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第三十八届中国控制会议

作者： Chao Ma Lihao Jia School of Automation and Electrical Engineering University of Science and Technology Beijing State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Research Center for Brain-inspired Intelligence Institute of Automation Chinese Academy of Sciences

This paper deals with the distributed consensus for Markov jump multi-agent systems(MJMASs) with switching topology. In particular, another different Markov process is adopted to describe the switching topology and mode-dependent transmission delays. By means of model conversion and using Lyapunov-Krasovskii functional method, consensus criterion is first established. The consensus controllers can be further obtained with the aid of matrix transform. The corresponding simulation is presented for showing the validity of the developed method.

关键词： Mode-dependent consensus Markov jump multi-agent systems asynchronous mode-dependent switching topology mode-dependent communication delays

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3D Deep Learning for 3D Printing of Tooth Model

3D Deep Learning for 3D Printing of Tooth Model

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

作者： Yiwen Liu Xiuqin Shang Zhen Shen Bin Hu Zhihai Wang Gang Xiong Faculty of Information The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing University of Technology Beijing China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Beijing Engineering Research Center of Intelligent Systems and Technology Chinese Academy of Sciences Beijing China Faculty of Information Beijing University of Technology Beijing China

Recently, with the development of both 3D sensors and 3D virtual network that bring the needs of interaction with the real world, many 3D applications burst out. However, it is difficult to understanding these three-dimensional scenes with a fixed program. Then, a data-driven method is required to process these 3D data, which brings a strong demand of 3D Deep Learning in 3D data. Towards this goal, with an end-to-end deep learning, the experiment is based on PointNet++, a well proposed method for feature extraction. The experiment optimizes the network structure and parameters to improve the classification results. Finally, the network is applied to tooth model for classification and identification so that the dental model can be found from different perspectives.

关键词： Three-dimensional displays Deep learning Transforms Cameras Object segmentation Image segmentation Sensors

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An Obstacles Avoidance Algorithm Based on Improved Artificial Potential Field

An Obstacles Avoidance Algorithm Based on Improved Artificia...

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IEEE International Conference on Mechatronics and automation

作者： Si Xi Lu En Li Rui Guo School of Artificial Intelligence University of Chinese Academy of Sciences No.19(A) Yuquan Road Beijing China The State Key Laboratory of Management and Control for Complex System Institute of Automation Chinese Academy of Science 95 East ZhongGuanCun Road Beijing China State Grid Shandong Electric Power Company Jinan China

ISBN: (数字)9781728164168

ISBN: (纸本)9781728164175

Obstacle avoidance is one of the most important issue in the motion planning and control of robot. There are many algorithms to avoid obstacles, but obstacle avoidance algorithm for manipulator is needed consider not only end effector, but also all links of manipulator. In terms of real-time and speed of all system, the algorithm also should be easy as possible. In this paper, an algorithm based on the improved artificial potential field is proposed to solve the problem of the local optimum. The idea of improvement is to find a path by fitting curve with target point and critical oscillating points. There is also a comparison in this paper with planning a path by tangent line. The conclusion proves that the path is much smoother by the algorithm based on improved artificial potential field in this paper. Furthermore, the problem of judder on the trajectory of traditional artificial potential field is also solved in this paper by an improved repulsive potential field function. Finally, the algorithm is proved by planning a smooth path for obstacle avoidance on manipulator: KinovaGen3 in MATlab.

关键词： Mechatronics Fitting End effectors Real-time systems Planning Trajectory Collision avoidance

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Attention Grasping Network: A Real-time Approach to Generating Grasp Synthesis

Attention Grasping Network: A Real-time Approach to Generati...

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IEEE International Conference on Robotics and Biomimetics

作者： Qipeng Gu Jianhua Su Xueting Bi the State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Key Laboratory of Electromagnetic Radiation and Sensing Technology Institute of Electronics Chinese Academy of Sciences Beijing China

This paper presents a real-time, pixelwise method to generate grasp synthesis based on fully convolutional netural networks (FCN). Our proposed Attention Grasping Network (AGN) applies a novel attention mechanism to robotic grasp detection, which automatically learns to focus on salient features of the input image. The model with attention mechnisms can compensate for the loss of detail information in standard FCN, which increases the sensitivity of the model and accuracy of prediction. In addition,in order to ensure a real-time grasp and save computing resources, the light-weight AGN model predicts the position and angle of grasping point. Our method only takes 22ms to execute the grasp detection pipeline on a GPU-equipped computer, and achieves 97.8% accuracy on Cornell Grasping Dataset.

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An Active and Adaptive Image Enhancement Method for Applications in Low-Light and Narrow Environment

An Active and Adaptive Image Enhancement Method for Applicat...

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Chinese automation Congress (CAC)

作者： Mingrui Luo En Li Rui Guo Shengchuan Li Cunfeng Kang The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China State Grid Shandong Electric Power Company Jinan China State Grid Liaoning Electric Power Company Limited Shenyang China Mechanical engineering and Applied Electronic Technology Beijing University of Technology Beijing China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

The images in the low-light and narrow environment generally have the problems of non-uniform illumination, low contrast, and large random noise. Existing algorithms are mostly subject to the imaging effect of the original image, and are prone to failure in extremely weak light or strong reflective environments. This paper proposes an active and adaptive image enhancement method. First of all, this paper combines the distributed light source model and the difference in brightness between frames to complete active feedback fill light, which improves the quality of the input image. Then gamma correction, contrast limited adaptive histogram equalization and bilateral filter are designed to enhance image details and suppress noise amplification. Combined with fuzzy inference parameter self-adjustment method, algorithm adaptability is improved. Finally, through experimental verification, the method proposed in this paper can effectively enhance the images in the low-light and narrow environment.

关键词： Light sources Histograms Adaptive equalizers Filtering algorithms Brightness Image color analysis Image enhancement

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Collaborative Optimization of Cyber Physical Social systems for Urban Transportation Based on Knowledge automation

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IFAC-PapersOnLine 2020年第5期53卷 572-577页

作者： Gang Xiong Xiaoyu Chen Nan Shuo Yisheng Lv Fenghua Zhu Tianci Qu Peijun Ye State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing 100049 China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing The Cloud Computing Center Chinese Academy of Sciences Dongguan 523808 China Graduate School of System Design Tokyo Metropolitan University Tokyo 191-0065 Japan

The integration of signals from physical, social and cyber spaces, known as Cyber-Physical-Social systems (CPSS), is a new research paradigm for urban transportation, where the traffic control and management (C&M) is collaborative optimized among the three sub-systems. Though some technologies and optimization methods have been studied since its proposition, there is a lack of a systemic architecture as well as an overall implementation about how to efficiently exploit the social signals. For this reason, this paper proposes a general framework of CPSS for urban transportation and presents a feasible solution for traffic optimization based on knowledge automation. The specific implementation includes basic modeling of CPSS, knowledge evolution and reasoning, and collaborative optimization of C&P strategies. As a remarkable highlight, the influence of both individual activities and social learning is concerned during knowledge evolution and reasoning part. A case study from the application in the city of Dongguan is also given to validate our proposed framework and methods, showing that they can efficiently improve the average speed of the actual transportation.

关键词： CPSS knowledge automation urban transportation Collaborative Optimization

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Cyber-Physical-Social systems for Smart City: An Implementation Based on Intelligent Loop

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IFAC-PapersOnLine 2020年第5期53卷 501-506页

Cyber-Physical-Social systems (CPSS) provides a novel perspective for constructing “Smart City”, which is also known as the Human-Machine-Things-System (HMTS), focusing on the fusion of ternary space: social network of human society, network of machines and the Internet of things. In this paper, we propose a specific implementation framework of CPSS for Smart City based on intelligent loops, including basic modeling and interactive fusion, state perception and cognition, and adaptive learning. On this basis, an overall architecture of the CPSS platform is designed, which is applied in the urban transportation management in Hangzhou. The application results demonstrate that the intelligent loop could optimize the control and management strategies for actual urban transportation.

关键词： CPSS Intelligent Loop Human-Cyber-Physical Ternary Space

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Multi-robot Path Planning and Trajectory Smoothing

Multi-robot Path Planning and Trajectory Smoothing

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IEEE International Conference on automation Science and Engineering (CASE)

作者： Hub Ali Gang Xiong Huaiyu Wu Bin Hu Zhen Shen Hongxing Bai School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences China Hubei Jointown Technology Co. Ltd Artificial Intelligence Research Institute Wuhan China

ISBN: (数字)9781728169040

ISBN: (纸本)9781728169057

In this paper we consider a problem in task execution for multi-robot trajectory planning with collision avoidance in a shared working environment. Consider two or more robots generating trajectories towards their respective goal positions. The collision may occur if their trajectory coordinates are intersecting at a point or follow the same path segment simultaneously. The central planner is introduced to control robot motion in the collision state and to reduce the complexity of the multi-robot path planning system. The global path for every robot is generated by the $\mathrm{A}^{*}$ algorithm in a grid-based environment. The path has presented a sequence of optimal grid numbers and later transformed into Cartesian coordinates for smooth trajectory generation. The central planner takes an optimal grid sequence for every robot to analyze the collision state according to its cost value. It regenerates the trajectories to minimize the complexity cost value and replaces the previous trajectory based on minimum cost value. In the collision state, the central planner allows one robot at a time to pass along the conflict path segment and hold others in queue at a safety offset distance until the previous robot passes safely. The algorithm has been applied to robots working in a shared environment in complex maps and the simulations is performed with MATlab to calculate the efficiency of this approach for handling collision states in a multi-robot path planning system.

关键词： Robot kinematics Trajectory Collision avoidance Task analysis Resource management

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BNAS: An efficient neural architecture search approach using broad scalable architecture

arXiv

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arXiv 2020年

作者： Ding, Zixiang Chen, Yaran Li, Nannan Zhao, Dongbin Sun, Zhiquan Philip Chen, C.L. State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China School of Automation and Electric Engineering University of Science and Technology Beijing Beijing100083 China School of Computer Science & Engineering South China University of Technology Guangzhou Guangdong510006 China College of Navigation Dalian Maritime University Dalian116026 China

Efficient Neural Architecture Search (ENAS) achieves novel efficiency for learning architecture with high-performance via parameter sharing and reinforcement learning. In the phase of architecture search, ENAS employs deep scalable architecture as search space whose training process consumes most of search cost. Moreover, time consuming of model training is proportional to the depth of deep scalable architecture. Through experiments using ENAS on CIFAR-10, we find that layer reduction of scalable architecture is an effective way to accelerate the search process of ENAS but suffers from prohibitive performance drop in the phase of architecture estimation. In this paper, we propose Broad Neural Architecture Search (BNAS) where we elaborately design broad scalable architecture dubbed Broad Convolutional Neural Network (BCNN) to solve the above issue. On one hand, the proposed broad scalable architecture has fast training speed due to its shallow topology. Moreover, we also adopt reinforcement learning and parameter sharing used in ENAS as the optimization strategy of BNAS. Hence, the proposed approach can achieve higher search efficiency. On the other hand, the broad scalable architecture extracts multi-scale features and enhancement representations, and feeds them into global average pooling layer to yield more reasonable and comprehensive representations. Therefore, the performance of broad scalable architecture can be promised. In particular, we also develop two variants for BNAS who modify the topology of BCNN. In order to verify the effectiveness of BNAS, several experiments are performed and experimental results show that 1) BNAS delivers 0.19 days which is 2.37x less expensive than ENAS who ranks the best in reinforcement learning-based NAS approaches, 2) compared with small-size (0.5 millions parameters) and medium-size (1.1 millions parameters) models, the architecture learned by BNAS obtains state-of-the-art performance (3.58% and 3.24% test error) on CIFAR-1

关键词： Topology

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Sliding mode fuzzy control-based path-following control for a dolphin robot

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Science China(Information Sciences) 2018年第2期61卷 240-242页

作者： Jincun LIU Zhengxing WU Junzhi YU Min TAN State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences University of Chinese Academy of Sciences

Dolphins are one of the most enigmatic sea creatures in the ***-term evolution endows them with excellent swimming skills,which motivate researchers and engineers to create an innovative biomimetic propulsive mechanis... 详细信息

关键词： Sliding mode fuzzy control-based path-following control for a dolphin robot

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