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Chinese automation Congress (CAC)

作者： GuangYao Xu En Li School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China The State Key Labrary of Management and Control for Complex System Institute of Automation Chinese Academy of Science Beijing China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

In this paper, we designed a real-time three-electrode cylindrical conductivity sensor for special working conditions. Compared with traditional conductivity sensors, the three-electrode design balances the relationship between measurement accuracy and structural complexity. The cylindrical electrode core can adapt to some complex working environments that traditional intrusive electrodes cannot. The CoOS real-time operating system adopted by the measurement software has the characteristics of a small kernel and low requirement for computing resources. It can well meet the demand for online measurement of conductivity sensor. The amplitude and frequency of the excitation signal can be adjusted by the measurement software, avoiding the invariance of the signal by hardware adjustment. Since the error is greatly affected by temperature, the sensor can effectively reduce the error by compensating for temperature. All electronic components of the sensor are designed with low power consumption, which can extend the working time while ensuring normal operation.

关键词： Conductivity Temperature measurement Electrodes Conductivity measurement Electrical resistance measurement Software measurement Software

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A vision-based path planning and following system for a miniature robotic fish

A vision-based path planning and following system for a mini...

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2019 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2019

作者： Lu, Yue Chen, Xingyu Wu, Zhengxing Yan, Yan Yu, Junzhi 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 State Key Laboratory for Turbulence and Complex System Department of Mechanics and Engineering Science BIC-ESAT College of Engineering Peking University Beijing100871 China

ISBN: (纸本)9781728137261

Most existing path following algorithms for middle- or large-sized underwater vehicles minimize yaw difference of the control object. Breaking the canonical manner, this paper presents a vision-based path planning and following system for miniature robotic fish. As for path planning, the proposed system finds a gird path based on the AStar algorithm and selects a series of target points in the grid path. By connecting these points in order, a curve path can be planned. In terms of path following, the proposed system forgoes to give a specific yaw and only uses a global camera. It obtains coordinates of the robotic fish in real time by KCF tracking algorithm to calculate an approximate motion vector. A PID controller is employed to drive the difference angle between the motion vector and the target vector (the vector from the robotic fish to the target point) to zero. By swimming to target points one by one, the robotic fish can move to the terminus following the planned path. Finally, aquatic experiments are conducted to verify the effectiveness of the vision-based path planning and following system. The results reveal that the developed system can generate a safe and smooth path, and the robotic fish can follow the path with acceptable deviation. © 2018 IEEE.

关键词： Motion planning

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Group Consensus for Euler-Lagrange Multi-Agent systems with Dynamic Event-Triggered control

Group Consensus for Euler-Lagrange Multi-Agent Systems with ...

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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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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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Design of Cascade control Framework for ROV control and Simulation

Design of Cascade Control Framework for ROV Control and Simu...

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IEEE International Conference on Mechatronics and automation

作者： Changlin Qiu Shihan Kong Chao Zhou Junzhi Yu State Key Lab Management and Control for Complex Systems Institute of Automation CAS Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Naval Research Academy Beijing China BIC-ESAT College of Engineering Peking University Beijing China

ISBN: (数字)9781728164168

ISBN: (纸本)9781728164175

In this paper, a cascade control framework is proposed a class of remote operated vehicles (ROVs) to enhance the control performance and capacity of interference resistance. Specifically, the control framework is composed of a fuzzy-based controller in primary loop and a PID neural network based controller in secondary loop. In the primary loop, fuzzy controller is implemented to avoid precise establishment of ROV model for calculating the expected velocity of the vehicle. Furthermore, in the secondary loop, a PID neural network with adaptation is adopted to accomplish the capacity of resisting disturbance. Eventually, the control framework is verified by theoretical and simulation analyses. The obtained results indicate that the proposed cascade control framework endows the ROV with the ability to guarantee the control performance and attenuate external disturbance effectively.

关键词： Resistance Couplings Adaptation models Mechatronics Software packages Neural networks Interference

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A Re-Ranking Framework for Knowledge Graph Completion

A Re-Ranking Framework for Knowledge Graph Completion

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International Joint Conference on Neural Networks (IJCNN)

作者： Zikang Wang Linjing Li Daniel Dajun Zeng 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 Shenzhen Artificial Intelligence and Data Science Institute (Longhua) Shenzhen China

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

Knowledge graph completion, one of the most important research questions in knowledge graphs, aims at predicting missing links in a given graph. Current mainstream approaches adopt high-quality embeddings of entities and relations of the graph to improve their performances. However, it is not easy to devise a universal embedding learner that can fit various scenarios. In this paper, we propose a general-purpose framework which can be employed to improve the performance of knowledge graph completion. Specifically, given an arbitrary knowledge graph completion model, we first run the original model to get a ranked entity list. Then, we combine the query and the top ranked entities with attention mechanism, re-rank all these entities by feeding the combined vector into a neural network. The proposed re-ranking phase can be conveniently added to a variety of models to improve their performance without substantial modification. We conduct experiments on four datasets: WN18, FB15k, WN18RR, and FB15k-237. We choose TransE, TransH, TransD, DistMult, and ANALOGY as base models. Experiments on these datasets and models validate the effectiveness of the proposed re-ranking framework. We further explore the influence of the number of top ranked entities used in the re-ranking phase. We also test other attention mechanism to determine the most effective one, and found that vanilla attention mechanism can balance accuracy and complexity.

关键词： Computational modeling Task analysis Neural networks Semantics complexity theory Measurement Magnetic heads

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MatchMesh: Knowledge-based 3D Point Cloud Meshing Using Divide-and-conquer Deformation

MatchMesh: Knowledge-based 3D Point Cloud Meshing Using Divi...

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IEEE International Conference on Networking, Sensing and control

作者： Ying Tang Shengtao Sun Ben Wu Rowan University Glassboro USA State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Institute of Smart Education Qingdao Academy of Intelligent Industries Qingdao China Rowan University Glassboro NJ USA

ISBN: (数字)9781728168555

ISBN: (纸本)9781728168562

The reconstruction of surface mesh from point cloud is compute-intensive but also very important step in the remanufacturing and personalization industries. With more 3D scanners providing lower cost and higher resolution, further detailed point clouds can be gathered without so much effort as before. In manufacturing, there are databases which contain the origin 3D design models of the products. How to utilize the design model data for swift production of related products remains a problem for remanufacturing and customization. In order to develop a knowledge-based way of handling this problem, editing or deforming an existing mesh to match the target is an effective way of easing the workload. In this paper, we introduce a divide-and -conquer process which segments the depth scan data and then find the best match in the database as its source of deformation. The segmentation is performed on 3D point level using global features extracted by 3D CNN. After that we find best match to our knowledge with the same features to acquire a fast meshing of the target object by deforming the existing parts from the match. The deformation of parts are being done sequentially. For further performance improvement, we present a deformation training method employing transfer learning on segment editing process.

关键词： Training Surface reconstruction Three-dimensional displays Databases Knowledge based systems Feature extraction Strain

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