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

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Rotor Imbalance Recognition of Electric Spindle Based on Wavelet Packet and Random Forest 基于小波包和随机森林的电主轴转子不平衡识别
2021 6th International Conference on Robotics and Automation Engineering (ICRAE) Pub Date : 2021-11-19 DOI: 10.1109/ICRAE53653.2021.9657827
Jingyao Sun, Weiguang Li, Chunlin Luo, Qiulin Yu
{"title":"Rotor Imbalance Recognition of Electric Spindle Based on Wavelet Packet and Random Forest","authors":"Jingyao Sun, Weiguang Li, Chunlin Luo, Qiulin Yu","doi":"10.1109/ICRAE53653.2021.9657827","DOIUrl":"https://doi.org/10.1109/ICRAE53653.2021.9657827","url":null,"abstract":"Aiming at the problem that it is difficult to identify and classify the rotor imbalance of the electric spindle, a dynamic balance test bench of the electric spindle is built, and the rotor imbalance experiment at different speeds is performed, and vibration signals are collected. 1. The wavelet packet method is adopted to denoise the vibration signal. 2. The four characteristic parameters of amplitude, variance, standard deviation, and mean square error are selected by tSEN cluster analysis to combine into the rotor imbalance state evaluation model. 3. The combined evaluation model is input into the chosen random forest for training and identification. The results show that the rotor imbalance evaluation model established in this paper can accurately and effectively identify different types of rotor imbalance. It is better than time-domain feature model, frequency-domain feature model and wavelet packet feature model in terms of time-consuming and accuracy.","PeriodicalId":338398,"journal":{"name":"2021 6th International Conference on Robotics and Automation Engineering (ICRAE)","volume":"203 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":"122664900","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
Time-constrained Multiple Unmanned Surface Vehicles Cooperation for Sea Surface Oil Pollution Cleanup 基于时间约束的多水面无人船合作海面油污清理
2021 6th International Conference on Robotics and Automation Engineering (ICRAE) Pub Date : 2021-11-19 DOI: 10.1109/ICRAE53653.2021.9657790
Xu Zhou, Yuan Ge, Wenzhang Li, Gang Ye
{"title":"Time-constrained Multiple Unmanned Surface Vehicles Cooperation for Sea Surface Oil Pollution Cleanup","authors":"Xu Zhou, Yuan Ge, Wenzhang Li, Gang Ye","doi":"10.1109/ICRAE53653.2021.9657790","DOIUrl":"https://doi.org/10.1109/ICRAE53653.2021.9657790","url":null,"abstract":"In view of the demand of multiple unmanned surface vehicles cooperative sea surface oil pollution cleaning under time constraints, a method to find the optimal number of unmanned surface vehicles is proposed. Firstly, perform image processing on the oil pollution image, and the path planning of the unmanned surface vehicle is carried out by using the self-defined right-angle path planning method. Secondly, the oil pollution area is calculated by simulating the change of oil pollution image after cleaning in a fixed period. Then, it iteratively calculates the total time length of oil pollution cleaning by different number of unmanned surface vehicles, and obtains the optimal number of unmanned surface vehicles by comparing the time constraint conditions. Finally, the effectiveness of the method is verified by an example, and the optimal number of unmanned surface vehicles can be obtained.","PeriodicalId":338398,"journal":{"name":"2021 6th International Conference on Robotics and Automation Engineering (ICRAE)","volume":"19 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":"122914551","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
Distributed Bargaining Method of a Multi-integrated Energy System Based on Nash Theory 基于纳什理论的多集成能源系统分布式议价方法
2021 6th International Conference on Robotics and Automation Engineering (ICRAE) Pub Date : 2021-11-19 DOI: 10.1109/ICRAE53653.2021.9657788
Jian Zhang, Youyi Chang, Miao Liu, Ye Zhang, Nan Liu, Qinglai Wei
{"title":"Distributed Bargaining Method of a Multi-integrated Energy System Based on Nash Theory","authors":"Jian Zhang, Youyi Chang, Miao Liu, Ye Zhang, Nan Liu, Qinglai Wei","doi":"10.1109/ICRAE53653.2021.9657788","DOIUrl":"https://doi.org/10.1109/ICRAE53653.2021.9657788","url":null,"abstract":"Aiming at the scenario of the interconnection of multiple heat and electricity integrated energy systems (HE-IES), this paper proposes a multi HE-IES power trading bargaining method based on Nash bargaining theory. Firstly, build the framework of multi HE-IES, and establish the power interaction model between multiple HE-IES and between HE-IES and distribution network. Secondly, taking the optimal operation cost of the direct transaction between integrated energy system operators (IESO) and Distribution network operator (DNO) as the breaking point of Nash bargaining, a cooperative game model of multi-IES bargaining is constructed. The nonconvex problem is transformed into two convex subproblems and the ADMM is used for the distributed solution. Finally, the effectiveness of the proposed method to reduce the operation cost of the multi-integrated energy system is verified by case analysis.","PeriodicalId":338398,"journal":{"name":"2021 6th International Conference on Robotics and Automation Engineering (ICRAE)","volume":"1 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":"128969772","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 Identifying Campus Garbage Application Based on Optimized Convolutional Neural Networks 基于优化卷积神经网络的校园垃圾识别应用
2021 6th International Conference on Robotics and Automation Engineering (ICRAE) Pub Date : 2021-11-19 DOI: 10.1109/ICRAE53653.2021.9657774
Hongyu Li, Chuanfang Xu, Qilong Wu, Junxing Guo, Xin-hua Zhu, Yan Su
{"title":"An Identifying Campus Garbage Application Based on Optimized Convolutional Neural Networks","authors":"Hongyu Li, Chuanfang Xu, Qilong Wu, Junxing Guo, Xin-hua Zhu, Yan Su","doi":"10.1109/ICRAE53653.2021.9657774","DOIUrl":"https://doi.org/10.1109/ICRAE53653.2021.9657774","url":null,"abstract":"This paper proposes to present an effective and practical mobile garbage identification application named GCNet (Garbage Classification Network), which is capable of classifying different types of campus garbage and estimating the results using convolutional neural networks. The GCNet consists of three major parts. First, a data enhancement based on image stitching is designed to enrich data sets and enhance network robustness. Then we propose a spatial attention for reducing model structure and detection accuracy enhancement. Deformable convolution, the third component, is used to solve the problem of loss of sampling details or burr of normal convolution. Furthermore, we deployed the GCNet model to the mobile terminal and designed a mobile terminal intelligent campus garbage identification application. The experimental results show that the proposed GCNet performs well on campus garbage detection with 89.8% mean average precision (mAP), which outperforms the state-of-the-art methods. The running time on our application could achieve 0.083s per image, meeting the real-time detection. The proposed method is effective and applicable for accurate and real-time campus garbage detection.","PeriodicalId":338398,"journal":{"name":"2021 6th International Conference on Robotics and Automation Engineering (ICRAE)","volume":"8 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":"132127494","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 Parameter Identification of Maritime Autonomous Surface Ships with Exponential Convergence 基于指数收敛的海上自主水面舰艇自适应参数辨识
2021 6th International Conference on Robotics and Automation Engineering (ICRAE) Pub Date : 2021-11-19 DOI: 10.1109/ICRAE53653.2021.9657778
Jiawang Yue, Zhouhua Peng, Dan Wang
{"title":"Adaptive Parameter Identification of Maritime Autonomous Surface Ships with Exponential Convergence","authors":"Jiawang Yue, Zhouhua Peng, Dan Wang","doi":"10.1109/ICRAE53653.2021.9657778","DOIUrl":"https://doi.org/10.1109/ICRAE53653.2021.9657778","url":null,"abstract":"This paper is concerned with the parameter identification of maritime autonomous surface ships (MASS) with fully unknown coefficients. An adaptive parameter identification method is proposed for an MASS to identify its model without the condition of the persistence of excitation (PE). Specifically, a composite adaptive update law is developed based on an integral filtering regression equation. By using this method, only the initial excitation (IE) condition is needed to assure the estimation convergence. A salient feature of the proposed method is that the acceleration information is totally not needed and only measured linear velocities and yaw rate are used for identification. Then, the stability of the online parameter identification method is proved by Lyapunov stability analysis, and the estimation errors exponentially converge to zero. Simulation results demonstrate the effectiveness of the proposed adaptive parameter identification method for the MASS.","PeriodicalId":338398,"journal":{"name":"2021 6th International Conference on Robotics and Automation Engineering (ICRAE)","volume":"26 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":"133132773","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
Bayesian Network Structure Learning Based on Small Sample Data 基于小样本数据的贝叶斯网络结构学习
2021 6th International Conference on Robotics and Automation Engineering (ICRAE) Pub Date : 2021-11-19 DOI: 10.1109/ICRAE53653.2021.9657789
C. Xiaoyu, Liu Baoning
{"title":"Bayesian Network Structure Learning Based on Small Sample Data","authors":"C. Xiaoyu, Liu Baoning","doi":"10.1109/ICRAE53653.2021.9657789","DOIUrl":"https://doi.org/10.1109/ICRAE53653.2021.9657789","url":null,"abstract":"Aiming at the problem that it is difficult to learn the optimal solution of Bayesian structure under the condition of small sample data, this paper proposes a Bayesian structure learning algorithm under small data set. Firstly, an improved Bootstrap sampling is proposed to expand the small data, and the extended sample is modified through the maximum weight spanning tree. Secondly, the standard particle swarm optimization (PSO) algorithm is improved, and the calculation method in the update formula is redefined to adapt to Bayesian network structure learning. Finally, the simulation verification of a calculation example proves the effectiveness of the improved algorithm for Bayesian network structure learning.","PeriodicalId":338398,"journal":{"name":"2021 6th International Conference on Robotics and Automation Engineering (ICRAE)","volume":"36 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":"133451969","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 Control for Uncertain Nonlinear Systems with Measurement Uncertainty 测量不确定非线性不确定系统的自适应控制
2021 6th International Conference on Robotics and Automation Engineering (ICRAE) Pub Date : 2021-11-19 DOI: 10.1109/ICRAE53653.2021.9657768
Song Jiang, Yan Lin, Chen Sun
{"title":"Adaptive Control for Uncertain Nonlinear Systems with Measurement Uncertainty","authors":"Song Jiang, Yan Lin, Chen Sun","doi":"10.1109/ICRAE53653.2021.9657768","DOIUrl":"https://doi.org/10.1109/ICRAE53653.2021.9657768","url":null,"abstract":"In this study, the global adaptive control is considered for a class of uncertain nonlinear systems with measurement uncertainty, where the bounds of the measurement uncertainty and system uncertainty are not a priori. A dynamic gain-based control strategy and the feedback domination technique are applied for the controller design, where a set of new dynamic gains is proposed to deal with uncertainties with unknown bounds. It has also been proven that global stability can be achieved under the designed controller. Finally, a simulation example is presented to illustrate the effectiveness of our proposed method.","PeriodicalId":338398,"journal":{"name":"2021 6th International Conference on Robotics and Automation Engineering (ICRAE)","volume":"30 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":"129312940","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
A New Improved Echo State Network with Multiple Output Layers for Time Series Prediction 一种改进的多输出层回声状态网络用于时间序列预测
2021 6th International Conference on Robotics and Automation Engineering (ICRAE) Pub Date : 2021-11-19 DOI: 10.1109/ICRAE53653.2021.9657812
Yanning Shao, Xianshuang Yao, Gong Wang, Shengxian Cao
{"title":"A New Improved Echo State Network with Multiple Output Layers for Time Series Prediction","authors":"Yanning Shao, Xianshuang Yao, Gong Wang, Shengxian Cao","doi":"10.1109/ICRAE53653.2021.9657812","DOIUrl":"https://doi.org/10.1109/ICRAE53653.2021.9657812","url":null,"abstract":"Through investigating the traditional echo state network, their output structure has only one output layer using the same output weight learning method, such that the networked prediction results is not always reliable. Therefore, a new echo state network with multiple output layers (MOL-ESN) in parallel configuration is proposed for time series prediction in this paper. For the output structure of MOL-ESN, multiple output layers with different output weight learning methods are built. Considering the multiple output layers are introduced, the computing burden of training the MOL-ESN will be also increased, and thus, on the premise of ensuring the stable network output, the prediction performance of the MOL-ESN need to be improved. Finally, the effectiveness of the proposed network is verified by a simulation example.","PeriodicalId":338398,"journal":{"name":"2021 6th International Conference on Robotics and Automation Engineering (ICRAE)","volume":"56 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":"132362331","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
An Switchable Multi-resolution Architecture of Cyber-Physical Manufacturing Systems (CPMS) for Industrial Robots Collaboration 面向工业机器人协作的信息物理制造系统(CPMS)可切换多分辨率体系结构
2021 6th International Conference on Robotics and Automation Engineering (ICRAE) Pub Date : 2021-11-19 DOI: 10.1109/ICRAE53653.2021.9657781
Wenzheng Liu, Chun Zhao, Wenjia Zhang, Yue Liu, Heming Zhang
{"title":"An Switchable Multi-resolution Architecture of Cyber-Physical Manufacturing Systems (CPMS) for Industrial Robots Collaboration","authors":"Wenzheng Liu, Chun Zhao, Wenjia Zhang, Yue Liu, Heming Zhang","doi":"10.1109/ICRAE53653.2021.9657781","DOIUrl":"https://doi.org/10.1109/ICRAE53653.2021.9657781","url":null,"abstract":"For complex manufacturing systems, fast, accurate, and reliable modeling and simulation of the real world, as well as the interaction from the simulation world to the real world, is required. The development of Cyber-Physical Systems (CPS) and Internet of Things (IoT) enable real-world manufacturing systems and their cyber world to form a manufacturing-oriented Cyber-Physical Systems - Cyber-Physical Manufacturing Systems (CPMS). However, the low performance of the edge, and the heavy storage and computing burden of the cloud, cannot meet the fast and accurate requirements of CPMS. To address these issues, this paper proposes an cloud-edge collaboration architecture of Cyber-Physical Manufacturing Systems intended for industrial robots collaboration. In the architecture, the key planning and decision are placed at a central computing station and the trivial calculation tasks are placed at the information shell of the manufacturing equipment. Specifically, robotic arms, AGVs and other manufacturing nodes are designed to store and perceive the environment and self-state, run with basic kinematics and kinetics. Reconfigurable computing nodes based on FPGA performs trivial logical calculation tasks. The manufacturing could is designed to plan and control all holonic nodes based on multi-agent deep reinforcement learning. The collaboration between robotic arm and AGV is studied as a case. The solution based on the proposed framework is given for the issue. The feasibility of the framework is verified by simulation and derivation.","PeriodicalId":338398,"journal":{"name":"2021 6th International Conference on Robotics and Automation Engineering (ICRAE)","volume":"31 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":"123885352","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
Deep Reinforcement Learning for Flocking Control of UAVs in Complex Environments 复杂环境下无人机集群控制的深度强化学习
2021 6th International Conference on Robotics and Automation Engineering (ICRAE) Pub Date : 2021-11-19 DOI: 10.1109/ICRAE53653.2021.9657767
Mahsoo Salimi, Philippe Pasquier
{"title":"Deep Reinforcement Learning for Flocking Control of UAVs in Complex Environments","authors":"Mahsoo Salimi, Philippe Pasquier","doi":"10.1109/ICRAE53653.2021.9657767","DOIUrl":"https://doi.org/10.1109/ICRAE53653.2021.9657767","url":null,"abstract":"Flocking formation of unmanned aerial vehicles (UAVs) is an open challenge due to kinematics complexity and uncertainties in complex environments. In this paper, the UAV flocking control problem is formulated as a partially observable Markov decision process (POMDP) and solved by deep reinforcing learning. In particular, we consider a leader-follower configuration, where consensus among all UAVs is used to train a shared control policy, and each UAV performs actions based on the local information it collects. In addition, to avoid collision among UAVs and guarantee flocking and navigation, a reward function is added with the global flocking maintenance, mutual reward, and a collision penalty. We adapt deep deterministic policy gradient (DDPG) with centralized training and decentralized execution to obtain the flocking control policy using actor-critic networks and a global state space matrix. The simulation results demonstrate that the trained optimal policy converges to flocking formation without parameter tuning and has good generalization ability for different UAVs.","PeriodicalId":338398,"journal":{"name":"2021 6th International Conference on Robotics and Automation Engineering (ICRAE)","volume":"167 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120882992","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: 4
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