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检索条件"机构=China Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex System"

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Distributed auction-based coverage motion planning for multiple cooperative robots 8

Distributed auction-based coverage motion planning for multi...

引用

8th International Symposium on Computational Intelligence and Industrial Applications and 12th china-Japan International Workshop on Information Technology and control Applications, ISCIIA and ITCA 2018

作者： Gao, Guanqiang Xin, Bin School of Automation Beijing Institute of Technology Beijing100081 China Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing100081 China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology Beijing100081 China

The multi-robot coverage motion planning (MCMP) problem refers to a typical task where every point of the target area must be covered by robots at least once. This paper designs a novel efficient offline algorithm to solve the MCMP problem, which is extended from the spiral-spanning tree coverage algorithm. A distributed auction mechanism is designed for the cooperative task allocation in the proposed algorithm. In this mechanism, each robot will be an auctioneer which chooses a suitable cell as an auction item form its neighbor vertexes and determines a winner robot. According to the connectedness and the balance of each robot’s graph, bidders will submit proper biddings to the auctioneer. After the auction processes, acceptable coverage trajectories can be planned rapidly. Computational experiments validate the effectiveness of the proposed MCMP algorithm. Meanwhile, the proposed algorithm is compared with the state-of-the-art algorithm named DARP. The comparative results show that the auction-based MCMP algorithm has apparent advantages in the running time and the makespans for large crowded configuration spaces. © ISCIIA and ITCA 2018 - 8th International Symposium on Computational Intelligence and Industrial Applications and 12th china-Japan International Workshop on Information Technology and control Application.

关键词： Multipurpose robots

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Optimal Consensus control of Unknown Nonlinear Multi-Agent systems Using Adaptive Dynamic Programming via MRAC

Optimal Consensus Control of Unknown Nonlinear Multi-Agent S...

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第37届中国控制会议

作者： Hao Fu Xin Chen Wei Wang Jinbin Li Yaodong Zhang School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems State Grid Hubei Electric Power Research Institute

This paper introduces an optimal consensus control scheme for nonlinear multi-agent systems with completely unknown dynamics. In general, it is difficult to solve the coupled Hamilton-Jacobi-Bellman（HJB） equations, which the optimal consensus control relies on in multi-agent systems, especially unknown nonlinear systems. For the purpose of solving the problem, we propose an optimal consensus control approach based on the model reference adaptive control（MRAC） and adaptive dynamic programming（ADP）. Using the structure of the diagonal recurrent neural network, the identifier and controller are devised to achieve MRAC for every plant of the unknown nonlinear systems, i.e. the reference model serves as a dynamic model of each individual agent. Then, according to reference models of distributed agents, an adaptive dynamic programming（ADP）is introduced to approximate the solution of the coupled HJB equations.

关键词： Multi-agent systems optimal consensus model reference adaptive control adaptive dynamic programming

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Hexagon-Shaped Screw Recognition and Positioning system Based on Binocular Vision

Hexagon-Shaped Screw Recognition and Positioning System Base...

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第37届中国控制会议

作者： Fang Wang Xin Chen Chang Tan Jinbin Li Yaodong Zhang School of Automation China University of Geoscience Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems State Grid Hubei Electric Power Research Institute

Traditional artificial live work asks for a high standard with operator’s mental state and technical level in high-voltage and strong-magnetic environment,which not only reduces the efficiency of modern production and living but also causes potential threats to the safety of ***,developing robot live work has great significance for both academic research and industrial applications.A key problem of the live work for an industrial robot is recognition and positioning of the hexagonshaped screw in substation with high *** solve this problem,this article designs a hexagon-shaped screw recognition and positioning system based on binocular vision in transformer substation.A method for stereo matching including 3 steps is proposed:edge noise of the target is reduced by median filter at the beginning;then the target can be obtained through the image segmentation method of adjusting threshold;further moments are used to express the centers of hexagons in 2 images which work as stereo matching *** depth information can be obtained due to visual geometric relationships based on stereo model and camera calibration parameters based on camera *** experiment results show that the binocular system designed in this article can accurately identify the target with good stability,and the measurement error is controlled in 4%.

关键词： Binocular stereo vision Image segmentation Stereo matching Depth information

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Deep Forest-Based Classification of Hyperspectral Images

Deep Forest-Based Classification of Hyperspectral Images

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第37届中国控制会议

作者： Xu Yin Ruilin Wang Xiaobo Liu Yaoming Cai School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Computer Science China University of Geosciences

The classification of hyperspectral images(HSIs) is a hot topic in the field of remote sensing technology. In recent years, convolutional neural network(CNN) has achieved great success for HSI classification. However, CNN has to do a great effort in parameters tuning which is time-consuming. Furthermore, a large number of samples are required to train CNN,nevertheless, it is expensive to obtain enough training samples from HSIs. In this paper, we propose a novel classification approach based on deep forest. To reduce the dimension of hyperspectral data, principal component analysis(PCA) is performed during the pre-processing. In contrast to the CNN, our method has fewer hyper-parameters and faster training speed. To the best of our knowledge, this is among the first deep forest-based hyperspectral spectral information classification. Extensive experiments are conducted on two real-world HSI datasets to show the proposed method is significantly superior to the state-ofthe-art methods.

关键词： Deep Forest Hyperspectral Image（HSI） Deep Neural Network（DNN） Principal Component Analysis（PCA）

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Calculation Method of Load Distribution for Two-crane Cooperative Lift

Calculation Method of Load Distribution for Two-crane Cooper...

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第37届中国控制会议

作者： Xiaoling Shen Jianqi An Takao Terano School of Automation China University of Geosciences Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Engineering Tokyo University of Technology

Cooperative lifting by two cranes is widely used in the current construction site. Since the influence of changeable load in the cooperative lifting process, it is essential to take the load distribution into consideration while planning the lifting path to ensure the safety of the whole lifting process. However, most researches did not consider the reasonable actual load distribution or just used a certain load in the lift process. This paper analyzes the relationship between crane operations and the pose of the lifted object. Then based on this relationship, this paper gives the method calculating the distribution of actual load over each crane that the planning approach is based. This paper also gives calculation methods and constraints for the skew angle of each lifting rope. The results of simulation show the effectiveness of this calculation method.

关键词： Cooperative Lifting Actual Load Calculation

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Empirical Research on a Fuzzy Model of Music Emotion Classification based on Pleasure-Arousal Model

Empirical Research on a Fuzzy Model of Music Emotion Classif...

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第37届中国控制会议

作者： Siqi Huang Li Zhou Zhentao Liu Shan Ni Jingxian He School of Arts and Communication China University of Geosciences School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

Affective computing plays a key role in music artificial intelligence, in which a music emotion classification model is indispensable. Both discrete classification model and continuous dimensional model are commonly used for music emotion classification. However, these models are not designed in views of composers, which is insufficient for the perception of music emotion. In this paper, a fuzzy music emotion classification model is proposed by extracting the music expression marks considering the composers emotion. Experiments on subjective evaluation in the feeling of pleasure and arousal according to the change of three selected features（i.e., tempo, register, and dynamic） are conducted by listeners from different subjects. The experimental results show that the correlation between the proposal fuzzy model and the the results from questionnaires reaches 80% on average, which demonstrate the validity of the proposed fuzzy music emotion classification model. The proposal could be applied to music emotion generation conveyed from either composers or players, and to emotion recognition of music as for audiences.

关键词： Music emotion Classification Affective computing Pleasure-Arousal model Fuzzy logic

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Simulation and Experimental Study on Piezoelectric Geophone Core Based on PMN-PT

Simulation and Experimental Study on Piezoelectric Geophone ...

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第37届中国控制会议

作者： JunLei Song YanXia Huang ZhiHeng Yang MeiJuan Chen Yong Yang WenQin Mo KaiFeng Dong Fang Jin School of Automation China University of Geosciences Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Mathmatics and Physics China University of Geosciences

Piezoelectric geophones are vibration detectors that convert vibration acceleration signals into electrical signals. High performance piezoelectric materials can improve the sensitivity of piezoelectric geophone and meet the need of high-resolution seismic data acquisition. The comprehensive performance of relaxor piezoelectric single crystal PMN-PT is more superior than PZT, and it is potential to be applied to high sensitivity and small volume geophones. In this paper, the central compressed geophone core model based on PMN-PT was established and theoretically analyzed. Then, a multiphysics simulation model was set up in COMSOL for simulation calculation. Finally, experimental verification was carried out. The results show that using PMN-PT in geophone core design can improve the sensitivity of the model by more than 120% compared with the traditional PZT material. The PMN-PT has the potential to be applied to high sensitivity and small volume geophones.

关键词： PMN-PT PZT Piezoelectric geophone Seismic exploration Accelerometer

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An intelligent Selection Strategy for Burden Distribution Based on Classification of Blast Furnace Condition

An intelligent Selection Strategy for Burden Distribution Ba...

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第37届中国控制会议

作者： Kexin Zhang Min Wu Jianqi An School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Engineering Tokyo University of Technology

Burden distribution plays an important role in the optimization of energy-consuming index of a blast furnace(BF).However, due to the low transparency of this process, operators adjust the burden distribution according to their own experiences,which makes it difficult to keep optimal production indexes for a BF. In this paper, an intelligent selection strategy of burden distribution is devised according to the energy-consuming index, where carbon-monoxide utilization rate(CMUR) is taken as the energy-consuming index. First, state variables are chosen based on the calculated Pearson correlation coefficient among each state variable. Then, the chosen state variables are divided into five clusters by k-means clustering algorithm. Finally, the values of CMUR in each condition cluster with different burden distribution are counted. According to the statistics, the burden distribution with the highest CMUR is judged to be the optimal one. Simulation results based on industrial data show that the proposed strategy selects the optimal burden distribution and provides a reasonable guide for the operators.

关键词： Burden distribution blast furnace k-means carbon-monoxide utilization rate

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Design of High-Gain-Observer-Based State Feedback controller for Positive Time-Delay system

Design of High-Gain-Observer-Based State Feedback Controller...

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第37届中国控制会议

作者： Mingyuan Xie Jinhua She Zhentao Liu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Engineering Tokyo University of Technology

This paper investigates the stabinzability of positive time-deray system,The nonnegative constraint makes the design of a control law different from a general ***,a method to calculate the L gain of a positive time-delay system is ***,the design of a state feedback controller and a high-gain observer is presented by taking full advantage of the characteristics of the positiveness of the ***,the observer and feedback controller are combined to stabilize the system and improve dynamic performance.A numerical example illustrates the provided method is effective.

关键词： Positive system Delay L1 gain Asymptotically stable High-Gain Observer Observer-based feedback controller

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Gesture Intention Understanding Based on Depth and RGB Data

Gesture Intention Understanding Based on Depth and RGB Data

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第37届中国控制会议

作者： Yu Feng Luefeng Chen Wanjuan Su Kaoru Hirota School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Engineering Tokyo Institute of Technology

Aiming at the problem that the process of gesture recognition based on color image is greatly affected by environmental factors such as lighting, a gesture intent understanding method based on the fusion of Red-Green-Blue(RGB) data and depth data is proposed. Firstly, the gesture feature extraction based on the Speeded Up Robust Feature(SURF) method after foreground segmentation are used to get gesture information. Then, we apply Backpropagation(BP) neural network to classify and recognize gestures. The final recognition results are obtained through data fusion from recognition results based on both RGB images and depth images. We evaluated the effectiveness of the proposed method through Cha Learn Gesture Database.

关键词： Gesture Intention Understanding Depth Data RGB Data

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