2021 IEEE International Conference on Real-time Computing and Robotics (RCAR)最新文献_第8页

A data-driven shared control system for exoskeleton rehabilitation robot 外骨骼康复机器人数据驱动共享控制系统

2021 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2021-07-15 DOI: 10.1109/RCAR52367.2021.9517363

Feng Li, Yong He, Jinke Li, J. Ni, Xin Wu

引用次数: 0

The effects of different training modes on the performance of silent speech recognition based on high-density sEMG 不同训练模式对基于高密度表面肌电信号的无声语音识别性能的影响

2021 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2021-07-15 DOI: 10.1109/RCAR52367.2021.9517619

Yao Pi, Mingxing Zhu, Zijian Yang, Xin Wang, Cheng Wang, Haoshi Zhang, Mingjiang Wang, Feng Wan, Shixiong Chen, Guanglin Li

{"title":"The effects of different training modes on the performance of silent speech recognition based on high-density sEMG","authors":"Yao Pi, Mingxing Zhu, Zijian Yang, Xin Wang, Cheng Wang, Haoshi Zhang, Mingjiang Wang, Feng Wan, Shixiong Chen, Guanglin Li","doi":"10.1109/RCAR52367.2021.9517619","DOIUrl":"https://doi.org/10.1109/RCAR52367.2021.9517619","url":null,"abstract":"The convolutional neural network (CNN) is frequenctly used in silent speech recognition (SSR) based on surface electromyography (sEMG). Currently, there are two different modes when training the CNN classifier, using the sEMG signals from a single subject as the training datasets and using the mixed signals from multiple subjects as the training datasets. However, it still remains unclear how different training modes affect the performance of the CNN classifier in different classification metrics. In this study, two different training modes were used for the CNN classifier of SSR based on high-density sEMG (HD sEMG) signals. HD sEMG signals collected from six subjects were used to build two different training datasets. The HD sEMG signals from either a single subject or multiple subjects were to train the same CNN model and the performance difference was thoroughly compared in different metrics. The results showed that the CNN model trained from the signals of a single subject showed superior performance with higher average precision, average recall, and average F1 score. It also converged faster and was more stable under different signal conditions. However, it was only suitable for the SSR of the same subject, while the CNN model trained from the signals of multiple subjects showed satisfactiroy performance across all the recruited subjects. This study revealed that the CNN models trained with different training modes performed differently, and therefore the training mode could be taken into consideration in different applications of SSR based on sEMG.","PeriodicalId":232892,"journal":{"name":"2021 IEEE International Conference on Real-time Computing and Robotics (RCAR)","volume":"12 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":"132751158","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

Prescribed Performance Sliding Mode Control for Safe De-Tumbling a Rolling Target by Eddy Current 涡流对滚动目标安全除滚的规定性能滑模控制

2021 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2021-07-15 DOI: 10.1109/RCAR52367.2021.9517571

Chen Zhai, Panfeng Huang, Gangqi Dong, Xiyao Liu

{"title":"Prescribed Performance Sliding Mode Control for Safe De-Tumbling a Rolling Target by Eddy Current","authors":"Chen Zhai, Panfeng Huang, Gangqi Dong, Xiyao Liu","doi":"10.1109/RCAR52367.2021.9517571","DOIUrl":"https://doi.org/10.1109/RCAR52367.2021.9517571","url":null,"abstract":"Accelerated by in-orbit explosions, collisions and other fragmentation events, the population of space debris is increasing sharply. Many of these space debris are spinning at high speed. Traditional space capture technologies cannot directly capture the high speed spinning non-cooperative targets. In order to reduce the spinning speed of the target, eddy current break is considered as one of the most promising methods because of the non-contact nature. Unfortunately, efficiency and safety concerns prevent its further application in space. Focus on this issue, the desired trajectory is calculated in this paper with a perpendicular configuration between the chaser and the target. Meanwhile, the safety constraint is considered to eliminate potential collision risk. Furthermore, a sliding model control (SMC) is designed to guarantee the chaser tracking the desired trajectory, where prescribed performance function is adopted to ensure the state error within the safety constraint. Finally, numerical simulation is performed to validate the effectiveness and efficiency of the proposed control algorithm.","PeriodicalId":232892,"journal":{"name":"2021 IEEE International Conference on Real-time Computing and Robotics (RCAR)","volume":"24 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":"132753096","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

Form-finding of Tensegrity Structures Utilizing a Nonlinear Fletcher-Reeves Conjugate Gradient Method 基于非线性Fletcher-Reeves共轭梯度法的张拉整体结构寻形

2021 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2021-07-15 DOI: 10.1109/RCAR52367.2021.9517591

Liming Zhao, Keping Liu, Chunxu Li, Long Jin, Zhongbo Sun

引用次数: 2

RNA genetic algorithm based on octopus learning mechanism 基于章鱼学习机制的RNA遗传算法

2021 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2021-07-15 DOI: 10.1109/RCAR52367.2021.9517596

Lifeng Zhang, Qiuxuan Wu, Xiaoni Chi, Jian Wang, Botao Zhang, Weijie Lin, S. A. Chepinskiy, A. Zhilenkov, Yanbin Luo, Farong Gao

引用次数: 0

A Prior Information Heuristic based Robot Exploration Method in Indoor Environment 基于先验信息启发式的室内环境机器人探索方法

2021 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2021-07-15 DOI: 10.1109/RCAR52367.2021.9517416

Jie Liu, Yong Lv, Yuan Yuan, Wenzheng Chi, Guodong Chen, Lining Sun

{"title":"A Prior Information Heuristic based Robot Exploration Method in Indoor Environment","authors":"Jie Liu, Yong Lv, Yuan Yuan, Wenzheng Chi, Guodong Chen, Lining Sun","doi":"10.1109/RCAR52367.2021.9517416","DOIUrl":"https://doi.org/10.1109/RCAR52367.2021.9517416","url":null,"abstract":"The Rapidly-exploring Random Tree (RRT) based method has been widely used in robotic exploration, which achieves better performance than other exploration methods in most scenes. However, its core idea is a greedy strategy, that is, the robot chooses the frontier with the largest revenue value as the target point regardless of the explored environment structure. It is inevitable that before a certain area is fully explored, the robot will turn to other areas to explore, resulting in the backtracking phenomenon with a relatively lower exploration efficiency. In this paper, inspired by the perception law of bionic human, a new exploration strategy is proposed on the basis of the prior information heuristic. Firstly, a lightweight network model is proposed for the recognition of the heuristic objects. Secondly, the prediction region is formed based on the position of the heuristic object, and the frontiers in this region are extracted by the method of image processing. Finally, a heuristic information gain model is designed to guide the robot to explore, which allocates priority to the frontiers within the heuristic object area, so that the robot can make effective use of the prior knowledge of the room in the scene. Priority is given to the exploration of one room completely and then to the next, which can greatly improve the efficiency of exploration. In the experimental studies, we compare our method with RRT based exploration method in different environments, and the experimental results prove the effectiveness of our method.","PeriodicalId":232892,"journal":{"name":"2021 IEEE International Conference on Real-time Computing and Robotics (RCAR)","volume":"12 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":"117043863","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: 9

sEMG-Based Gesture Recognition Using GRU With Strong Robustness Against Forearm Posture 基于表面肌电信号的GRU对前臂姿态的强鲁棒性手势识别

2021 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2021-07-15 DOI: 10.1109/RCAR52367.2021.9517639

Rui Chen, YuanZhi Chen, Weiyu Guo, Chao Chen, Zheng Wang, Yongkui Yang

{"title":"sEMG-Based Gesture Recognition Using GRU With Strong Robustness Against Forearm Posture","authors":"Rui Chen, YuanZhi Chen, Weiyu Guo, Chao Chen, Zheng Wang, Yongkui Yang","doi":"10.1109/RCAR52367.2021.9517639","DOIUrl":"https://doi.org/10.1109/RCAR52367.2021.9517639","url":null,"abstract":"The surface Electromyographic (sEMG) based gesture recognition has been widely adopted in human–computer interaction. Traditional machine learning algorithms, such as Random Forest, SVM and KNN, have been employed for sEMG-based gesture recognition. Even though these traditional machine learning methods achieve high classification accuracy, few of reported works consider the gesture recognition robustness against different forearm postures, which often happens in real application scenario. On the other side, since the sEMG signals represent the sum of subcutaneous motor action potentials, the features of sampled sEMG under various forearm postures will be significantly different. Our experimental results show that the classification accuracy of gesture recognition using Random Forest reduces from 81% to 44%, when changing the forearm posture. In this paper, we propose a sEMG-based gesture recognition that uses recurrent neural network, specifically the Gate Recurrent Unit (GRU), to improve the robustness of gesture recognition against forearm posture. The experimental results show that the robustness against different forearm postures of our proposed gesture recognition is much stronger than that using traditional machine learning algorithms, including Random Forest, Decision Tree, SVM, KNN and Naive Bayes.","PeriodicalId":232892,"journal":{"name":"2021 IEEE International Conference on Real-time Computing and Robotics (RCAR)","volume":"22 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":"117337009","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: 3

A Generalized Kinematic Error Modeling Method for Serial Industrial Robots Based on Product of Exponentials Formula 基于指数积公式的串联工业机器人运动误差广义建模方法

2021 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2021-07-15 DOI: 10.1109/RCAR52367.2021.9517556

Zeyin Zhao, Xin Wang, Jiafang Chen, Mengzhong Chen

{"title":"A Generalized Kinematic Error Modeling Method for Serial Industrial Robots Based on Product of Exponentials Formula","authors":"Zeyin Zhao, Xin Wang, Jiafang Chen, Mengzhong Chen","doi":"10.1109/RCAR52367.2021.9517556","DOIUrl":"https://doi.org/10.1109/RCAR52367.2021.9517556","url":null,"abstract":"Geometric errors such as inaccurate link length and assembly alignment are the primary sources of positioning errors for industrial robots. Besides, complex joint-dependent kinematic errors in the bearing system and harmonic drives are also non-negligible. The robot is regarded as an ideal rigid body in typical kinematic models, which can only describe the influence of geometric errors. This paper proposes a generalized kinematic error model based on product of exponentials (POE) formula, which contains constant geometric errors and complex joint-dependent kinematic errors. The unknown model parameters are identified with the Levenberg-Marquardt method. Experiments are implemented on an Efort ECR5 robot to validate the effectiveness of the proposed model. In these experiments, we use 250 measurements as the identification data set for parameter identification, and other 100 measurements are utilized to validate the accuracy of the proposed model. These experiments display that the proposed model can reduce the mean position error of the Efort ECR5 robot from 2.014 mm to 0.115 mm on the validation data set. Experimental results prove that the proposed model can describe the kinematics of industrial robots with high accuracy.","PeriodicalId":232892,"journal":{"name":"2021 IEEE International Conference on Real-time Computing and Robotics (RCAR)","volume":"77 7 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":"114151563","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

sEMG-based Gesture Recognition by Rotation Forest-Based Extreme Learning Machine 基于旋转森林的极限学习机基于表面肌电信号的手势识别

2021 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2021-07-15 DOI: 10.1109/RCAR52367.2021.9517479

Fulai Peng, Cai Chen, Xikun Zhang, Xingwei Wang, Changpeng Wang, Lin Wang

{"title":"sEMG-based Gesture Recognition by Rotation Forest-Based Extreme Learning Machine","authors":"Fulai Peng, Cai Chen, Xikun Zhang, Xingwei Wang, Changpeng Wang, Lin Wang","doi":"10.1109/RCAR52367.2021.9517479","DOIUrl":"https://doi.org/10.1109/RCAR52367.2021.9517479","url":null,"abstract":"The motion information contained in surface electromyography (sEMG) signals contributes significantly to the prosthetic hand control. However, the accuracy and speed of gesture recognition from sEMG signals are still insufficient for natural control. In order to alleviate this problem, this paper propose a rotation forest-based extreme learning machine method (RoF-ELM) to improve the recognition performance based on sEMG signals. Firstly, the active motion segments were picked out and pre-processed by sliding window. Then, 104 features were extracted from each sample, and SVM-RFE method was used to reduce the feature space dimension. Finally, the RoF-ELM classification model was constructed and tested based on the EMG data for gestures Data Set containing a total of 72 recordings, each of which consists of six basic gestures. 100 trials were implemented to validate the performance of the proposed method, the results show that the RoF-ELM method have the highest accuracy (91.11%) across different subjects with relatively short runtime compared with decision tree (DT), ELM, random forest (RF), and RoF methods.","PeriodicalId":232892,"journal":{"name":"2021 IEEE International Conference on Real-time Computing and Robotics (RCAR)","volume":"104 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":"116187786","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: 2

Virtual reality navigation system of nasal endoscopy with real surface texture information 基于真实表面纹理信息的鼻内窥镜虚拟现实导航系统

2021 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2021-07-15 DOI: 10.1109/RCAR52367.2021.9517378

Zhouhai Cui, Yucheng He, Peng Zhang, Ying Hu, Haiyang Jin, S. Liu

{"title":"Virtual reality navigation system of nasal endoscopy with real surface texture information","authors":"Zhouhai Cui, Yucheng He, Peng Zhang, Ying Hu, Haiyang Jin, S. Liu","doi":"10.1109/RCAR52367.2021.9517378","DOIUrl":"https://doi.org/10.1109/RCAR52367.2021.9517378","url":null,"abstract":"The 3D information of medical images can effectively assist surgeons in diagnosis and surgical planning. In this paper, the methods of monocular endoscopic reconstruction of the inner surface of the nasal cavity and nasal endoscopic surgery path planning are proposed, and a virtual nasal endoscopic navigation system with real surface texture information is realized, which provides an intuitive reference for surgeons' diagnosis and surgery. First, by extracting the ASIFT feature points of the nasal endoscopic images, the structure from motion and multi-view stereo reconstruction are performed to construct a point cloud model. The point cloud model is used to reconstruct the triangle surface and texture mapping to obtain a 3D model of the nasal cavity with real surface texture information. Secondly, in view of the lack of scale information of the obtained monocular nasal reconstruction model, a variable-scale registration method between the obtained monocular nasal reconstruction model and the CT image reconstruction model is proposed. The FPFH algorithm is used to achieve feature description and matching, and coarse registration is achieved; the variable scale iterative closest point algorithm is used to achieve fine registration. Then, considering the cylinder shape of the nasal endoscope, a collision-free 3D planning map is constructed, and the RRT algorithm is used to realize the collision-free path planning under the complex nasal cavity space constraints. Finally, a virtual nasal endoscopy navigation system with real surface texture information is constructed, and virtual nasal endoscopy experiments are carried out to verify the effectiveness of the navigation system.","PeriodicalId":232892,"journal":{"name":"2021 IEEE International Conference on Real-time Computing and Robotics (RCAR)","volume":"7 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":"127240103","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