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IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI)

作者： Yang, Jian Hu, Bin Yu, JieHan An, Jianqi Xiong, Gang Chinese Acad Sci Cloud Comp Ctr Dongguan Res Inst CASIA Dongguan Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management Control Complex Syst Beijing Peoples R China Guang Zhou Commun Informat Construct Investment & Guangzhou Guangdong Peoples R China Cent S Univ Sch Informat Sci & Engn Changsha Hunan Peoples R China

ISBN: (纸本)9781479905300;9781479905294

License Plate Recognition based on machine vision has been widely used in ITS(Intelligent Transportation system) and Smart Parking system. In this paper, we developed a system which included the image acquisition, license plate location, character segmentation and character recognition. To improve the accuracy of the license plate location, we improved the effect of the binary image positioning method. In character segmentation, we solved several typical license plate characteristics. In order to improve the accuracy of segmentation, we have taken a variety of methods to deal with their respective characteristics. In character recognition, we use template matching method and considered whether to consider the background matching. In the system, we just adopted the image recognition to recognize the characters. With no significant exposure, the area of license plate is more than 1/5 of the total images, we can get a good recognition.

关键词： License Plate Location Template matching Character Recognition Machine Vision

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Target Tracking of Robotic Fish Based on Embedded Vision and CPG Model

Target Tracking of Robotic Fish Based on Embedded Vision and...

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IEEE International Conference on Information and Automation

作者： Feihu Sun Junzhi Yu De Xu Ming Wang The State Key Lab of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China School of Information and Electrical Engineering Shandong Jianzhu University Jinan 250101 China

ISBN: (纸本)9781479913329

There has been greatly increased interest in using embedded vision for aquatic environments. This paper presents a robust target tracking method for a free-swimming biomimetic robotic fish by means of embedded vision and central pattern generator (CPG)-based motion control. Specifically, an algorithm called automatic continuous adaptive mean shift (Auto-CAMSHIFT) is firstly proposed to obtain the position and size of the interested target. Then a fuzzy logic controller is developed to generate the control input closely related to the desired target. A CPG controller that is robust against small and unexpected disturbance, at the same time, is employed to produce coordinated signals for multiple moving joints of the robotic fish. Finally, aquatic testing results verify the effectiveness of the proposed methods and show a satisfactory performance.

关键词： Embedded vision Central Pattern Generator (CPG) fuzzy logic control robotic fish

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A Review on Robot Learning and controlling: Imitation Learning and Human-computer Interaction

A Review on Robot Learning and Controlling: Imitation Learni...

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25th Chinese control and Decision Conference (CCDC)

作者： Wang, QiZhi Xu, De Shi, LuYan Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing 100044 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

ISBN: (纸本)9781467355322;9781467355339

Robot have been increasingly and significantly more powerful and intelligent over the last decade, and moving towards more service oriented roles. Imitation learning and human-robot interaction play important roles in effectively improving robot's intelligence and ability to co-work with human being. This paper reviews the current state of the art in robot learning and controlling based on imitation learning and human-robot interaction. Recent research, imitation, and the control strategy for imitation learning are addressed. Human-robot physical interaction and multi-modal interaction are emphasized.

关键词： robot learning control imitation learning human-robot interaction human-robot physical interaction multi modal human robot interaction

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Optimal control for discrete-time affine non-linear systems using general value iteration

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IET control THEORY AND APPLICATIONS 2012年第18期6卷 2725-2736页

作者： Li, H. Liu, D. Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100190 Peoples R China

In this study, the authors propose a novel adaptive dynamic programming scheme based on general value iteration (VI) to obtain near optimal control for discrete-time affine non-linear systems with continuous state and control spaces. First, the selection of initial value function is different from the traditional VI, and a new method is introduced to demonstrate the convergence property and convergence speed of value function. Then, the control law obtained at each iteration can stabilise the system under some conditions. At last, an error-bound-based condition is derived considering the approximation errors of neural networks, and then the error between the optimal and approximated value functions can also be estimated. To facilitate the implementation of the iterative scheme, three neural networks with Levenberg-Marquardt training algorithm are used to approximate the unknown system, the value function and the control law. Two simulation examples are presented to demonstrate the effectiveness of the proposed scheme.

关键词： Nonlinear systems

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Intelligent Ports Based on Internet of Things

Intelligent Ports Based on Internet of Things

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IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI)

作者： Dong Xisong Xiong Gang Guo Xiujiang Li Yuantao Lv Yisheng Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Dongguan Peoples R China Measurements Ctr Stat PetroChina Huabei Oilfield Co Renqiu Peoples R China Chinese Acad Sci Cloud Comp Ctr Beijing 100864 Peoples R China Chinese Acad Sci Inst Automat Beijing Engn Res Ctr Intelligent Syst Technol Beijing 100864 Peoples R China

ISBN: (纸本)9781479905300;9781479905294

In this article, the informatization developments of main international ports are summarized. Then, the detail requirements of next generation Intelligent Ports are collected and analyzed. Later, the corresponding key technologies of Internet of Things are summarized. And, a kind of Intelligent Ports solution is proposed, and its main functions are designed in detail. Finally, the future development trend of Intelligent Ports is predicted.

关键词： Intelligent Ports Internet of Things Wireless Sensor Network Radio Frequency Identification

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An iterative ∈-optimal control scheme for a class of discrete-time nonlinear systems with unfixed initial state

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NEURAL NETWORKS 2012年 32卷 236-244页

作者： Wei, Qinglai Liu, Derong Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

In this paper, a finite horizon iterative adaptive dynamic programming (ADP) algorithm is proposed to solve the optimal control problem for a class of discrete-time nonlinear systems with unfixed initial state. A new is an element of-optimal control algorithm based on the iterative ADP approach is proposed that makes the performance index function iteratively converge to the greatest lower bound of all performance indices within an error is an element of in finite time. The convergence analysis of the proposed ADP algorithm in terms of performance index function and control policy is conducted. The optimal number of control steps can also be obtained by the proposed is an element of-optimal control algorithm for the unfixed initial state. Neural networks are used to approximate the performance index function, and compute the optimal control policy, respectively, for facilitating the implementation of the is an element of-optimal control algorithm. Finally, a simulation example is given to show the effectiveness of the proposed method. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Adaptive dynamic programming Approximate dynamic programming is an element of-optimal control Finite horizon Neural networks

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control system Design for Two-Wheel Self-Balanced Robot Based on the Stepper Motor

Control System Design for Two-Wheel Self-Balanced Robot Base...

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IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI)

作者： Zhang, Rui Xiong, Gang Cheng, Changjian Shang, Xiuqin Ma, Yonghong Lu, Zichen Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Chinese Acad Sci Inst Automat Beijing Engn Res Ctr Intelligent Syst & Technol Beijing Peoples R China Chinese Acad Sci Cloud Comp Ctr CASIA Dongguan Res Inst Dongguan Peoples R China Dongguan KAIN Elect Sci & Tech Co Ltd Dongguan Peoples R China

ISBN: (纸本)9781479905300;9781479905294

A general control system of two-wheel self-balanced robot usually has a complex structure due to plenty of sensors, whose price is very expensive. According to this condition, a control system with the stepper motor driver is proposed, eliminating the expensive components, like optical encoder and brushless DC motor. At the same time, the low-power and high-performance STM32, 32-bit microprocessor, is selected as a controller. Outputted pulse frequency is adjusted through the adjustment of STM32 timer prescaler value, which regulates the step motor speed. With inclination angle formed by the Kalman filter of the output signal of the accelerometer and gyroscope as feedback, self-balanced closed loop control of two-wheeled robot is realized.

关键词： Two-wheel robot Self-balanced STM32 Kalman Filter PID control Prescaler value

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A Hybrid Approach for Multiple Particle Tracking Microrheology

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INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC systemS 2013年第2期10卷

作者： Xie, Liangjun Gu, Nong Cao, Zhiqiang Li, Dalong Schlumberger Ltd Houston TX 77056 USA Deakin Univ Ctr Intelligent Syst Res Waurn Ponds Australia Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Hewlett Packard Corp Houston TX USA

Geometric object detection has many applications, such as in tracking. Particle tracking microrheology is a technique for studying mechanical properties by accurately tracking the motion of the immersed particles undergoing Brownian motion. Since particles are carried along by these random undulations of the medium, they can move in and out of the microscope's depth of focus, which results in halos (lower intensity). Two-point particle tracking microrheology (TPM) uses a threshold to find those particles with peak, which leads to the broken trajectory of the particles. The halos of those particles which are out of focus are circles and the centres can be accurately tracked in most cases. When the particles are sparse, TPM will lose certain useful information. Thus, it may cause inaccurate microrheology. An efficient algorithm to detect the centre of those particles will increase the accuracy of the Brownian motion. In this paper, a hybrid approach is proposed which combines the steps of TPM for particles in focus with a circle detection step using circular Hough transform for particles with halos. As a consequence, it not only detects more particles in each frame but also dramatically extends the trajectories with satisfactory accuracy. Experiments over a video microscope data set of polystyrene spheres suspended in water undergoing Brownian motion confirmed the efficiency of the algorithm.

关键词： Multiple Particle Tracking Microrheology Circular Hough Transform Circle Detection

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Improved Rao-Blackwellized Particle Filter by Particle Swarm Optimization

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JOURNAL OF APPLIED MATHEMATICS 2013年第1期2013卷

作者： Zhao, Zeng-Shun Feng, Xiang Lin, Yan-yan Wei, Fang Wang, Shi-Ku Xiao, Tong-Lu Cao, Mao-Yong Hou, Zeng-Guang Tan, Min Shandong Univ Sci & Technol Coll Informat & Elect Engn Shandong Prov Key Lab Robot & Intelligent Technol Qingdao 266590 Peoples R China Shandong Univ Sch Control Sci & Engn Jinan 250061 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100090 Peoples R China

The Rao-Blackwellized particle filter (RBPF) algorithm usually has better performance than the traditional particle filter (PF) by utilizing conditional dependency relationships between parts of the state variables to estimate. By doing so, RBPF could not only improve the estimation precision but also reduce the overall computational complexity. However, the computational burden is still too high for many real-time applications. To improve the efficiency of RBPF, the particle swarm optimization (PSO) is applied to drive all the particles to the regions where their likelihoods are high in the nonlinear area. So only a small number of particles are needed to participate in the required computation. The experimental results demonstrate that this novel algorithm is more efficient than the standard RBPF.

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Learning Representative Features for Robot Topological Localization Regular Paper

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INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC systemS 2013年第4期10卷

作者： Zhao, Zeng-Shun Feng, Xiang Wei, Fang Lin, Yan-Yan Li, Yi-Bin Hou, Zeng-Guang Tan, Min Shandong Univ Sch Control Sci & Engn Jinan 250100 Peoples R China Shandong Univ Sch Control Sci & Engn Jinan 250100 Peoples R China Shandong Univ Sci & Technol Coll Informat & Elect Engn Qingdao Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China

This paper proposes a new method for mobile robots to recognize places with the use of a single camera and natural landmarks. In the learning stage, the robot is manually guided along a path. Video sequences are captured with a front-facing camera. To reduce the perceptual alias of visual features, which are easily confused, we propose a modified visual feature descriptor which combines the dominant hue colour information with the local texture. A Location Features Vocabulary Model (LVFM) is established for each individual location using an unsupervised learning algorithm. During the course of travelling, the robot employs each detected interest point to vote for the most likely place. The spatial relationships between the locations, modelled by the Hidden Markov Model (HMM), are exploited to increase the robustness of location recognition in cases of dynamic change or visual similarity. The proposed descriptors are compared with several state-of-the-art descriptors including SIFT, colour SIFT, GLOH and SURF. Experiments show that both the LVFM based on the dominant Hue-SIFT feature and the spatial relationships between the locations contribute considerably to the high recognition rate.

关键词： Vision-Based Localization Hidden Markov Model Invariant Feature Competitive Learning

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