IET Cybersystems and Robotics最新文献_第10页

A Robust Seabed Terrain Contour Aid Navigation Method Facing the Smooth Terrain 面向平滑地形的鲁棒海底地形等高线辅助导航方法

IET Cybersystems and Robotics Pub Date : 2022-07-27 DOI: 10.1109/CYBER55403.2022.9907123

Wenjun Zhang, Dong Ma, Jingxuan Gao, Teng Ma, Yueyang Ben

{"title":"A Robust Seabed Terrain Contour Aid Navigation Method Facing the Smooth Terrain","authors":"Wenjun Zhang, Dong Ma, Jingxuan Gao, Teng Ma, Yueyang Ben","doi":"10.1109/CYBER55403.2022.9907123","DOIUrl":"https://doi.org/10.1109/CYBER55403.2022.9907123","url":null,"abstract":"The underwater terrain-aid navigation (TAN) method using a single-beam sonar has demonstrated its potential for autonomous underwater vehicles (AUVs) long-range accurate navigation without the aid of acoustics arrays and satellites. This paper proposed a robust terrain contour aid navigation(TCAN) method using a single-beam sonar in the smooth terrain areas, which considered the error characteristics of an inertial navigation system(INS), can realize long-range underwater navigation. As for the measurement error of a single-beam sonar and the influence of the smooth terrain areas, this paper proposed a measurement confidence interval calculation method, which can reduce the influence of the measurement error on the navigational result, improving the accuracy and robustness of the TCAN method. The influence of algorithm parameters on the result of the navigation method are researched by simulation experiments. The results show that the navigation method can realize long-range underwater navigation in the smooth terrain areas on making full use of an INS error characteristics, and has a good application prospect.","PeriodicalId":34110,"journal":{"name":"IET Cybersystems and Robotics","volume":"29 1","pages":"1166-1170"},"PeriodicalIF":0.0,"publicationDate":"2022-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75058972","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

Adaptive PID Trajectory Tracking Algorithm Using Q-Learning for Mobile Robots 基于q -学习的移动机器人自适应PID轨迹跟踪算法

IET Cybersystems and Robotics Pub Date : 2022-07-27 DOI: 10.1109/CYBER55403.2022.9907573

Xiaoliang Fan, Jin Sui, Naifeng He, Bi Zhang, Chunguang Bu, Junbo Yang, Lele Cui

引用次数: 0

A 3D Reconstruction Technology of Indoor Scene based on Image Sequence 基于图像序列的室内场景三维重建技术

IET Cybersystems and Robotics Pub Date : 2022-07-27 DOI: 10.1109/CYBER55403.2022.9907578

Songna Zhang, Tong Jia, Wenhao Li, Xiaojun Sun

{"title":"A 3D Reconstruction Technology of Indoor Scene based on Image Sequence","authors":"Songna Zhang, Tong Jia, Wenhao Li, Xiaojun Sun","doi":"10.1109/CYBER55403.2022.9907578","DOIUrl":"https://doi.org/10.1109/CYBER55403.2022.9907578","url":null,"abstract":"The 3D reconstruction technology of indoor scenes based on image sequences has always been the focus of research in computer vision. It can be widely used in medical diagnosis, unmanned driving, AR/VR, cultural relics restoration, and other fields. However, due to the complex information and cluttered features of indoor scenes, the existing feature matching algorithms and point cloud registration algorithms still have certain limitations in terms of computational efficiency and matching accuracy. Therefore, this paper firstly adopts a uniform extraction of ORB features method based on octree and a feature matching method based on colour and descriptor distance information and uses the RANSAC algorithm to eliminate mismatched points to obtain matching results with high accuracy. Secondly, this paper adopts a point cloud fine-registration method based on a double threshold constraint. Based on the point cloud normal vector angle threshold constraint, the search of the nearest neighbour point pair in the ICP algorithm is realized through the adaptive distance threshold constraint. Finally, experimental analysis is carried out in a real indoor scene to verify the effectiveness of the proposed algorithm in reconstruction efficiency and accuracy.","PeriodicalId":34110,"journal":{"name":"IET Cybersystems and Robotics","volume":"15 1","pages":"906-911"},"PeriodicalIF":0.0,"publicationDate":"2022-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88273516","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