IET Cybersystems and Robotics最新文献_第10页

Research on Human Features Semantic Segmentation Based on Laser Point Cloud 基于激光点云的人体特征语义分割研究

IET Cybersystems and Robotics Pub Date : 2022-07-27 DOI: 10.1109/CYBER55403.2022.9907384

Tianyi Ma, Bokai Xuan, Jian Li, Yuexuan Xu, Minghe Liu, Qingsong Ding, Jianwen Wang, Hao Sun

引用次数: 0

Evaluation and Comparison of Gmapping and Karto SLAM Systems Gmapping与Karto SLAM系统的评价与比较

IET Cybersystems and Robotics Pub Date : 2022-07-27 DOI: 10.1109/CYBER55403.2022.9907154

Shengshu Liu, Y. Lei, Xin Dong

引用次数: 0

Gait tracking control of biped robot based on adaptive gait switching algorithm 基于自适应步态切换算法的双足机器人步态跟踪控制

IET Cybersystems and Robotics Pub Date : 2022-07-27 DOI: 10.1109/CYBER55403.2022.9907560

Jianjun Yu, Ruiqi Li, Daoxiong Gong, Yixin Liu, Peng Liu

{"title":"Gait tracking control of biped robot based on adaptive gait switching algorithm","authors":"Jianjun Yu, Ruiqi Li, Daoxiong Gong, Yixin Liu, Peng Liu","doi":"10.1109/CYBER55403.2022.9907560","DOIUrl":"https://doi.org/10.1109/CYBER55403.2022.9907560","url":null,"abstract":"In order to make the walking gait of biped robot more human like, this paper takes the human walking data as the expected gait of robot, and uses the periodic characteristics of gait, proposes a gait tracking control strategy of Biped Robot Based on adaptive gait switching algorithm. Firstly, this paper establishes the complete dynamic models of left leg support phase (LSP) and right leg support phase (RSP) based on Lagrange method, then designs the corresponding LQR gait tracking control strategy, and uses the adaptive weighted particle swarm algorithm (A WPSO) to obtain the optimal controller parameters. Finally, the threshold range of plantar contact force in two periods are estimated based on the adaptive mechanism, and the occurrence of gait switching is detected according to the defined decision rules, thus trigger the control strategy in the next stage to realize the walking tracking control of biped robot. The experimental results show that only two LQR controllers to realize the accurate tracking of the desired gait of the biped robot, and the maximum gait speed reaches two steps/s, which is close to the human gait speed. Compared with other methods, the gait is more human like.","PeriodicalId":34110,"journal":{"name":"IET Cybersystems and Robotics","volume":"42 4 1","pages":"105-110"},"PeriodicalIF":0.0,"publicationDate":"2022-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90424781","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

An Efficient Color and Geometric Feature Fusion Module for 6D Object Pose Estiamtion 一种有效的6D物体姿态估计颜色和几何特征融合模块

IET Cybersystems and Robotics Pub Date : 2022-07-27 DOI: 10.1109/CYBER55403.2022.9907032

Jiangeng Li, Hong Liu, Gao Huang, Guoyu Zuo

引用次数: 0

Design of Programmable Droplet Manipulation Platform Based on Magnetic Control 基于磁控的可编程液滴操作平台设计

IET Cybersystems and Robotics Pub Date : 2022-07-27 DOI: 10.1109/CYBER55403.2022.9907625

Xianmiao Zhang, Jie Liu, Jiying Liu, Yu-zhou Wang, Mian Zhang, Hongbiao Xiang

{"title":"Design of Programmable Droplet Manipulation Platform Based on Magnetic Control","authors":"Xianmiao Zhang, Jie Liu, Jiying Liu, Yu-zhou Wang, Mian Zhang, Hongbiao Xiang","doi":"10.1109/CYBER55403.2022.9907625","DOIUrl":"https://doi.org/10.1109/CYBER55403.2022.9907625","url":null,"abstract":"Droplet actuation simplifies the handling of various reagents or samples and can be applied to a wide range of fields, including chemistry, biology, biomedical, and others. This paper presents a programmable droplet control system based on a magnetoelastic membrane and electromagnetic pillar array. Different magnetic blocks with different magnetization directions were designed on the silicone rubber membrane, and the magnetoelastic membrane deformed under the magnetic field generated by the array of electromagnetic pillars. By combining the gravitational forces of the droplet and the deformation of the magnetic membranes, the motion of the droplet can be controlled. Furthermore, the surface of membranes was ablated with a laser machine to impart superhydrophobic properties. The simulation results show that with the different magnetic fields, the droplet can move lengthwise, widthwise, and diagonally in the horizontal plane, and multiple droplets can be merged and mixed. In contrast to the traditional droplet control method, the droplet programmable movement control system utilizing superhydrophobic magnetoelastic membranes and an electromagnetic pillar array has better stationarity, flexibility and does not affect the basic properties of the droplets.","PeriodicalId":34110,"journal":{"name":"IET Cybersystems and Robotics","volume":"21 1","pages":"1224-1229"},"PeriodicalIF":0.0,"publicationDate":"2022-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74911572","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

Robot Manipulation Skill Learning Based on Dynamic Movement Primitive* 基于动态运动原语的机器人操作技能学习

IET Cybersystems and Robotics Pub Date : 2022-07-27 DOI: 10.1109/CYBER55403.2022.9907262

Yunfeng Bai, Fengming Li, Man Zhao, Wei Wang, Yibin Li, R. Song

引用次数: 0

A Novel Lightweight Architecture of Deep Convolutional Neural Networks 一种新的轻量级深度卷积神经网络架构

IET Cybersystems and Robotics Pub Date : 2022-07-27 DOI: 10.1109/CYBER55403.2022.9907319

Baicheng Liu, Xi’ai Chen, Zhi Han, Huidi Jia, Yandong Tang

引用次数: 0

Research on Structure Design and Control of Plane 3-DOF Cable Driven Virtual Microgravity Training System 平面三自由度缆索驱动虚拟微重力训练系统结构设计与控制研究

IET Cybersystems and Robotics Pub Date : 2022-07-27 DOI: 10.1109/CYBER55403.2022.9907691

Xue Feng, Zhang Li-xun, Wang Chao, Wang Zhen-han, Fan Yu-he

{"title":"Research on Structure Design and Control of Plane 3-DOF Cable Driven Virtual Microgravity Training System","authors":"Xue Feng, Zhang Li-xun, Wang Chao, Wang Zhen-han, Fan Yu-he","doi":"10.1109/CYBER55403.2022.9907691","DOIUrl":"https://doi.org/10.1109/CYBER55403.2022.9907691","url":null,"abstract":"Astronauts' microgravity environment simulation training on the ground is an important preparation for space operation tasks. In view of the problems of high cost, short single training time and low simulation accuracy of the existing microgravity training equipment, a virtual microgravity training system driven by plane 3 degrees of freedom (hereinafter referred to as “DOF”) cable is proposed. The system controls the motion of the virtual object pulled by the cable by sampling the astronauts' operating force on the working object; so that the virtual object conforms to the motion law in the microgravity environment. The structure of the system is designed. Aiming at the problems of insufficient workspace and high requirements for the performance of the driving unit caused by the unreasonable distribution of cable tension in the previous cable drive system, a control strategy of optimizing cable tension using genetic algorithm is proposed. The simulation results show that the motion of virtual mass under the action of operating force conforms to the motion law in microgravity environment, and has high simulation accuracy; The cable tension changes smoothly and the system has good stability. It can realize the simulated operation training of moving objects with different masses in microgravity environment.","PeriodicalId":34110,"journal":{"name":"IET Cybersystems and Robotics","volume":"30 4","pages":"1218-1223"},"PeriodicalIF":0.0,"publicationDate":"2022-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72616493","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

Photovoltaic and energy storage control of partially observable distribution network based on deep reinforcement learning 基于深度强化学习的部分可观测配电网光伏与储能控制

IET Cybersystems and Robotics Pub Date : 2022-07-27 DOI: 10.1109/CYBER55403.2022.9907595

Q. Bu, P. Lv, Kexin Zhang, Xiaobo Dou, Fei Luo, Xufeng Zhou

引用次数: 0

Prediction of Battery Capacity Based on Deep Residual Network 基于深度残差网络的电池容量预测

IET Cybersystems and Robotics Pub Date : 2022-07-27 DOI: 10.1109/CYBER55403.2022.9907034

Yankui Wang, Wenhao Yao, Min Dong, Yixuan Li, Longxing Zhu, Sheng Bi

{"title":"Prediction of Battery Capacity Based on Deep Residual Network","authors":"Yankui Wang, Wenhao Yao, Min Dong, Yixuan Li, Longxing Zhu, Sheng Bi","doi":"10.1109/CYBER55403.2022.9907034","DOIUrl":"https://doi.org/10.1109/CYBER55403.2022.9907034","url":null,"abstract":"Consistency is essential to the life of battery packs. Therefore, there is a special process to determine the capacity of lithium batteries in their production process (aka grading). However, this process takes a very long time. We propose a new method based on deep learning, which uses data collected by sensors before the grading process to predict the battery capacity, hoping to reduce the time consumed in the whole process. We propose an end-to-end battery capacity prediction model. In our processing steps, complex feature extraction steps are not needed. On the contrary, we use a residual network to complete it automatically. We modified the original ResNet to suit our task. Convolution1D and global pooling layers are used to extract the time series feature. To improve the model's accuracy, we design a fusion model to deal with the time series of multi-step processes. Transfer learning is applied to help us train the model faster. The results on the test set show that the root mean square error of the predicted capacity of our fusion model is 4mAh, which is a 45% decline compared with the benchmark model. We visualize the extracted features, interpret the model and explain the possible mechanism of our model. Furthermore, based on our analysis, suggestions for improving prediction performance are put forward.","PeriodicalId":34110,"journal":{"name":"IET Cybersystems and Robotics","volume":"226 1 1","pages":"462-467"},"PeriodicalIF":0.0,"publicationDate":"2022-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72934177","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