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arXiv 2020年

作者： Wang, Guangming Chen, Muyao Liu, Hanwen Yang, Yehui Liu, Zhe Wang, Hesheng The Department of Automation Institute of Medical Robotics Key Laboratory of System Control and Information Processing Ministry of Education Key Laboratory of Marine Intelligent Equipment System of Ministry of Education Shanghai Jiao Tong University Shanghai200240 China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology China The Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China The Department of Computer Science and Technology University of Cambridge

With the rapid development of measurwement technology, LiDAR and depth cameras are widely used in the perception of the 3D environment. Recent learning based methods for robot perception most focus on the image or video, but deep learning methods for dynamic 3D point cloud sequences are underexplored. Therefore, developing efficient and accurate perception method compatible with these advanced instruments is pivotal to autonomous driving and service robots. An Anchor-based Spatio-Temporal Attention 3D Convolution operation (ASTA3DConv) is proposed in this paper to process dynamic 3D point cloud sequences. The proposed convolution operation builds a regular receptive field around each point by setting several virtual anchors around each point. The features of neighborhood points are firstly aggregated to each anchor based on the spatio-temporal attention mechanism. Then, anchor-based 3D convolution is adopted to aggregate these anchors' features to the core points. The proposed method makes better use of the structured information within the local region and learns spatio-temporal embedding features from dynamic 3D point cloud sequences. Anchor-based Spatio-Temporal Attention 3D Convolutional Neural Networks (ASTA3DCNNs) are built for classification and segmentation tasks based on the proposed ASTA3DConv and evaluated on action recognition and semantic segmentation tasks. The experiments and ablation studies on MSRAction3D and Synthia datasets demonstrate the superior performance and effectiveness of our method for dynamic 3D point cloud sequences. Our method achieves the state-of-the-art performance among the methods with dynamic 3D point cloud sequences as input on MSRAction3D and Synthia datasets. Copyright © 2020, The Authors. All rights reserved.

关键词： Embeddings

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Real-world Robot Reaching Skill Learning Based on Deep Reinforcement Learning

Real-world Robot Reaching Skill Learning Based on Deep Reinf...

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第32届中国控制与决策会议

作者： Naijun Liu Tao Lu Yinghao Cai Rui Wang Shuo Wang State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences University of Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced TechnologyChinese Academy of Sciences Center for Excellence in Brain Science and Intelligence Technology of the Chinese Academy of Sciences

Traditional programming method can achieve certain manipulation tasks with the assumption that robot environment is known and ***,with robots gradually applied in more domains,robots often encounter working scenes which are complicated,unpredictable,and *** overcome the limitation of traditional programming method,in this paper,we apply deep reinforcement learning(DRL) method to train robot agent to obtain skill *** policy trained with DRL on real-world robot is time-consuming and costly,we propose a novel and simple learning paradigm with the aim of training physical robot ***,our method train a virtual agent in an simulated environment to reach random target position from random initial ***,virtual agent trajectory sequence obtained with the trained policy,is transformed to real-world robot command with coordinate transformation to control robot performing reaching *** show that the proposed method can obtain self-adaptive reaching policy with low training cost,which is of great benefits for developing intelligent and robust robot manipulation skill system.

关键词： Robot reaching skill learning deep reinforcement learning

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Local Pose optimization with an Attention-based Neural Network

Local Pose optimization with an Attention-based Neural Netwo...

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2019 IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Yiling Liu Hesheng Wang Fan Xu Yong Wang Weidong Chen Qirong Tang Department of Automation Shanghai Jiao Tong University Shanghai China State Key Laboratory of Robotics and System (HIT) Beijing Institute of Control Engineering Tongii University

In this paper, we propose a novel pose optimizer which can be inserted into either supervised or unsupervised end-to-end visual odometry for the purpose of local pose optimization. The pose optimizer is an analogue of the pose graph optimization used in traditional VSLAM algorithms. Local pose optimization is performed by an attention-based neural network which iteratively refines the predicted pose estimates of an image snippet. Instead of complicated graph convolutional network, the attention mechanism based on geometric consistency of trajectory constraint is utilized because pose features whose spatial distribution is not important can be flattened to vectors and then processed. The pose optimizer is aimed at improving pose estimation accuracy by redistributing errors of pose estimates. Quantitative and qualitative evaluation of the proposed approach on the KITTI Odometry dataset [1] is presented to demonstrate its effectiveness in improving pose estimation accuracy and minimizing pose drift.

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One-shot distributed algorithm for generalized eigenvalue problem

arXiv

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arXiv 2020年

作者： Lyu, Kexin He, Fan Huang, Xiaolin Yang, Jie Chen, Liming Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University MOE Key Laboratory of System Control and Information Processing 800 Dongchuan Road Shanghai200240 China Institute of Medical Robotics Shanghai Jiao Tong University 800 Dongchuan Road Shanghai200240 China Ecole Centrale de Lyon France

Nowadays, more and more datasets are stored in a distributed way for the sake of memory storage or data privacy. The generalized eigenvalue problem (GEP) plays a vital role in a large family of high-dimensional statistical models. However, the existing distributed method for eigenvalue decomposition cannot be applied in GEP for the divergence of the empirical covariance matrix. Here we propose a general distributed GEP framework with one-shot communication for GEP. If the symmetric data covariance has repeated eigenvalues, e.g., in canonical component analysis, we further modify the method for better convergence. The theoretical analysis on approximation error is conducted and the relation to the divergence of the data covariance, the eigenvalues of the empirical data covariance, and the number of local servers is analyzed. Numerical experiments also show the effectiveness of the proposed algorithms. Copyright © 2020, The Authors. All rights reserved.

关键词： Eigenvalues and eigenfunctions

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A Map Accessibility Analysis Algorithm for Mobile Robot Navigation in Outdoor Environment

A Map Accessibility Analysis Algorithm for Mobile Robot Navi...

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IEEE International Conference on Real-time Computing and robotics (RCAR)

作者： Xiangyu Yu Jingchuan Wang Weidong Chen Hesheng Wang Ministry of Education of China Key Laboratory of System Control and Information Processing and Institute of Medical Robotics Shanghai Jiao Tong University and Autonomous Robot Lab Shanghai China

ISBN: (数字)9781728137261

ISBN: (纸本)9781728137278

Autonomous mobile robots have been widely used to perform specific tasks in recent decades. Map accessibility analysis is necessary for those robots to achieve safe and efficient path planning and navigation, especially in outdoor environment where the terrain can be uneven. Researchers have done sufficient works on the influences of different factors. However, integrating these factors is still a great challenge since the information of some constraints are only needed for specific robot tasks. In this paper, we firstly build a Three Dimensional (3D) Occupancy Grid Map (OctoMap) using a 3D Lidar and encoder-based odometer, and then extract different factors from the OctoMap and real-time lidar data. In order to analyze the relationship between map accessibility and variety of constraints, we design an open system and propose a map accessibility analysis algorithm to integrate different factors which have influence on mobile robot navigation. The system and algorithm are demonstrated to be efficient via experiments.

关键词： Three-dimensional displays Mobile robots Laser radar Navigation Task analysis Real-time systems

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Cooperative Pursuit with Multi-Pursuer and One Faster Free-moving Evader

arXiv

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arXiv 2020年

作者： Fang, Xu Wang, Chen Xie, Lihua Chen, Jie School of Electrical and Electronic Engineering Nanyang Technological University Singapore639798 Singapore Robotics Institute Carnegie Mellon University PittsburghPA15213 United States School of Electronics and Information Engineering Tongji University Shanghai200092 China Key Laboratory of Intelligent Control and Decision of Complex System Beijing Institute of Technology Beijing100081 China

—This paper addresses a multi-pursuer single-evader pursuit-evasion game where the free-moving evader moves faster than the pursuers. Most of the existing works impose constraints on the faster evader such as limited moving area and moving direction. When the faster evader is allowed to move freely without any constraint, the main issues are how to form an encirclement to trap the evader into the capture domain, how to balance between forming an encirclement and approaching the faster evader, and what conditions make the capture possible. In this paper, a distributed pursuit algorithm is proposed to enable pursuers to form an encirclement and approach the faster evader. An algorithm that balances between forming an encirclement and approaching the faster evader is proposed. Moreover, sufficient capture conditions are derived based on the initial spatial distribution and the speed ratios of the pursuers and the evader. Simulation and experimental results on ground robots validate the effectiveness and practicability of the proposed method. Copyright © 2020, The Authors. All rights reserved.

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Research on Stereo Vision Technology Based on Improved Region Growing Method

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Journal of Physics: Conference Series 2021年第1期2024卷

作者： Shilong Dai Xinghua Xia Hualiang Zhang School of Information and Control Engineering Shenyang Jianzhu University Shenyang 110168 China. Industrial Control Network and System Laboratory Shenyang Institute of Automation Chinese Academy of Sciences Shenyang 110016 China. Institudes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang 110169 China.

Image segmentation is the cornerstone of image analysis and image processing, its main difficulty is the ill-posedness of image segmentation. The region growing method is the most commonly used method in image segmentation. Its advantages are fast calculation speed, lower algorithm difficulty, and easy understanding. This article uses the area growing algorithm and TOF combined with binocular fusion technology. Discussed how to select the seed points in the region growing method. The algorithm proposed in this paper has high accuracy and high matching quality in the boundary area of the object and the area with large difference in depth. It has both matching time and reliability. It has good results and overcomes the limitations of the active and passive distance methods in the vision system.

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An approach to extract state information from multivariate time series

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Journal of Computers (Taiwan) 2020年第6期31卷 1-11页

作者： Gui, Jun Zheng, Zeyu Qin, Zhaobo Jia, Danping Gao, Yuan Liu, Zhi Yao, Qingfeng Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China University of Chinese Academy of Sciences Beijing100049 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Network Control System Chinese Academy of Sciences Shenyang110016 China School of Information Science and Engineering Shenyang University of Technology Shenyang110870 China

system behaviors could be recorded as multivariate time series by smart sensors. It is challenging to interpret such high-dimensional dataset with a one-dimensional temporal state sequence. In this paper, we propose a new approach DBSE (Distribution-based States Extraction) which is based on statistical and clustering analysis. State extraction problem could be resolved through using distribution parameters to describe each subsequence in multivariate time series and applying clustering method to extract states based on distribution similarity in order to obtain a temporal state sequence and information of each state. We validate DBSE and demonstrate how to use DBSE in real-world by extracting state information from a wearable sensor dataset (PAMAP2_Dataset). By comparing DBSE with TICC (Toeplitz Inverse Covariance-based Clustering) and FCM (Fuzzy C-means Clustering), the new approach is more accurate and effective. Moreover, DBSE is also expected to facilitate future behavior analysis. © 2020 Computer Society of the Republic of China. All rights reserved.

关键词： Extraction

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Model Predictive control with Reduced Integration Step Size of a Switched Reluctance Drive

Model Predictive Control with Reduced Integration Step Size ...

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International Conference on Power Drives systems (ICPDS)

作者： Alecksey Anuchin Valentina Podzorova Mario Hirz Christoph Datlinger Lassi Aarniovuori Viktoriya Popova Galina Demidova Chen Hao Moscow Power Engineering Institute Moscow Russia Institute of Automotive Engineering Graz University of Technology Graz Austria Electrical Drives Laboratory LUT University Lappeenranta Finland Ogarev Mordova State University Saransk Russia Faculty of Control System and Robotics ITMO University Saint-Petersburg Russia School of Electrical and Power Engineering China University of Mining & Technology Xuzhou China

ISBN: (数字)9781728165387

ISBN: (纸本)9781728165394

The problem of torque pulsation and its stabilization by means of control is typical for switched reluctance drives. This drives usually controlled using hysteresis current regulators, which produce audible noise and torque ripple. The method of model predictive control with reduced integration step size was introduced in order to implement precise torque stabilization using pulse width modulation. Active thermal control was developed as well to increase the continuous output power of the drive. The developed control strategy was simulated and shows accurate torque regulation even when only few integration steps inside single PWM cycle are used for duty cycle evaluation. Active thermal control reduced the temperature of the hottest power module by 7.4%, which allows to increase the continuous output power of the drive.

关键词： Torque Multichip modules Switches Pulse width modulation control systems Power generation Predictive control

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Interleaved Boost PFC control strategy based on repetitive control

Interleaved Boost PFC control strategy based on repetitive c...

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International Conference on Electrical Machines and systems (ICEMS)

作者： JiaQi Zheng Tao Zhang Hualiang Zhang Pengpeng Ren Rongkai Su College of Information Science and Engineering Northeastern University Shenyang China Key Laboratory of Network Control System Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9781728133997

In this paper, the working principle of the staggered parallel Boost power factor correction (PFC) circuit is studied. The main circuit adopting the topology of the rectifier device has the following problems: the input current contains higher harmonics, the control system has poor steady-state performance, and the dynamic performance is low. The current loop of the control system adopts the improved repetitive PI double loop control instead of the traditional PI control method to realize the no-difference tracking of the higher harmonics, and to effectively suppress the higher harmonics contained in the output current, so that the control system has good steady-state performance; Combining repetitive control with PI control to effectively compensate for the dynamic performance reduced by the repetitive control. In order to verify the effectiveness of the proposed method, the improved repeated PI double loop control method is compared with the traditional PI control method. The simulation results show that the improved repeated PI double loop control can improve the input current waveform of the rectifier, improve the power factor of the converter, and reduce the THD value of the input current of the rectifier device.

关键词： Voltage control control systems Rectifiers Steady-state Inductors Reactive power Frequency control

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