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International Conference on control and automation (ICCA)

作者： Ran Cao Long Cheng Zhe Dong State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China School of Electrical and Control Engineering North China University of Technology Beijing China

ISBN: (数字)9781728190938

ISBN: (纸本)9781728190945

This paper mainly investigates the group consensus of Euler-Lagrange multi-agent systems based on the dynamical event-triggered controller. All agents in the system are divided into several groups. The agents in the same group converge to one state and those in the different groups may not coincide. To use fewer communication resources, an event-triggered controller using the dynamical event-triggered law is designed. Several mild assumptions are made to achieve the group consensus. And Lyapunov analysis is given to prove that the group consensus can be achieved under the designed control algorithm. Compared to existing results, this paper mainly designs the dynamical event-triggered law to achieve the group consensus rather than consensus or tracking, and the concerned agent is the nonlinear Euler-Lagrange system.

关键词： Nonlinear dynamical systems Directed graphs Multi-agent systems Laplace equations Symmetric matrices Network topology Torque

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Generalized Visual-Tactile Transformer Network for Slip Detection

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IFAC-PapersOnLine 2020年第2期53卷 9529-9534页

作者： Shaowei Cui Junhang Wei Xiaocan Li Rui Wang Yu Wang Shuo Wang The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China School of Future Technology University of Chinese Academy of Sciences Beijing 100049 China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai 200031 China

Slip detection plays a vital role in robotic dexterous grasping and manipulation, and it has long been a challenging problem in the robotic community. Different from traditional tactile perception-based methods, we propose a Generalized Visual-Tactile Transformer (GVT-Transformer) network to detect slip based on visual and tactile spatiotemporal sequences. The main novelty of GVT-Transformer is its ability to address unaligned vision and tactile data in various formats captured by various tactile sensors. Furthermore, we train and test our proposed network on a public and our visual-tactile grasping datasets. The experimental results show that our method is more suitable for sliding detection tasks than previous visual-tactile learning methods and more versatile.

关键词： Information sensor fusion Perception sensing Intelligent robotics Deep neural networks Visual-tactile fusion perception

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Moving Target Tracking control of a Biomimetic Robotic Fish

Moving Target Tracking Control of a Biomimetic Robotic Fish

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Chinese automation Congress (CAC)

作者： Yangfan Zhou Yingnian Wu Chao Zhou Pengfei Zhang Zhengxing Wu School of Automation Beijing Information Science and Technology University Beijing China Naval Research Academy Beijing China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

This paper proposes a moving target tracking control framework for a biomimetic robotic fish. Inspired by a boxfish in nature, a single-joint robotic fish with a pair of one-degree-of-freedom pectoral fins is first developed. A camera and an infrared sensor are specially employed to recognize the underwater target. In order to describe the fishlike swimming, a dynamic model based on the Newton-Euler method is constructed. Meanwhile, according to the control approach of fishlike swimming motion, a target tracking control framework including a speed controller and a direction controller is proposed to track a static or moving target, where the oscillation frequency and directional bias parameters are adopted to regulate the swimming speed and direction, respectively. Finally, simulation results verify the effectiveness of the proposed target tracking control method, where the robotic fish is controlled to track a red target moving in a circle at a certain speed.

关键词： Robots Target tracking Sports Robot kinematics Service robots automation Mechatronics

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Webly Supervised Knowledge Embedding Model for Visual Reasoning

Webly Supervised Knowledge Embedding Model for Visual Reason...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Wenbo Zheng Lan Yan Chao Gou Fei-Yue Wang School of Software Engineering Xi'an Jiaotong University The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences School of Intelligent Systems Engineering Sun Yat-sen University

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

Visual reasoning between visual image and natural language description is a long-standing challenge in computer vision. While recent approaches offer a great promise by compositionality or relational computing, most of them are oppressed by the challenge of training with datasets containing only a limited number of images with ground-truth texts. Besides, it is extremely time-consuming and difficult to build a larger dataset by annotating millions of images with text descriptions that may very likely lead to a biased model. Inspired by the majority success of webly supervised learning, we utilize readily-available web images with its noisy annotations for learning a robust representation. Our key idea is to presume on web images and corresponding tags along with fully annotated datasets in learning with knowledge embedding. We present a two-stage approach for the task that can augment knowledge through an effective embedding model with weakly supervised web data. This approach learns not only knowledge-based embeddings derived from key-value memory networks to make joint and full use of textual and visual information but also exploits the knowledge to improve the performance with knowledge-based representation learning for applying other general reasoning tasks. Experimental results on two benchmarks show that the proposed approach significantly improves performance compared with the state-of-the-art methods and guarantees the robustness of our model against visual reasoning tasks and other reasoning tasks.

关键词： Visualization Cognition Knowledge based systems Task analysis Knowledge engineering Modulation Robustness

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Enhancing rumor detection in social media using dynamic propagation structures

arXiv

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arXiv 2020年

作者： Wang, Shuai Kong, Qingchao Wang, Yuqi Wang, Lei State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences China University of Chinese Academy of Sciences China Shenzhen Artificial Intelligence and Data Science Institute Longhua） China State Information Center Beijing100045 China

Social media, such as Facebook and Twitter, has become one of the most important channels for information dissemination. However, these social media platforms are often misused to spread rumors, which has brought about severe social problems, and consequently, there are urgent needs for automatic rumor detection techniques. Existing work on rumor detection concentrates more on the utilization of textual features, but diffusion structure itself can provide critical propagating information in identifying rumors. Previous works which have considered structural information, only utilize limited propagation structures. Moreover, few related research has considered the dynamic evolution of diffusion structures. To address these issues, in this paper, we propose a Neural Model using Dynamic Propagation Structures (NM-DPS) for rumor detection in social media. Firstly, we propose a partition approach to model the dynamic evolution of propagation structure and then use temporal attention based neural model to learn a representation for the dynamic structure. Finally, we fuse the structure representation and content features into a unified framework for effective rumor detection. Experimental results on two real-world social media datasets demonstrate the salience of dynamic propagation structure information and the effectiveness of our proposed method in capturing the dynamic structure. Copyright © 2020, The Authors. All rights reserved.

关键词： Social networking (online)

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A simulator with an elastic guidewire and vascular system for minimally invasive vascular surgery

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Science China(Information Sciences) 2018年第10期61卷 250-252页

作者： Xiaoran CHENG Xiaoliang XIE Guibin BIAN Zengguang HOU Shiqi LIU Zhanjie GAO State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences University of Chinese Academy of Sciences CAS Center for Excellence in Brain Science and Intelligence Technology

Simulators play an important role in minimally invasive vascular surgery training. A challenging task for the simulator is to model an elastic guidewire and an elastic vascular system, both of which are important aspects for vascular ***, many simulators treat the virtual vascular system as a rigid object, which reduces the immersion of operators. In this study, a virtual guidewire is first modeled based on Cosserat rod

关键词： Pi Figure A simulator with an elastic guidewire and vascular system for minimally invasive vascular surgery

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Spiking Neuron Networks based Energy-Efficient Object Detection for Mobile Robot

Spiking Neuron Networks based Energy-Efficient Object Detect...

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Chinese automation Congress (CAC)

作者： Bo-Chao Liu Qian Yu Jing-Wen Gao Shuang Zhao Xiong-Chang Liu Yan-Feng Lu Beijing Information Science and Technology University Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China School of Instrument Science and Optoelectronic Engineering Beijing Information Science and Technology University Beijing China The State Key Laboratory of Management and Control for Complex Systems CASIA Beijing China Science and Technology on Underwater Vehicle Technology Laboratory Harbin Engineering University Harbin China

ISBN: (纸本)9781665426480

This paper focuses on the highly energy-efficient visual detection method, which first apples spiking neuron networks based object detection method in mobile robot. Visual perception is an important part of mobile robot, but the real-time visual perception is with high energy consumption that limits the battery running time of the robotic platform. In this paper, we bring in a Spiking-YOLO algorithm for the visual detection of the mobile robot, which combines the YOLO algorithm with the third generation artificial neural network-Spiking Neural Network (SNN). The Spiking-YOLO integrates the characteristics in real-time and high precision of the YOLO, and event-driven and low-powered nature of the SNN. We introduce the Spiking-YOLO model to the Robotnik's SUMMIT-XL mobile robot to perform the task of visual detection and the experimental verification is carried out on data sets. The experimental results show that this method has excellent energy-efficient and precision on the detection task of the robot.

关键词： Visualization Energy consumption Neurons Object detection Energy efficiency Real-time systems Mobile robots

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Application and Future of Modern Energy Internet Based on Artificial Intelligence

Application and Future of Modern Energy Internet Based on Ar...

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Youth Academic Annual Conference of Chinese Association of automation (YAC)

作者： Xiaojing Yang Tianlu Gao Shiqian Ma Xiaoyan Gong Zhigang Huang Dazhi Hao Power Dispatching and Control Center State Grid Tianjin Electric Power Company Tianjin China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Science Beijing China Electric Power Research Institute State Grid Tianjin Electric Power Company Tianjin China

ISBN: (数字)9781728176840

ISBN: (纸本)9781728176857

The emergence of deep learning is undoubtedly a landmark breakthrough in artificial intelligence, and the emergence of this technology makes it possible for us to realize the real smart grid. A major challenge currently facing deep learning technology is the requirement of enough training samples to ensure the accuracy of results of the learning. But for the current power grid, training data can be obtained because a lot of real time data is collected to maintain the stability of its operation. With the deep learning technology, there is no doubt that these data can be utilized to realize the intelligent operation of the power grid. First, this paper gives a brief introduction to the current artificial intelligence, followed by the introduction of deep learning technology. Some potential applications of the deep learning in the daily operation of the power grid are proposed. And in the end, there is a brief description of some of the features that future smart grids should have.

关键词： Deep learning Training Training data Learning (artificial intelligence) Power system stability Real-time systems Smart grids

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Fault-Tolerant control of a CPG-Governed Robotic Fish

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Engineering 2018年第6期4卷 861-868页

作者： Yueqi Yang Jian Wang Zhengxing Wu Junzhi Yu State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100049China University of the Chinese Academy of Sciences Beijing 100049China

Fault tolerance is essential for the maneuverability of self-propelled biomimetic robotic fish in real-world aquatic *** paper explores the fault-tolerance control problem of a free-swimming robotic fish with multiple moving joints and a stuck tail *** created control system is composed of two main components:a feedback controller and a feedforward ***,the bio-inspired central pattern generator-based feedback controller is designed to make the robotic fish robust to external disturbances,while the feedforward compensator speeds up the convergence of the overall control *** are performed for control system analysis and performance validation of the faulty robotic *** experimental results demonstrate that the proposed fault-tolerant control method is able to effectively regulate the faulty robotic fish,allowing it to complete the desired motion in the presence of damage and thereby improving both the stability and the lifetime of the real robotic system.

关键词： Fault-tolerant control Robotic fish Motion control Feedback controller Feedforward compensator

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Motion control and Motion Coordination of Bionic Robotic Fish： A Review

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Journal of Bionic Engineering 2018年第4期15卷 579-598页

作者： Junzhi Yu Ming Wang Huifang Dong Yanlu Zhang Zhengxing Wu State Key Laboratory 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

Fish＇s outstanding motion and coordination performance make it an excellent source of inspiration for scientists and engineers aiming to design and control next-generation autonomous underwater vehicles within the framework of bionics. This paper offers a general review of the current status of bionic robotic fish, with particular emphasis on the hydrodynamic modeling and testing, kinematic modeling and control, learning and optimization, as well as motion coordination control. Among these aspects, representative studies based on ideas and concepts inspired from fish motion and coordination are discussed. At last, the major challenges and the future research directions are addressed in the context of integration of various research streams from ichthyologic, hydrodynamic, mechanical, electronic, control, and artificial intelligence. Further development of bionic robotic fish can be utilized to execute some specific missions in complex underwater environments, where operations are unsafe or impractical for divers or conventional underwater vehicles.

关键词： bionic robotic fish motion control coordination control fish swimming learning and optimization

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