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第33届中国控制与决策会议

作者： Guangrong Chen Huafeng Lu Bowen Hou Sheng Guo Junzheng Wang Robotics Research Center Beijing Jiaotong University Beijing Engineering and Technology Research Center of Rail Transit Line Safety and Disaster Prevention State Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of Technology

In traditional impedance control model,the contact force can be reduced ***,there exists a tracking error at the stable state due to the existence of stiffness,which is not conducive to tackle tasks based on high performance position control for ***,this paper proposes a novel dynamic interaction model:fractional order impedance control,to address this ***,an integral item is added into the traditional impedance model to eliminate the tracking ***,the idea of fractional order is introduced to make the orders of inertia,damping,and stiffness change from integers to fractions to achieve more significant compliant ***,simulation results validate the advantages of proposed fractional order impedance control and it can be also employed to absorb/increase,hold/keep,and dissipate/decrease system energy to achieve jumping,bouncing and friendly contact,***,stability analysis and three criterions of choosing and tuning 6 classic parameters in the proposed fractional order impedance control are both given out.

关键词： Dynamic interaction Compliance control Fractional order Impedance control Performance analysis

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A moving target detection and localization strategy based on optical flow and pin-hole imaging methods using monocular vision

A moving target detection and localization strategy based on...

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2021 IEEE International Conference on Real-Time Computing and robotics, RCAR 2021

作者： Wang, Shun Guo, Qingqiang Xu, Sheng Su, Dan School of Control Science and Engineering Shandong University Shandong Jinan250061 China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Shenzhen518055 China Shandong Institute of Advanced Technology Chinese Academy of Sciences Shandong Jinan250102 China Orbbec Inc. Shenzhen518057 China

ISBN: (纸本)9781665436786

This paper proposes a new strategy for moving target detection and localization based on monocular vision. Firstly, to detect a moving target with large displacement and high speed accurately, two consecutive video images captured by a monocular camera are preprocessed using the enhancement and denoising methods. Then, the optical flow representing motion information is calculated iteratively by the modified Lucas-Kanade optical flow method. Secondly, a new interest region extraction method is developed to overcome the negative impacts caused by the noises in the background. Specifically, this proposed method combines a two-level image segmentation strategy from coarse to fine, including median filtering, two-direction dynamic threshold segmentation, the Otsu method, and morphological processing. Thirdly, a low computational cost target localization algorithm is proposed based on pin-hole imaging theory. Besides, it only uses two-dimensional image and camera parameters to obtain the moving target's position in the three-dimensional space. Finally, experimental results show that the proposed strategy can effectively eliminate noise interferences and realize moving target detection, extraction, and localization. © 2021 IEEE.

关键词： Image segmentation

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Optimization of Simulation Parameters of Input Shaper Based on Genetic Algorithm

Optimization of Simulation Parameters of Input Shaper Based ...

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Advanced Technologies in intelligent control, Environment, Computing & Communication Engineering (ICATIECE), International Conference on

作者： Tao Yi Quanshen Pei Deying Li Shugong Wei Danping Jia Hualiang Zhang Hunan University of Information Changsha China School of Information Science and Engineering Shenyang University of Technology Shenyang China College of Physics and Electronic Engineering Guangxi Normal University for Nationalities Chongzuo China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9781665493970

In the motion control system, input shaper is often used to suppress the residual oscillation of high-precision positioning system, but the selection of input shaper parameters is difficult. In view of the difficulty of selecting input shaper parameters, this paper proposes to use genetic algorithm to select the parameters of input shaper. Firstly, the input shaper is designed in the light of the three-loop mathematical model of motor; Secondly, because the genetic algorithm directly optimizes the output of the motor and the algorithm can reduce the influence of the deviation of generator parameters on the parameter selection of the input shaper to a certain extent, the parameters of the input shaper are selected by the genetic algorithm to optimize the output of the motor; In the finish, a comparative experiment between a system with input shaper and a system with simple PID control is carried out. The results show that the optimized system can obtain higher robustness and smaller tracking deviation.

关键词： Vibrations Tracking Mathematical models Robustness Decoding Rigidity Motion control

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LinK3D: Linear Keypoints Representation for 3D LiDAR Point Cloud

arXiv

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arXiv 2022年

作者： Cui, Yunge Zhang, Yinlong Dong, Jiahua Sun, Haibo Chen, Xieyuanli Zhu, Feng The Key Laboratory of Opto-Electronic Information Processing Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China The Key Laboratory of Networked Control Systems Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China The State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Science Shenyang China The Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110016 China The University of Chinese Academy of Sciences Beijing101408 China The Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai China The College of Intelligence Science and Technology National University of Defense Technology Changsha China

Feature extraction and matching are the basic parts of many robotic vision tasks, such as 2D or 3D object detection, recognition, and registration. As is known, 2D feature extraction and matching have already achieved great success. Unfortunately, in the field of 3D, the current methods may fail to support the extensive application of 3D LiDAR sensors in robotic vision tasks due to their poor descriptiveness and inefficiency. To address this limitation, we propose a novel 3D feature representation method: Linear Keypoints representation for 3D LiDAR point cloud, called LinK3D. The novelty of LinK3D lies in that it fully considers the characteristics (such as the sparsity and complexity) of LiDAR point clouds and represents the keypoint with its robust neighbor keypoints, which provide strong constraints in the description of the keypoint. The proposed LinK3D has been evaluated on three public datasets, and the experimental results show that our method achieves great matching performance. More importantly, LinK3D also shows excellent real-time performance, faster than the sensor frame rate at 10 Hz of a typical rotating LiDAR sensor. LinK3D only takes an average of 30 milliseconds to extract features from the point cloud collected by a 64-beam LiDAR and takes merely about 20 milliseconds to match two LiDAR scans when executed on a computer with an Intel Core i7 processor. Moreover, our method can be extended to LiDAR odometry task, and shows good scalability. We release the implementation of our method at https://***/YungeCui/LinK3D. Copyright © 2022, The Authors. All rights reserved.

关键词： Object detection

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Consensus of linear multi-agent systems with a smart leader

Consensus of linear multi-agent systems with a smart leader

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Chinese intelligent Systems Conference, CISC 2018

作者： Li, Yangbo Liu, Hui Liu, Zhongxin Chen, Zengqiang College of Computer and Control Engineering Nankai University Tianjin China Tianjin Key Laboratory of Intelligent Robotics Nankai University Tianjin China

ISBN: (纸本)9789811322877

This paper addresses the consensus problem with linear systems via a smart leader under directed topology. The smart leader tracks a given reference model and a control function is constructed to control the smart leader. The smart leader can utilize its neighboring followers’ feedback information when the control function meet certain condition, which can effectively reduce the leader’s controller cost and the tracking error among the leader and followers. By utilizing the relative output message of neighboring agents, a reduced-order observer is adopted under the assumption that the directed topology have a directed spanning tree. A sufficient condition is given to guarantee that the leader-following system can achieve consensus. Finally, simulation examples are given to demonstrate the effectiveness of the obtained results. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Linear systems

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A multi-resolution frontier-based planner for autonomous 3D exploration

arXiv

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arXiv 2020年

作者： Batinović, Ana Petrović, Tamara Ivanovic, Antun Petric, Frano Bogdan, Stjepan University of Zagreb Faculty of Electrical Engineering and Computing LARICS Laboratory for Robotics and Intelligent Control Systems Unska 3 Zagreb10000 Croatia

In this paper we propose a planner for 3D exploration that is suitable for applications using state-of-the-art 3D sensors such as lidars, which produce large point clouds with each scan. The planner is based on the detection of a frontier - a boundary between the explored and unknown part of the environment - and consists of the algorithm for detecting frontier points, followed by clustering of frontier points and selecting the best frontier point to be explored. Compared to existing frontier-based approaches, the planner is more scalable, i.e. it requires less time for the same data set size while ensuring similar exploration time. Performance is achieved by not relying on data obtained directly from the 3D sensor, but on data obtained by a mapping algorithm. In order to cluster the frontier points, we use the properties of the Octree environment representation, which allows easy analysis with different resolutions. The planner is tested in the simulation environment and in an outdoor test area with a UAV equipped with a lidar sensor. The results show the advantages of the approach. Copyright © 2020, The Authors. All rights reserved.

关键词： Conformal mapping

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Novel Methods Inspired by Reinforcement Learning Actor-Critic Mechanism for Eye-in-Hand Calibration in robotics

Novel Methods Inspired by Reinforcement Learning Actor-Criti...

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IEEE International Conference on Development and Learning, ICDL

作者： Chenxing Li Yinlong Liu Yingbai Hu Fabian Schreier Jan Seyler Shahram Eivazi Department of Computer Science University of Tübingen Tübingen Germany Advanced Development Analytics and Control Festo SE and Co. KG Esslingen Germany State Key Laboratory of Internet of Things for Smart City (SKL-IOTSC) University of Macau Macau China HUA Cloud Intelligent Healthcare Co. Ltd. Ningbo China Multi-Scale Medical Robotics Centre and the Chow Yuk Ho Technology Centre for Innovative Medicine The Chinese University of Hong Kong Hong Kong China

Eye-in-hand camera calibration is an essential step in robotic vision systems. Traditional methods perform the calibration via geometry computation methods where the calibration points are acquired by moving the robot arm while capturing images of a static object (often a checkerboard) of known geometry. Our work aims to explore deep reinforcement learning (DRL) techniques for solving common calibration optimization problems. Inspired by the actor-critic mechanism of DRL, we propose a novel approach to perform eye-in-hand camera calibration on the robot arm. Numerical and real-world experiments prove the feasibility of the proposed method as such here we show the potential of the reinforcement learning method to solve the camera calibration problem.

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A Haptic Exploration and Surface Classification of Objects with Four Typical Surface Properties

A Haptic Exploration and Surface Classification of Objects w...

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International Conference on Advanced robotics and Mechatronics (ICARM)

作者： Peng Qi Yunfeng Wu Tianliang Yao Bo Lu Yi Sun Jian S. Dai Department of Control Science and Engineering College of Electronics and Information Engineering Tongji University Shanghai P. R. China Robotics and Microsystems Center School of Mechanical and Electric Engineering Soochow University Suzhou Jiangsu China Laboratory of Intelligent Systems Ecole Polytechnique Federale de Lausanne Lausanne Switzerland Department of Mechanical and Energy Engineering College of Engineering Southern University of Science and Technology Shenzhen P. R. China School of Natural Mathematical and Engineering Sciences King's College London

To effectively interact with the physical world, an intelligent robot is required to have the ability to obtain the detailed features of an unknown object. Visual devices are commonly used to detect the global geometry of an object; however, detailed information such as surface properties cannot be identified using these devices. The current study proposes an efficient haptic exploration method to recognize the physical properties of surfaces using an intelligent fingertip. Our surface-following algorithm utilizes the normal force vector and the tangential force vector at the contact point between the fingertip and the target object to predict the moving direction and implement the surface exploration. And a correction index $K$ is introduced to adjust the sliding velocity. The algorithm is proved to be robust and high performance both in simulation and lab experiments. Finally, we propose and explore a haptic prediction neural network, which enables our robot to have an accurate feel through physical interaction. In the classification experiment of four types of objects with different surface properties, the average accuracy of the proposed model is 90.2%.

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An Anomaly Detection Method of Industrial Data Based on Stacking Integration

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Journal on Artificial Intelligence 2021年第1期3卷 9-19页

作者： Kunkun Wang Xianda Liu College of Automation and Electrical Engineering Shenyang Ligong UniversityShenyang110159China Key Laboratory of Networked Control Systems Chinese Academy of SciencesShenyang110016China Shenyang Institute of Automation Chinese Academy of SciencesShenyang110016China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang110169China

With the development of Internet technology,the computing power of data has increased,and the development of machine learning has become faster and *** the industrial production of industrial control systems,quality inspection and safety production of process products have always been our *** at the low accuracy of anomaly detection in process data in industrial control system,this paper proposes an anomaly detection method based on stacking integration using the machine learning *** are collected from the industrial site and processed by feature *** component analysis(PCA)and integrated rule tree method are adopted to reduce the dimension of the process data,which can restore the original feature information of the data to the maximum *** forest(RF),Adaboost,XGboost,SVM were selected as the first layer of basic *** regression(LR)was used as the secondary learner to build the exception detection model based on stacking integrated *** data was used to train the base learner model and the integrated *** comparing and analyzing the experimental results of between integrated model and each basic learning *** comparing and analyzing the experimental results of the constructed anomaly detection model and the basic learning model,the accuracy of process data anomaly detection is effectively improved,and the false alarm rate of process data anomaly detection is effectively reduced.

关键词： Industrial control system anomaly detection random forest SVM stacking

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Identifiability Analysis of Noise Covariances for LTI Stochastic Systems with Unknown Inputs

arXiv

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arXiv 2022年

作者： Kong, He Sukkarieh, Salah Arnold, Travis J. Chen, Tianshi Mu, Biqiang Zheng, Wei Xing Shenzhen518055 China Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities SUSTech Shenzhen518055 China Sydney Institute for Robotics and Intelligent Systems The University of Sydney NSW2006 Australia Madison WI53704 United States School of Data Science Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen518172 China Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China School of Computer Data and Mathematical Sciences Western Sydney University SydneyNSW2751 Australia

Most existing works on optimal filtering of linear time-invariant (LTI) stochastic systems with arbitrary unknown inputs assume perfect knowledge of the covariances of the noises in the filter design. This is impractical and raises the question of whether and under what conditions one can identify the process and measurement noise covariances (denoted as Q and R, respectively) of systems with unknown inputs. This paper considers the identifiability of Q/R using the correlation-based measurement difference approach. More specifically, we establish (i) necessary conditions under which Q and R can be uniquely jointly identified;(ii) necessary and sufficient conditions under which Q can be uniquely identified, when R is known;(iii) necessary conditions under which R can be uniquely identified, when Q is known. It will also be shown that for achieving the results mentioned above, the measurement difference approach requires some decoupling conditions for constructing a stationary time series, which are proved to be sufficient for the well-known strong detectability requirements established by Hautus. © 2022, CC BY.

关键词： Stochastic systems

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