ratio through the color module with a sequence of image processing steps. Utilizing only one single camera reduces errors arising from sensor signal alignment and enhances detection accuracy. With a single-wheel testbed, we simulate wheel motion under different slip ratios and carry out numerous experiments to validate the effectiveness of the proposed algorithm. Our algorithm attains a detection frequency of up to 20Hz and exhibits outstanding precision. The relative sinkage error remains below 4%, while the relative slippage error is confined within 0.8%.

关键词： Accuracy Image color analysis Robot vision systems Wheels Estimation Cameras Real-time systems

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A Deep Ship Recognition Model Based on Multi-category Fusion of General Datasets

A Deep Ship Recognition Model Based on Multi-category Fusion...

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IEEE International Conference on Civil Aviation Safety and Information technology (ICCASIT)

作者： Hang Su Guoping Qiu School of Telecommunications Engineering and Intelligentization Dongguan University of Technology Dongguan Guangdong China College of Electronic and Information Engineering Shenzhen University Shenzhen Guangdong China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen Guangdong China Shenzhen Institute of Artificial Intelligence and Robotics Shenzhen University Branch Shenzhen Guangdong China Shenzhen Key Laboratory of Digital Creative Technology Shenzhen University Shenzhen Guangdong China

ISBN: (数字)9798350389418

ISBN: (纸本)9798350389425

To meet the demand for accurate and rapid recognition of ship targets in existing ship image recognition methods, this study applies the DCNN model, known for its powerful feature extraction capabilities, to the task of ship image recognition. To enhance the model's performance, a novel dataset fusion training method is utilized. The DCNN model requires large-scale labeled training datasets; however, there is currently a lack of diverse and large-scale labeled ship image datasets in the public domain. Additionally, manually collecting and labeling large-scale ship recognition data is labor-intensive. Therefore, this paper proposes a new approach by creating a ship target recognition dataset that fuses existing general datasets for training the ship recognition model. Furthermore, a model training method that integrates multi-category image recognition is proposed. In this method, a focal loss-based optimization training approach is specifically employed to handle imbalanced data classification, thereby improving the recognition performance of ship images within multi-category datasets. Performance tests on the trained model show that the mean Average Precision (mAP) of the model trained with multi-category fusion is relatively improved by $9.45 \%$ compared to the $\mathbf{3 8 . 1 9 \%}$ mAP of the model trained without focal loss on a single category. Experimental results demonstrate that the proposed multi-category image training method can effectively enhance the performance of ship image recognition tasks in training using existing general image recognition datasets.

关键词： Training Image recognition Target recognition Training data Trademarks Data models Safety Marine vehicles Standards Optimization

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Position control of humanoid thorax based on fuzzy PID controller

Position control of humanoid thorax based on fuzzy PID contr...

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Siyuan Fang Zaojun Fang Chengbin Peng Chi Zhang Guiling Yang Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo Institute of Materials & Technology Engineering Chinese & Academy of Sciences Ningbo China Ningbo Institute of Materials & Technology Engineering Chinese & Academy of Sciences Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology Ningbo China College of Science & Technology Ningbo University Ningbo China

Respiration is the most notable vital sign of humans so that it is usually employed to diagnose disease in medicine. Recently, a solution is proposed to decrease the cost of healthcare using flexible stretchable sensors to monitor respiration. However, the sensor is difficult to test millions of cycles on a real human body, which is essential to ensure accuracy and repeatability in practical applications. In this paper, a new humanoid thoracic cavity that mimics rib expansion/constriction is exhibited. Then, a position control based on Fuzzy PID method is presented. Focused on the scenario, the performance between the common PID method and the Fuzzy PID method is investigated. Firstly, the position control based on the Fuzzy PID method is constructed by utilizing position output error and its rate. Then, three gain parameters of PID are adjusted online according to the established fuzzy rules. Finally, the above two methods are compared by simulation and experiments. In a simulation, the fuzzy PID control algorithm improves the response speed, shortens the adjustment time, and meets the requirements of the system for high response and high accuracy. Besides, an experiment is carried out on a humanoid chest prototype. the results show that the control period of the presented method is shortened by about 50% v.s. the traditional PID under the step signal input. Tracking marked points using a laser tracker, the maximum tracking error is 0.0523 mm, the minimum error is 0.0450 mm, and the average error is 0.0473mm. Therefore, the presented method is validated to improve the performance of the humanoid chest.

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Research on hybrid flow-shop scheduling based on improved genetic algorithm

Research on hybrid flow-shop scheduling based on improved ge...

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Haopeng Chen Zaojun Fang Caiming Zhong Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo Institute of Materials & Technology Engineering Chinese & Academy of Sciences Ningbo China Ningbo Institute of Materials & Technology Engineering Chinese & Academy of Sciences Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology Ningbo China College of Science & Technology Ningbo University Ningbo China

Aiming at the scheduling problem in the production process of hybrid flow-shop, a hybrid flow-shop scheduling model with the objective of minimizing the makespan is established, and a neighborhood search adaptive genetic algorithm (NSAGA) based on heuristic decoding strategy is proposed. An encoding strategy based on the first stage processing sequence and a decoding strategy based on a heuristic rule are used to guide the algorithm to find the optimal solution quickly. In order to speed up the convergence of the algorithm, the elitism strategy is introduced into the genetic operation, and adaptive crossover and mutation strategy based on sigmoid function is designed. In order to enhance the local search capability of the algorithm, a neighborhood search strategy is designed. Finally, the effectiveness and superiority of NSAGA for solving the hybrid flow-shop scheduling problem (HFSP) with different scales are verified by simulation experiments.

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Rehabilitation Training of Spinal Cord Injured Individuals by a Self-Balancing Exoskeleton Robot

Rehabilitation Training of Spinal Cord Injured Individuals b...

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IEEE International Conference on Real-time Computing and robotics (RCAR)

作者： Wentao Li Jinke Li Yong He Dingkui Tian Feng Li Jianquan Sun Xinyu Wu Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China SIAT CUHK Joint Laboratory of Robotics and Intelligent Systems Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen China China Construction Third Bureau First Engineering MEP Co. Ltd. Shenzhen China

Physical therapy plays an important role in the rehabilitation for spinal cord injured individuals, and exoskeleton can assist with this process. In this paper, a self-balancing exoskeleton robot is introduced, which can provide assist not only in gait training, but also in balance training. In addition, it allows individuals walk with it without other assistive devices. This exoskeleton is designed to have a high degree of compatibility with humans. The structure and control system of the exoskeleton is introduced and the forward and inverse kinematic are solved, which enables the robot to perform various rehabilitation training actions as required. Experiments with a male subject have shown that this exoskeleton achieves the predicted results in gait training and balance training.

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A DDOS Attack Traffic Classification Model for Industrial Internet Based on CNN-LSTM

A DDOS Attack Traffic Classification Model for Industrial In...

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2022 Chinese Automation Congress, CAC 2022

作者： Wei, Weixuan Liu, Xianda Sheng, Chuan Feng, Ansong Shenyang University of Chemical Technology College of Information Engineering Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9781665465335

With the continuous development and application of industrial Internet, the weakness of the security defense capability of industrial Internet has become increasingly prominent. This paper presents an end-to-end classification model combining Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network for DDoS attacks over industrial Internet. By self-learning the spatial and temporal characteristics of the original attack traffic, the model can avoid subjective errors that may occur due to manual selection of features. This method is a feasible means, with an accuracy rate of 99%. © 2022 IEEE.

关键词： Long short-term memory

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Analysis of Big Data Complex Network Structure Based on Fuzzy Clustering Algorithm

Analysis of Big Data Complex Network Structure Based on Fuzz...

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2021 International Conference on Networking, Communications and Information technology, NetCIT 2021

作者： Shanshan, Fu Zhiqiang, Ruan Minjiang University Center of Modem Education Technology and Information Management Fujian350108 China Minjiang University College of Computer and Control Engineering Fujian350108 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fujian350108 China Digital Fujian IoT Laboratory of Intelligent Production Fujian350108 China

ISBN: (纸本)9781665400701

In the era of artificial intelligence with big data as the background, the statistical analysis method based on complex network has become a research hotspot and actively promoted the development of big data visualization. Complex network is a new discipline developed by cross research with other disciplines. Its network topology has obvious clustering characteristics, that is, complex network cluster structure. Together with small world and scale-free, it is called the three most important and basic attributes of complex network topology. The fundamental purpose of complex network clustering algorithm is to detect the actual network cluster structure in complex network, which has very important practical value for mining the cluster structure in network and understanding its characteristics, and is conducive to understanding the functions of network, such as predicting the development of complex network, analyzing the behavior of social network, discovering hot topics on network, etc. This paper combines the methods and ideas of complex network analysis, carries out the method design and research of organization analysis, and tries to solve the relationship and characteristics of inter departmental cooperation within the organization. © 2021 IEEE.

关键词： Complex networks

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Deep Domain Adaptation Regression for Force Calibration of Optical Tactile Sensors

arXiv

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arXiv 2024年

作者： Chen, Zhuo Ou, Ni Jiang, Jiaqi Luo, Shan Robot Perception Lab Centre for Robotics Research Department of Engineering King’s College London LondonWC2R 2LS United Kingdom State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing100081 China

Optical tactile sensors provide robots with rich force information for robot grasping in unstructured environments. The fast and accurate calibration of three-dimensional contact forces holds significance for new sensors and existing tactile sensors which may have incurred damage or aging. However, the conventional neural-network-based force calibration method necessitates a large volume of force-labeled tactile images to minimize force prediction errors, with the need for accurate Force/Torque measurement tools as well as a time-consuming data collection process. To address this challenge, we propose a novel deep domain-adaptation force calibration method, designed to transfer the force prediction ability from a calibrated optical tactile sensor to uncalibrated ones with various combinations of domain gaps, including marker presence, illumination condition, and elastomer modulus. Experimental results show the effectiveness of the proposed unsupervised force calibration method, with lowest force prediction errors of 0.102N (3.4% in full force range) for normal force, and 0.095N (6.3%) and 0.062N (4.1%) for shear forces along the x-axis and y-axis, respectively. This study presents a promising, general force calibration methodology for optical tactile sensors. Copyright © 2024, The Authors. All rights reserved.

关键词： Forecasting

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