2021 6th International Conference on Robotics and Automation Engineering (ICRAE)最新文献_第7页

Optimal Design of Magnetic Resistance of Permanent Magnet Linear Motor 永磁直线电机磁阻优化设计

2021 6th International Conference on Robotics and Automation Engineering (ICRAE) Pub Date : 2021-11-19 DOI: 10.1109/ICRAE53653.2021.9657794

Xueying Kong, Yin Hai Tao, Liu Hua Qing

引用次数: 1

Loose Coupled Initialization Method for Visual Inertial Navigation in Outdoor Low Altitude Flight Environment 室外低空飞行环境下视觉惯性导航的松耦合初始化方法

2021 6th International Conference on Robotics and Automation Engineering (ICRAE) Pub Date : 2021-11-19 DOI: 10.1109/ICRAE53653.2021.9657780

Yibin Wang, Chengwei Yang, Cheng Zhang

{"title":"Loose Coupled Initialization Method for Visual Inertial Navigation in Outdoor Low Altitude Flight Environment","authors":"Yibin Wang, Chengwei Yang, Cheng Zhang","doi":"10.1109/ICRAE53653.2021.9657780","DOIUrl":"https://doi.org/10.1109/ICRAE53653.2021.9657780","url":null,"abstract":"In the outdoor low altitude flight environment, the inertial navigation self-alignment initialization method is difficult to apply to the dynamic environment, since the non-autonomous alignment method is not accurate in the case with large misalignment angles, and the method of using visual-information-assisted inertial navigation system initialization relies on the horizon extraction algorithm or the advance deployment of artificial landmarks, which is not flexible enough. To solve the problems above, a loose coupled inertial navigation system initialization method is proposed aided by visual information. First, the feature points are segmented into sky end and ground end at the vision front end. And the camera poses in the camera coordinate system are calculated using the visual structure from motion. Then the camera poses and the re-integration of acceleration and angular velocity measurements are aligned to obtain the initialized poses. The performance of our algorithm is verified by performing a low altitude flight simulation in three cases with a semi-physical simulation system.","PeriodicalId":338398,"journal":{"name":"2021 6th International Conference on Robotics and Automation Engineering (ICRAE)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125423521","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

Pedestrian Avoidance with and Without Incoming Traffic by Using Deep Reinforcement Learning 基于深度强化学习的行人避撞

2021 6th International Conference on Robotics and Automation Engineering (ICRAE) Pub Date : 2021-11-19 DOI: 10.1109/ICRAE53653.2021.9657771

Dazhi Guan, Shu Xu, Qinjie Liu, Jinyan Ma

引用次数: 0

Classification of Anode Current Signals Based on 1D Convolutional Neural Networks 基于一维卷积神经网络的阳极电流信号分类

2021 6th International Conference on Robotics and Automation Engineering (ICRAE) Pub Date : 2021-11-19 DOI: 10.1109/ICRAE53653.2021.9657797

X. Chen, Shiwen Xie, Yong-Yu Xie, Xiaofang Chen

{"title":"Classification of Anode Current Signals Based on 1D Convolutional Neural Networks","authors":"X. Chen, Shiwen Xie, Yong-Yu Xie, Xiaofang Chen","doi":"10.1109/ICRAE53653.2021.9657797","DOIUrl":"https://doi.org/10.1109/ICRAE53653.2021.9657797","url":null,"abstract":"Intelligent and refined production becomes the development direction of the aluminium electrolysis industry. Anode current signals (ACS) can reflect the local conditions of electrolytic cells, timely and accurately classify the anode current signals, which will help to achieve regionalization and fine control of cells. Anode current signals are typical multivariable time series, so it is difficult to obtain its discriminant features based on traditional spectrum classification methods. Therefore, this paper presents a method to classify the anode current signals using one-dimensional convolutional neural networks (1D-CNN). In addition to the input layer and output layer, the proposed CNN model consists of 8 layers, including 3 convolution layers, 2 max-pooling layers, and 3 fully connected layers. The model can automatically extract the features from the original data, so as to realize the three types of anode current signals classification, namely, normal, anode effect (AE) and anode change (AC). The experimental results show that the classification accuracy reaches 87.6%, which verifies the effectiveness of the method.","PeriodicalId":338398,"journal":{"name":"2021 6th International Conference on Robotics and Automation Engineering (ICRAE)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125982577","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 Optimization of the Constant Flow Parallel Micropump Using RBF Neural Network 基于RBF神经网络的恒流量并联微泵优化

2021 6th International Conference on Robotics and Automation Engineering (ICRAE) Pub Date : 2021-09-17 DOI: 10.1109/ICRAE53653.2021.9657808

Chen-yang Ma, Boyuan Xu

引用次数: 0

Learning Friction Model for Tethered Capsule Robot 系留胶囊机器人的学习摩擦模型

2021 6th International Conference on Robotics and Automation Engineering (ICRAE) Pub Date : 2021-08-16 DOI: 10.1109/ICRAE53653.2021.9657825

Yi Wang, Yuchen He, Xutian Deng, Ziwei Lei, Yiting Chen, Miao Li

引用次数: 2