2021 IEEE International Conference on Real-time Computing and Robotics (RCAR)最新文献_第5页

Development of a Virtual Training System for Master-Slave Hip Replacement Surgery 主从髋关节置换术虚拟训练系统的开发

2021 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2021-07-15 DOI: 10.1109/RCAR52367.2021.9517408

Riwei Zhang, Quanquan Liu, Shuting Cai, Chunbao Wang, Xin Zhang, L. Duan, Yongtian Lu, Bo Zhang, Zhengzhi Wu, Jing Guo

{"title":"Development of a Virtual Training System for Master-Slave Hip Replacement Surgery","authors":"Riwei Zhang, Quanquan Liu, Shuting Cai, Chunbao Wang, Xin Zhang, L. Duan, Yongtian Lu, Bo Zhang, Zhengzhi Wu, Jing Guo","doi":"10.1109/RCAR52367.2021.9517408","DOIUrl":"https://doi.org/10.1109/RCAR52367.2021.9517408","url":null,"abstract":"Robotical hip replacement surgeries can benefit patient by precise operation and less complication. However, the robotic manipulation under master-slave mode requires operator to steer a handle for remote operation with high manipulative skill. This paper developed a virtual training system for the positioning of the acetabular cup in total hip replacements. The simulation system provides users with a master-slave mode of human-machine interaction training to assist them in accelerating their adaptation to the orthopedic surgery robot. The system can offer a security and realistic learning environment for addressing the inability to determine acetabular cup placement due to osteophytes. The user completes the mapping with the virtual surgical tool in the controller by controlling the haptic device during the training process. When the virtual tool reaches the positioning point at the correct angle, the message of distance and color shift of the point on the virtual panel indicates a successful operation. Five users experienced the system and the time taken to complete the trials showed that it helped to improve proficiency. Furthermore, the virtual simulation system can provide vivid and intuitive perception, improve the understanding of the remote manipulation.","PeriodicalId":232892,"journal":{"name":"2021 IEEE International Conference on Real-time Computing and Robotics (RCAR)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127047407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Navigation and Control Study of UAV for Cross-domain Bridge Collaboration Detection 面向跨域桥梁协同检测的无人机导航控制研究

2021 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2021-07-15 DOI: 10.1109/RCAR52367.2021.9517661

Yuchen Yan, Yizhai Zhang, Panfeng Huang

引用次数: 0

The Optimization of Adiabatic Pulses with Constant Amplitude Spin-lock for Magnetic Resonance $mathrm{T}_{1rho}$ Imaging 磁共振成像中恒幅自旋锁绝热脉冲的优化

2021 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2021-07-15 DOI: 10.1109/RCAR52367.2021.9517352

Yuxin Yang, Zhongmin Chen, Xi Xu, Yuanyuan Liu, Yanjie Zhu, Dong Liang

{"title":"The Optimization of Adiabatic Pulses with Constant Amplitude Spin-lock for Magnetic Resonance $mathrm{T}_{1rho}$ Imaging","authors":"Yuxin Yang, Zhongmin Chen, Xi Xu, Yuanyuan Liu, Yanjie Zhu, Dong Liang","doi":"10.1109/RCAR52367.2021.9517352","DOIUrl":"https://doi.org/10.1109/RCAR52367.2021.9517352","url":null,"abstract":"Quantitative magnetic resonance $mathrm{T}_{1rho}$ mapping is an important tool for a number of clinical applications, which can be used to obtain useful molecular information from diseased tissue non-invasively and without contrast agents. However, the stability of $mathrm{T}_{1rho}$ mapping is vulnerable to the influence of the magnetic field inhomogeneity, causing image artifacts and non-negligible quantization errors. Therefore, approaches using adiabatic pulses are proposed to alleviate the above deficiencies. This study proposes a method to optimize the HS and HSExp adiabatic pulses with constant amplitude spin-lock. The optimized pulse parameters were obtained through simulation which calculated the stability degree of the longitudinal magnetization Mz under a range of off-resonance values, and verified by phantom and in vivo experiments. The results of the experiment showed that the optimized adiabatic spin-lock pulses can achieve acceptable $mathrm{T}_{1rho}$-weighted imaging and $mathrm{T}_{1rho}$ mapping qualities.","PeriodicalId":232892,"journal":{"name":"2021 IEEE International Conference on Real-time Computing and Robotics (RCAR)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125690469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Prediction Method of Lower Limb Muscle Fatigue Based on Combining Random Forest and Gated Recurrent Unit Neural Network 基于随机森林与门控循环单元神经网络相结合的下肢肌肉疲劳预测方法

2021 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2021-07-15 DOI: 10.1109/RCAR52367.2021.9517349

Xin Shi, Shuyuan Xu, Pengjie Qin, Gaojie He, Zhengli Leng

{"title":"Prediction Method of Lower Limb Muscle Fatigue Based on Combining Random Forest and Gated Recurrent Unit Neural Network","authors":"Xin Shi, Shuyuan Xu, Pengjie Qin, Gaojie He, Zhengli Leng","doi":"10.1109/RCAR52367.2021.9517349","DOIUrl":"https://doi.org/10.1109/RCAR52367.2021.9517349","url":null,"abstract":"In this paper, the traditional fatigue state classification method is abandoned, and a neural network model is established to predict the variation of muscle fatigue by using the extracted muscle fatigue characteristics. This is of great significance for the subsequent use of muscle fatigue characteristics to compensate for the changes of sEMG signals caused by muscle fatigue in continuous motion, so as to achieve the compliance control of the exoskeleton. In the muscle fatigue experiment, we selected 7 representative subjects and collected the data of each subject from non-fatigue state to fatigue state during the dynamic contraction of the lower limb, fifteen sets of data were collected for each subject. In this paper, a muscle fatigue prediction method combining random forest (RF) and gated recursive unit (GRU) neural network is proposed, in the experiment, 75 sets of data from the first 5 subjects were used for model training, and 30 sets of data from the last 2 subjects were used for model test, and each set of data was predicted separately. In order to verify the generalization of the proposed model, 20 experiments are carried out. The experimental results show that compared with the traditional recursive neural network (RNN), long and short term memory (LSTM), GRU and multi-layer feedforward and back propagation neural network (BPNN), the proposed model has the advantages of higher prediction accuracy and better generalization.","PeriodicalId":232892,"journal":{"name":"2021 IEEE International Conference on Real-time Computing and Robotics (RCAR)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130664227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

High-Precision Pose Estimation Method of the 3C Parts by Combining 2D and 3D Vision for Robotic Grasping in Assembly Applications 结合二维和三维视觉的3C零件装配机器人抓取高精度位姿估计方法

2021 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2021-07-15 DOI: 10.1109/RCAR52367.2021.9517329

Nan Zhang, Yixin Xie, Xiansheng Yang, Haopeng Hu, Y. Lou

{"title":"High-Precision Pose Estimation Method of the 3C Parts by Combining 2D and 3D Vision for Robotic Grasping in Assembly Applications","authors":"Nan Zhang, Yixin Xie, Xiansheng Yang, Haopeng Hu, Y. Lou","doi":"10.1109/RCAR52367.2021.9517329","DOIUrl":"https://doi.org/10.1109/RCAR52367.2021.9517329","url":null,"abstract":"Using robots to replace manual production has been seen as a feasible solution to reduce production costs and increase productivity, especially in the 3C (Computer, Communication, and Consumer Electronics) products assembly lines, which rely heavily on labor. Due to the characteristic of small size and accurate fitting precision, small uncertainties in the assembly process will lead to the failure of the assembly, especially in the process of grasping, when the gripper needs to move to a certain position. So, to realize the 3C products flexible assembly of the robot, machine vision is required to provide the high-precision pose information of the object. So far, point cloud based 6D (6-dimensional) pose estimation algorithms have attracted the attention of many researchers because point cloud can provide three-dimensional information directly. However, the disorder of the point cloud and the background information with miscellaneous noise makes it impossible to directly estimate the pose of the target object with high precision. To deal with this problem, we propose a 2D-3D combined high-precision pose estimation method. The whole method is divided into two stages. In the first stage, the mask of the object in the 2D image is identified through the Mask R-CNN which is trained through fine-turning. In the second stage, we use a structured light camera to generate the point cloud and map the mask to it to extract useful point cloud, then the high-precision pose estimating algorithm composed by PCA-ICP is used to get the global pose of the part. Finally, the pose is converted to the robot coordinate frame by the result of hand-eye calibration. The proposed method is verified by the grasping and assembly experiments.","PeriodicalId":232892,"journal":{"name":"2021 IEEE International Conference on Real-time Computing and Robotics (RCAR)","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132509260","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Nano Robotic Manipulator Positioning Accuracy Measurement By Secondary Electron Image 基于二次电子图像的纳米机器人机械手定位精度测量

2021 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2021-07-15 DOI: 10.1109/RCAR52367.2021.9517439

Litao Yang, Lue Zhang, Zhi Qu, Zengsheng Li, Zhan Yang

{"title":"Nano Robotic Manipulator Positioning Accuracy Measurement By Secondary Electron Image","authors":"Litao Yang, Lue Zhang, Zhi Qu, Zengsheng Li, Zhan Yang","doi":"10.1109/RCAR52367.2021.9517439","DOIUrl":"https://doi.org/10.1109/RCAR52367.2021.9517439","url":null,"abstract":"In order to perform nano-manipulation more accurately, it is necessary to study the relationship between the scanning electron microscope (SEM) image and the actual length. In this paper, based on the secondary electron image, the displacement of 1.81nm in SEM was measured the actual displacement was obtained according to the change of the pixels, so as to realize the nanometer displacement calibration and real-time measurement of the nano robot manipulator in SEM. According to the magnification, resolution and scale bar of SEM image, the mapping relationship between pixels and actual length was established. The obvious feature point and area in the SEM image were selected to be marked. The driving signal was applied to the manipulator of the nano robot, and the template matching algorithm was used to track the feature point and area in real time. The change of the feature point in the SEM image was obtained, and the actual displacement of the manipulator was calculated according to the mapping relationship between the pixel points and the actual displacement. We calibrated the displacement of the nanorobots and measured the actual displacement in real time, which laid a foundation for the precise control of the nanorobots in the future.","PeriodicalId":232892,"journal":{"name":"2021 IEEE International Conference on Real-time Computing and Robotics (RCAR)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132311781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Real-Time Attitude Tracking of Capsule Endoscope Based on MEMS IMU and Error Analysis 基于MEMS IMU的胶囊内窥镜实时姿态跟踪及误差分析

2021 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2021-07-15 DOI: 10.1109/RCAR52367.2021.9517523

Zhuokang Huang, Chengzhi Hu

{"title":"Real-Time Attitude Tracking of Capsule Endoscope Based on MEMS IMU and Error Analysis","authors":"Zhuokang Huang, Chengzhi Hu","doi":"10.1109/RCAR52367.2021.9517523","DOIUrl":"https://doi.org/10.1109/RCAR52367.2021.9517523","url":null,"abstract":"Wireless capsule endoscope (WCE) has been increasingly exploited for noninvasive screening and diagnosis of the entire esophagus, stomach, colon, and small intestine. WCE allows direct visualization of gastrointestinal tracts and causes less pain to the patients. Automated navigation of WCE during the medical procedure will significantly reduce the testing time and improve the usage and efficiency of medical infrastructure. Attitude tracking of WCE is of great importance for real-time tracking of the position and orientation of capsule endoscope. In this paper, a method for calculating the attitude of WCE based on MEMS IMU (Inertial Measurement Units) is implemented. The accuracy of attitude tracking is measured by a series of experiments. By reorientating the capsule endoscope with a high precision 3D rotary stage, the error from the proposed algorism is determined. The experimental results show that the attitude of WCE based on MEMS IMU can meet the diagnostic requirements in about six minutes, but the performance cannot be maintained due to error accumulation. Additionally, we propose a method to improve the attitude accuracy by reciprocating the rotational motion during the inspection process.","PeriodicalId":232892,"journal":{"name":"2021 IEEE International Conference on Real-time Computing and Robotics (RCAR)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126629255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Design and implementation of variable stiffness rigid-soft coupling pneumatic actuated joint 变刚度刚软耦合气动驱动关节的设计与实现

2021 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2021-07-15 DOI: 10.1109/RCAR52367.2021.9517478

Jinfeng Zhao, Changqu Wu, Wenbiao Wang, Shineng Sheng, Z.C. Qiu, Manrong Wang, G. Bao

引用次数: 2

Thrust Ripple Minimization of PMLSM Using Robust Two Degrees-of-Freedom controller and Thrust Ripple Observer 基于鲁棒二自由度控制器和推力脉动观测器的永磁同步电机推力脉动最小化

2021 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2021-07-15 DOI: 10.1109/RCAR52367.2021.9517574

Mingfei Huang, Yongting Deng, Hongwen Li, Jing Liu, Meng Shao

引用次数: 1

A Moving Target Detection and Localization Strategy Based on Optical Flow and Pin-hole Imaging Methods Using Monocular Vision 基于光流和针孔成像的单目运动目标检测与定位策略

2021 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2021-07-15 DOI: 10.1109/RCAR52367.2021.9517462

Shun Wang, Qingqiang Guo, Sheng Xu, Dan Su

{"title":"A Moving Target Detection and Localization Strategy Based on Optical Flow and Pin-hole Imaging Methods Using Monocular Vision","authors":"Shun Wang, Qingqiang Guo, Sheng Xu, Dan Su","doi":"10.1109/RCAR52367.2021.9517462","DOIUrl":"https://doi.org/10.1109/RCAR52367.2021.9517462","url":null,"abstract":"This paper proposes a new strategy for moving target detection and localization based on monocular vision. Firstly, to detect a moving target with large displacement and high speed accurately, two consecutive video images captured by a monocular camera are preprocessed using the enhancement and denoising methods. Then, the optical flow representing motion information is calculated iteratively by the modified Lucas-Kanade optical flow method. Secondly, a new interest region extraction method is developed to overcome the negative impacts caused by the noises in the background. Specifically, this proposed method combines a two-level image segmentation strategy from coarse to fine, including median filtering, two-direction dynamic threshold segmentation, the Otsu method, and morphological processing. Thirdly, a low computational cost target localization algorithm is proposed based on pin-hole imaging theory. Besides, it only uses two-dimensional image and camera parameters to obtain the moving target's position in the three-dimensional space. Finally, experimental results show that the proposed strategy can effectively eliminate noise interferences and realize moving target detection, extraction, and localization.","PeriodicalId":232892,"journal":{"name":"2021 IEEE International Conference on Real-time Computing and Robotics (RCAR)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127632283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5