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IEEE/CAA Journal of Automatica Sinica 2016年第4期3卷 345-348页

作者： Fei-Yue Wang Xiao Wang Lingxi Li Li Li IEEE State Key Laboratory of Management and Control for Complex Systems (SKL-MCCS) Institute of AutomationChinese Academy of Sciences (CASIA) Research Center of Computational Experiments and Parallel Systems the National University of Defense Technology Qingdao Academy of Intelligent Industries (QAII) QingdaoShandongChinaand SKL-MCCSCASIA Department of Electrical and Computer Engineering Purdue School of Engineering and TechnologyIndiana University-Purdue University Indianapolis Department of Automation Tsinghua University Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies

The origin of artificial intelligence is investigated, based on which the concepts of hybrid intelligence and parallel intelligence are presented. The paradigm shift in Intelligence indicates the ''new normal'' of cyber-social-physical systems (CPSS), in which the system behaviors are guided by Merton's Laws. Thus, the ACP-based parallel intelligence consisting of Artificial societies, Computational experiments and Parallel execution are introduced to bridge the big modeling gap in CPSS. © 2014 Chinese Association of Automation.

关键词： control systems Electrical engineering

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An erlang-based simulation approach of Artificial Transportation systems

An erlang-based simulation approach of Artificial Transporta...

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International Conference on intelligent Transportation

作者： Songhang Chen Fenghua Zhu Fei-Yue Wang The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Qingdao Academy of Intelligent Industries Qingdao Shandong China

ISBN: (纸本)9781509018901

Artificial Transportation systems (ATS) can support a variety of computational experiments for different purposes, and have become important tools for transportation research. However, when the road network modeled in ATS is large or there are a lot of vehicles, ATS will run very slow or even cannot be started due to the memory limit of a standalone computer. With the acceleration of urbanization process and rapid increase of car ownership, it is necessary to find a high-performance computing method for running large-scale ATS. Therefore, the paper presents an Erlang-based approach to realize concurrent and distributed computing of ATS. To verify the feasibility, a prototype is built, and a performance test is conducted. The results show the method can achieve the efficiency utilization of computing resources.

关键词： Roads Computational modeling Object oriented modeling Computers Sociology Statistics

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A Graph Matching Based key Point Correspondence Method for Lunar Surface Images

A Graph Matching Based Key Point Correspondence Method for L...

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World Congress on intelligent control and Automation

作者： Yuren Zhang Xu Yang Hong Qiao Zhiyong Liu Chuankai Liu Baofeng Wang State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing Aerospace Flight Control Center

ISBN: (纸本)9781467384155

key point correspondence plays an important role in lunar surface image processing. Since lunar surface images often contain obvious illumination changes, noisy points and repetitive patterns, traditional appearance based algorithms may fail when local appearance descriptors become less distinctive. In this paper, we introduce a graph matching based algorithm to tackle this problem. First, by incorporating structural information, key point sets in lunar surface images are represented by graphs. Then key point correspondence is formulated as a specific graph matching problem which aims to find a specified number of best assignments, and effectively approximately solved. Finally, an outlier assignment elimination method is proposed based on the affine invariance assumption. Simulations on both benchmark datasets and lunar surface images witness the effectiveness of the proposed method.

关键词： Graph matching lunar surface images Correspondence

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Real-time Depth-based Tracking Using A Binocular Camera

Real-time Depth-based Tracking Using A Binocular Camera

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World Congress on intelligent control and Automation

作者： Leijie Zhang Zhiqiang Cao Xiangrui Meng Chao Zhou Shuo Wang State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9781467384155

Depth map provides rich information and it can be utilized in object tracking to handle some challenging problems in conventional RGB tracking such as occlusions and model drift. In this paper, we present a tracker that provides an effective real-time target tracking method based on a binocular camera. The proposed tracker is an extension of the popular KCF algorithm that leverages a circulant structure of tracking-by-detection with kernels for tracking. On this basis, we design a simple yet effective method to detect occlusions and recover tracking using noisy depth map obtained from a binocular camera. Firstly, one needs to identify exact and reasonable peak number of the target region's depth histogram and apply GMM model to evaluate the depth distribution. The occlusion is then detected based on the depth evaluation and the maximum response from KCF. When the occlusion happens, it is segmented to build the corresponding search region for recovery. The experimental results demonstrate the effectiveness of the proposed method and the superiority to the KCF tracker.

关键词： Target tracking Kernel Cameras Matrices Algorithm design and analysis Histograms Correlation

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Convolutional Fitted Q Iteration For Vision-Based control Problems

Convolutional Fitted Q Iteration For Vision-Based Control Pr...

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International Joint Conference on Neural Networks

作者： Dongbin Zhao Yuanheng Zhu Le Lv Yaran Chen Qichao Zhang State Key Laboratory of Management and Control for Complex Systems Institution of Automation Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9781509006212

In this paper a deep reinforcement learning (DRL) method is proposed to solve the control problem which takes raw image pixels as input states. A convolutional neural network (CNN) is used to approximate Q functions, termed as Q-CNN. A pretrained network, which is the result of a classification challenge on a vast set of natural images, initializes the parameters of Q-CNN. Such initialization assigns Q-CNN with the features of image representation, so it is more concentrated on the control tasks. The weights are tuned under the scheme of fitted Q iteration (FQI), which is an offline reinforcement learning method with the stable convergence property. To demonstrate the performance, a modified Food-Poison problem is simulated. The agent determines its movements based on its forward view. In the end the algorithm successfully learns a satisfied policy which has better performance than the results of previous researches.

关键词： Feature extraction Artificial neural networks Learning (artificial intelligence) Biological neural networks Image representation Convergence Aerospace electronics

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Towards an Esox lucius inspired multimodal robotic fish

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Science China(Information Sciences) 2015年第5期58卷 182-194页

作者： WU ZhengXing YU JunZhi SU ZongShuai TAN Min LI ZhenLong State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences College of Electronic Information and Control Engineering Beijing University of Technology

In this paper, we further explore multimodal locomotion via an updated robotic fish model based on Esox lucius. Besides the improved actuation properties like higher torque servomotors and powerful electronics,the robotic fish has some innovative mechanical design to pursue diverse swimming modes and superior performance. Specifically, we introduced a ±50° yawing head joint that functions as the neck for enhancing turning ability. A pair of pectoral mechanisms with two DOFs per fin is constructed to achieve 3-D swimming and to enrich multiple pectoral motions. At the control level, an improved central pattern generator(CPG) model allowing for free adjustment of the phase relationship among outputs is employed to produce rhythmic signals of multimodal swimming. Extensive experiments were carried out to examine how characteristic parameters in CPGs including amplitude, frequency, and phase lag affect the swimming performance. As a result, the robotic fish successfully performed various locomotion actions such as forward swimming, backward swimming, turning, diving, surfacing, as well as three pectoral motions in the form of pitching, heaving, and *** found that small phase lag between oscillating joints which means large propulsive body wave length and undulation width could lead to a faster swimming in body and/or caudal fin(BCF) locomotion.

关键词： bio-inspired robot robotic fish CPG multimodal swimming motion control

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A Robot Pose Estimation Approach Based on key Feature Registration

A Robot Pose Estimation Approach Based on Key Feature Regist...

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World Congress on intelligent control and Automation

作者： Wenbo Yuan Tianzhu Wang Zhiqiang Cao Min Tan State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9781467384155

Pose estimation is a fundamental issue in the research of mobile robot. A robot pose estimation approach based on key feature registration is proposed in this paper. The key features in the environment are extracted firstly. When dealing with many non-corresponding points, registration has a slow convergence rate, and gains poor performance. To solve these problems, a key Feature Based Accelerated Iterative Closest Point (KF-AICP) algorithm is presented for key feature registration. Then the current robot pose can be estimated based on the result of registration. The dataset is established using 2D laser radar in indoor scene. The experimental results show that the proposed approach achieves good accuracy and real-time in pose estimation of mobile robot. The validity of proposed approach is verified.

关键词： Iterative closest point algorithm Pose estimation Laser radar Feature extraction Mobile robots Data models

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A Novel Model Analysis Method and Dynamic Modelling for Hybrid Structure Flexible Manipulator

A Novel Model Analysis Method and Dynamic Modelling for Hybr...

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IEEE International Conference on Mechatronics and Automation

作者： Teng Long En Li Guodong Yang Zize Liang The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9781509023974

Tip deflection and vibration have a large impact on the manipulator operation safety, and hybrid structure flexible manipulator has a larger workspace and more flexibility. In this paper, a new modeling scheme for a hybrid structure flexible manipulator is proposed. In the proposed scheme, the joint variables of hybrid structure flexible manipulator are divided in rigid joint angle and small joint angle deviations. The prismatic joint is divided into three segments according to the structure features of flexible manipulator. The three segments are the foundation segment, overlap segment and extension segment. Each segment is modeled by the theory of the Euler-Bernoulli beam, and the elastic deflection is described by assumed mode method, the kinetic energy and potential energy of every segment is analyzed respectively. Base on the previous researches of modeling for multi-link flexible manipulator, the dynamic equations of the manipulator are obtained by using the Lagrangian approach. In order to verify the correctness of model and analyze the characteristics of model, the comparative simulation experiments are designed. Tip vibration, angle deviation, vibration frequency and amplitude are observed, and the characteristics of the model are analyzed according to the experiment results.

关键词： model analysis dynamic modelling hybrid structure flexible manipulator

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Motion detection based on improved Sobel and ViBe algorithm

Motion detection based on improved Sobel and ViBe algorithm

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Chinese control Conference (CCC)

作者： Yujia Zhang Xiaoguang Zhao Min Tan State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing Beijing CN

Motion detection plays a crucial role in most video based applications. A particular background subtraction technique called ViBe (Visual Background Extractor) is commonly used to obtain foreground objects from the background due to its high detection rate and low computational complexity. However, the performance is not very satisfying. Therefore, this paper presents an improved ViBe algorithm to increase the accuracy and robustness of motion detection. Specifically, a foreground feature map is created by optimizing the result of ViBe algorithm. Then the edge detection of the original video frames is achieved after pre-sharpening using improved Sobel operator and Otsu algorithm. Finally, by feature fusion (of the foreground and background feature maps) and contour filling, the motion detection results can be obtained. The experiments demonstrate the improvements of the proposed modifications at a limited additional cost.

关键词： Image edge detection Motion detection Feature extraction Filtering algorithms Signal processing algorithms Maximum likelihood detection Nonlinear filters

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Scene-specific pedestrian detection based on parallel vision

arXiv

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arXiv 2017年

作者： Zhang, Wenwen Wang, Kunfeng Qu, Hua Zhao, Jihong Wang, Fei-Yue School of Software Engineering Xi’an Jiaotong University Xi’an710049 China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Qingdao Academy of Intelligent Industries Qingdao266000 China School of Software Engineering Xi’an Jiaotong University Xi’an710049 China Department of Communication and Information Engineering Xi’an Jiaotong University Xi’an710049 China Research Center for Computational Experiments and Parallel Systems Technology National University of Defense Technology Changsha410073 China

As a special type of object detection, pedestrian detection in generic scenes has made a significant progress trained with large amounts of labeled training data manually. While the models trained with generic dataset work bad when they are directly used in specific scenes. With special viewpoints, flow light and backgrounds, datasets from specific scenes are much different from the datasets from generic scenes. In order to make the generic scene pedestrian detectors work well in specific scenes, the labeled data from specific scenes are needed to adapt the models to the specific scenes. While labeling the data manually spends much time and money, especially for specific scenes, each time with a new specific scene, large amounts of images must be labeled. What’s more, the labeling information is not so accurate in the pixels manually and different people make different labeling information. In this paper, we propose an ACP-based method, with augmented reality’s help, we build the virtual world of specific scenes, and make people walking in the virtual scenes where it is possible for them to appear to solve this problem of lacking labeled data and the results show that data from virtual world is helpful to adapt generic pedestrian detectors to specific scenes. Copyright © 2017, The Authors. All rights reserved.

关键词： Virtual reality

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