2021 IEEE International Conference on Real-time Computing and Robotics (RCAR)最新文献_第7页

Design and Energy Consumption Optimization of an Automatic Hybrid Sailboat 自动混合动力帆船的设计与能耗优化

2021 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2021-07-15 DOI: 10.1109/RCAR52367.2021.9517339

Rong-Shyang Ou, Cheng Liang, X. Ji, Huihuan Qian

引用次数: 2

Saturated nonlinear control of robots with series elastic actuators 串联弹性作动器机器人的饱和非线性控制

2021 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2021-07-15 DOI: 10.1109/RCAR52367.2021.9517535

Jie Cheng, Shengpei Ding, Hongjun Yang, Xuexin Zhang, Tairen Sun

引用次数: 0

Finger Joint Angle Estimation based on sEMG signals and deep learning method 基于表面肌电信号和深度学习方法的手指关节角度估计

2021 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2021-07-15 DOI: 10.1109/RCAR52367.2021.9517643

Chenfei Ma, Weiyu Guo, Lisheng Xu, Guanglin Li

{"title":"Finger Joint Angle Estimation based on sEMG signals and deep learning method","authors":"Chenfei Ma, Weiyu Guo, Lisheng Xu, Guanglin Li","doi":"10.1109/RCAR52367.2021.9517643","DOIUrl":"https://doi.org/10.1109/RCAR52367.2021.9517643","url":null,"abstract":"Conventional movement recognition methods are normally based on classification algorithms, which could only provide discrete movement classification rather than natural human body continuous movements. In this paper, we utilized the deep learning methods to estimate eight complicated movements of fingers by extracting the kinematic information based on surface electromyographic (sEMG) signals. Aiming at realizing continuous estimation, we adopted four representative models, AlexNet, Residual neural network (ResNet), Long Short-term Memory network (LSTM) and Gate Recurrent Unit (GRU) in this study. Convolutional kind models (AlexNet and ResNet) are chosen because of their irreplaceable feature extraction ability. And recurrent kind models (LSTM and GRU) are chosen because they are suitable for time-series signal processing. We took 10 degrees of freedom (DoFs) of joint angles from one hand as the target, 12 channels of sEMG as input and trained the models with the stochastic gradient descent and backpropagation. The models were tested on 8 abled subjects. The results indicated that the employed AlexNet turned out to show the best estimation performance and stability than other models. We realized the AlexNet is more suitable for sEMG based continuous movement estimation.","PeriodicalId":232892,"journal":{"name":"2021 IEEE International Conference on Real-time Computing and Robotics (RCAR)","volume":"17 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":"130086911","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

Tactile Grasp Stability Classification Based on Graph Convolutional Networks 基于图卷积网络的触觉抓握稳定性分类

2021 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2021-07-15 DOI: 10.1109/RCAR52367.2021.9517085

Tingting Mi, D. Que, Senlin Fang, Zhenning Zhou, Chaoxiang Ye, Chengliang Liu, Zhengkun Yi, Xinyu Wu

引用次数: 6

Research on Diameter Prediction of Silicon Single Crystal Based on Data Driven 基于数据驱动的硅单晶直径预测研究

2021 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2021-07-15 DOI: 10.1109/RCAR52367.2021.9517426

Xiya Zhang, Shan Wang, D. Gao, Yan Zhao, G. Lin, Xin Peng

引用次数: 0

WRS: A Novel Word-embedding Method for Real-time Sentiment with Integrated LSTM-CNN Model 基于LSTM-CNN模型的实时情感词嵌入方法

2021 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2021-07-15 DOI: 10.1109/RCAR52367.2021.9517671

Abdur Rasool, Qingshan Jiang, Qiang Qu, Chaojie Ji

{"title":"WRS: A Novel Word-embedding Method for Real-time Sentiment with Integrated LSTM-CNN Model","authors":"Abdur Rasool, Qingshan Jiang, Qiang Qu, Chaojie Ji","doi":"10.1109/RCAR52367.2021.9517671","DOIUrl":"https://doi.org/10.1109/RCAR52367.2021.9517671","url":null,"abstract":"Artificial Intelligence (AI) is a research-focused technology in which Natural Language Processing (NLP) is a core technology in AI. Sentiment Analysis (SA) aims to extract and classify the people's opinions by NLP. The Machine Learning (ML) and lexicon dictionaries have limited competency to efficiently analyze massive live media data. Recently, deep learning methods significantly enrich the accuracy of recent sentiment models. However, the existing methods provide the aspect-based extraction that reduces individual word accuracy if a sentence does not follow the aspect information in real-time. Therefore, this paper proposes a novel word embedding method for the real-time sentiment (WRS) for word representation. The WRS's novelty is a novel word embedding method, namely, Word-to-Word Graph (W2WG) embedding that utilizes the Word2Vec approach. The WRS method assembles the different lexicon resources to employ the W2WG embedding method to achieve the word feature vector. Robust neural networks leverage these features by integrating LSTM and CNN to improve sentiment classification performance. LSTM is utilized to store the word sequence information for the effective real-time SA, and CNN is applied to extract the leading text features for sentiment classification. The experiments are conducted on Twitter and IMDB datasets. The results demonstrate our proposed method's effectiveness for real-time sentiment classification.","PeriodicalId":232892,"journal":{"name":"2021 IEEE International Conference on Real-time Computing and Robotics (RCAR)","volume":"29 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":"122286318","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: 6

Stereo Vision-based Autonomous Navigation for Oil and Gas Pressure Vessel Inspection Using a Low-cost UAV 基于立体视觉的低成本无人机油气压力容器检测自主导航

2021 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2021-07-15 DOI: 10.1109/RCAR52367.2021.9517584

Leijian Yu, Erfu Yang, Beiya Yang, Andrew Loeliger, Zixiang Fei

{"title":"Stereo Vision-based Autonomous Navigation for Oil and Gas Pressure Vessel Inspection Using a Low-cost UAV","authors":"Leijian Yu, Erfu Yang, Beiya Yang, Andrew Loeliger, Zixiang Fei","doi":"10.1109/RCAR52367.2021.9517584","DOIUrl":"https://doi.org/10.1109/RCAR52367.2021.9517584","url":null,"abstract":"It is vital to visually inspect pressure vessels regularly in the oil and gas company to maintain their integrity. Compared with visual inspection conducted by sending engineers and ground vehicles into the pressure vessel, utilising an autonomous Unmanned Aerial Vehicle (UAV) can overcome many limitations including high labour intensity, low efficiency and high risk to human health. This work focuses on enhancing some existing technologies to support low-cost UAV autonomous navigation for visual inspection of oil and gas pressure vessels. The UAV can gain the ability to follow the planned trajectory autonomously to record videos with a stereo camera in the pressure vessel, which is a GPS-denied and low-illumination environment. Particularly, the ORB-SLAM3 is improved by adopting the image contrast enhancement technique to locate the UAV in this challenging scenario. What is more, a vision hybrid Proportional-Proportional-Integral-Derivative (P-PID) position tracking controller is integrated to control the movement of the UAV. The ROS-Gazebo-PX4 simulator is customised deeply to validate the developed stereo vision-based autonomous navigation approach. It is verified that compared with the ORB-SLAM3, the numbers of ORB feature points and effective matching points obtained by the improved ORB-SLAM3 are increased by more than 400% and 600%, respectively. Thereby, the improved ORB-SLAM3 is effective and robust enough for UAV self-localisation, and the developed stereo vision-based autonomous navigation approach can be deployed for pressure vessel visual inspection.","PeriodicalId":232892,"journal":{"name":"2021 IEEE International Conference on Real-time Computing and Robotics (RCAR)","volume":"123 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":"127043545","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

A 22-DOFs Bio-inspired Soft Hand Achieving 6 Kinds of In-hand Manipulation 22自由度仿生软手，实现6种手部操作

2021 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2021-07-15 DOI: 10.1109/RCAR52367.2021.9517366

Jianshu Zhou, Yunquan Li, Yang Yang, Hanwen Cao, Junda Huang, Yunhui Liu

{"title":"A 22-DOFs Bio-inspired Soft Hand Achieving 6 Kinds of In-hand Manipulation","authors":"Jianshu Zhou, Yunquan Li, Yang Yang, Hanwen Cao, Junda Huang, Yunhui Liu","doi":"10.1109/RCAR52367.2021.9517366","DOIUrl":"https://doi.org/10.1109/RCAR52367.2021.9517366","url":null,"abstract":"Bio-inspired soft robots present distinctive superiorities in safety issues working in a human-centered environment. Soft robotic hands are of prominent popularity for soft robots to be applied in real applications. With continuous efforts being devoted, researchers have been developing various soft hands performing excellent grasping capability towards daily objects with salient safety insurance. However, the functional dexterity of soft robotic hands is still limited, especially because lacking in-hand object manipulation capabilities, which inevitably limits the application potential of soft robotic hands. This restriction is induced by the limited dexterity of soft actuators usually with a one-DOF per actuator providing a predefined motion trajectory. To tackle this challenge, a novel bio-inspired multi-DOF soft actuator based on V-joint is proposed, which achieves human finger-joint-like motion. By connecting the V-joint in a desired order, it is possible to build multi-DOF soft robots like traditional rigid counterparts. A 22-DOFs anthropomorphic soft hand (S-22) is fabricated out based on the V-joint, which is capable of realizing dexterous hand gestures and 6 kinds of in-hand manipulation. The presented soft robotic approach is a promising solution for dexterous soft robotic hand design and broden the potential application of soft hands.","PeriodicalId":232892,"journal":{"name":"2021 IEEE International Conference on Real-time Computing and Robotics (RCAR)","volume":"29 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":"126766065","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

Bronchoscopic Interventional Surgery Robot which Constrained by a Shear-Fork Mechanism 基于剪叉机构约束的支气管镜介入手术机器人

2021 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2021-07-15 DOI: 10.1109/RCAR52367.2021.9517429

Jie Li, Chao Han, Tao Yu, Xiao He, Zhenming Jiang, Leijie Sun, Hao Liu

引用次数: 0

Joint Torque Feedback-based Decentralized Neuro-optimal Control of Input-constrained Modular Robot Manipulator System 基于关节转矩反馈的输入约束模块化机器人系统分散神经最优控制

2021 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2021-07-15 DOI: 10.1109/RCAR52367.2021.9517614

B. Ma, Yuan-chun Li

引用次数: 0