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Measurement Method of the Least Mean Square Adaptive Filtering Parameters for Gas-solid Flow

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Measurement Method of the Least Mean Square Adaptive Filteri...

Chinese Automation Congress (CAC)

作者： A. Ruidong Xie B. Yingmin Yi C. Haichuan Yang Key Lab of Manufacturing Equipment of Shaanxi Province Xi’an University of Technology Xi’an China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’an China

With regard to the unknown prior knowledge of signal noise in the parameter measurement of gas-solid flow, the present paper puts forward the AC signal processing method based on the least mean square (LMS) adaptive filtering. In addition, the floating processor DSP-TMS320F28335 is employed to collect AC signal of electrostatic sensors and the acquired signals are processed by the aid of LMS adaptive filtering algorithm which adjusts the filter weights according to the criterion of the least mean square error to achieve the optimal filtering. Drawing on the experimental platform, the solid-phase velocity is measured online in real time for the gas-solid flow. The results show that the method can improve the accuracy and stability for the velocity measurement of gas-solid flow.

关键词： Filtering Velocity measurement Filtering algorithms Adaptive filters Solids Atmospheric measurements Particle measurements

A registration-aided domain adaptation network for 3D Point cloud based place recognition

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arXiv 2020年

作者： Qiao, Zhijian Hu, Hanjiang Shi, Weiang Chen, Siyuan Liu, Zhe Wang, Hesheng Department of Automation Insititue of Medical Robotics Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and 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 Department of Mechanical Engineering Carnegie Mellon University United States Department of Computer Science and Technology University of Cambridge United Kingdom

In the field of large-scale SLAM for autonomous driving and mobile robotics, 3D point cloud based place recognition has aroused significant research interest due to its robustness to changing environments with drastic daytime and weather variance. However, it is time-consuming and effort-costly to obtain high-quality point cloud data for place recognition model training and ground truth for registration in the real world. To this end, a novel registration-aided 3D domain adaptation network for point cloud based place recognition is proposed. A structure-aware registration network is introduced to help to learn features with geometric information and a 6-DoFs pose between two point clouds with partial overlap can be estimated. The model is trained through a synthetic virtual LiDAR dataset through GTA-V with diverse weather and daytime conditions and domain adaptation is implemented to the real-world domain by aligning the global features. Our results outperform state-of-the-art 3D place recognition baselines or achieve comparable on the real-world Oxford RobotCar dataset with the visualization of registration on the virtual dataset. Copyright © 2020, The Authors. All rights reserved.

关键词： Three dimensional computer graphics

Learning of long-horizon sparse-reward robotic manipulator tasks with base controllers

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arXiv 2020年

作者： Wang, Guangming Xin, Minjian Wu, Wenhua Liu, Zhe Wang, Hesheng Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai200240 China Department of Computer Science and Engineering University of California San Diego La Jolla CA92093 United States Department of Computer Science and Technology University of Cambridge CambridgeCB2 1TN United Kingdom

Deep Reinforcement Learning (DRL) enables robots to perform some intelligent tasks end-to-end. However, there are still many challenges for long-horizon sparse-reward robotic manipulator tasks. On the one hand, a sparse-reward setting causes exploration inefficient. On the other hand, exploration using physical robots is of high cost and unsafe. In this paper, we propose a method of learning long-horizon sparse-reward tasks utilizing one or more existing traditional controllers named base controllers in this paper. Built upon Deep Deterministic Policy Gradients (DDPG), our algorithm incorporates the existing base controllers into stages of exploration, value learning, and policy update. Furthermore, we present a straightforward way of synthesizing different base controllers to integrate their strengths. Through experiments ranging from stacking blocks to cups, it is demonstrated that the learned state-based or image-based policies steadily outperform base controllers. Compared to previous works of learning from demonstrations, our method improves sample efficiency by orders of magnitude and improves the performance. Overall, our method bears the potential of leveraging existing industrial robot manipulation systems to build more flexible and intelligent controllers. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

Wireless communication using chaotic baseband waveform

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arXiv 2019年

作者： Yao, Jun-Liang Sun, Yu-Zhe Ren, Hai-Peng Grebogi, Celso Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an710048 China Institute for Complex System and Mathematical Biology University of Aberdeen AB24 3UE United Kingdom

Some new properties of the chaotic signal have been implemented in communication system applications recently. They include: (i) chaos is proven to be the optimal communication waveform in the sense of very simple matched filter being used to achieve maximum signal to noise ratio;(ii) the amount of information contained in a chaotic signal is unaltered by a wireless multipath channel;and (iii) chaos property can be used to resist multipath effect. All these support the application of chaos in a practical communication system. However, due to the broadband property of the chaotic signal, it is very difficult for a practical transducer or antenna to convert such a broadband signal into a signal that would be suitable for practical band-limited wireless channel. Thus, the use of chaos property to improve the performance of conventional communication system without changing the system configuration becomes a critical issue in communication with chaos. In this paper, chaotic baseband waveform generated by a chaotic shaping filter is used to show that this difficulty can be overcome. The generated continuous-time chaotic waveform is proven to be topologically conjugate to a symbolic sequence, allowing the encoding of arbitrary information sequence into the chaotic waveform. A finite impulse response filter is used to replace the impulse control in order to encode information into the chaotic signal, simplifying the algorithm for high speed communication. A wireless communication system is being proposed using the chaotic signal as the baseband waveform, which is compatible with the general wireless communication platform. The matched filter and decoding method, using chaos properties, enhance the communication system performance. The Bit Error Rate (BER) and computational complexity performances of the proposed wireless communication system are analyzed and compared with the conventional wireless systems. The results show that the proposed chaotic baseband waveform of our

关键词： Bit error rate

A distributed alternating direction method of multipliers algorithm for consensus optimization

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A distributed alternating direction method of multipliers al...

Chinese Automation Congress (CAC)

作者： Xia Zhang Ding Liu Fei Yu Duqiao Zhao National and Local Joint Engineering Research Center of Crystal Growth Equipment and System Integration Xian University of Technology Xian China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xian China

The Alternating Directions Methods of Multipliers (ADMM) are widely used in many fields of scientific computing in recent years. This method applies iterative computation to the information exchange between individual agent and neighbor. However, despite the success of traditional centralized ADMM in some application environments, its applicability is limited in global convergence center by its communication requirements. In our paper, we provide the linear convergence rate for this distributed consensus optimization problem, which satisfies strongly convex local objective functions. Then, the properties of the local objective function and the parameters of the algorithm, the theoretical convergence rate is given according to the network topology.

关键词： Convergence Optimization Linear programming Convex functions Numerical models Signal processing algorithms information processing

Anchor-based spatio-temporal attention 3D convolutional networks for dynamic 3D point cloud sequences

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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

Synchronization stability and circuit experiment of hyperchaos with time delay using impulse control

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arXiv 2019年

作者： Ren, Hai-Peng Tian, Kun Bai, Chao Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xian University of Technology Xian710048 China School of Mechatronics Engineering Xi’an Technological University Xian710048 China

Secure communication using hyperchaos has a better potential performance, but hyperchaotic impulse circuits synchronization is a challenging task. In this paper, an impulse control method is proposed for the synchronization of two hyperchaotic Chen circuits. The sufficient conditions for the synchronization of hyperchaotic systems using the impulse control are given. The upper bound of the impulse interval is derived to assure the synchronization error system to be asymptotically stable. Simulation and circuit experiment show the correctness of the analysis and feasibility of the proposed method. Copyright © 2019, The Authors. All rights reserved.

关键词： Synchronization

End-to-End 3D Point Cloud Learning for Registration Task Using Virtual Correspondences

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End-to-End 3D Point Cloud Learning for Registration Task Usi...

2020 IEEE/RSJ International Conference on intelligent Robots and systems (IROS)

作者： Huanshu Wei Zhijian Qiao Zhe Liu Chuanzhe Suo Peng Yin Yueling Shen Haoang Li Hesheng Wang The Chinese University of Hong Kong T Stone Robotics Institute Hong Kong Shanghai Jiao Tong University China University of Cambridge United Kingdom Carnegie Mellon University Insititue of Medical Robotics Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Jiao Tong University Shanghai China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology China

ISBN: (数字)9781728162126

ISBN: (纸本)9781728162133

3D Point cloud registration is still a very challenging topic due to the difficulty in finding the rigid transformation between two point clouds with partial correspondences, and it's even harder in the absence of any initial estimation information. In this paper, we present an end-to-end deep-learning based approach to resolve the point cloud registration problem. Firstly, the revised LPD-Net is introduced to extract features and aggregate them with the graph network. Secondly, the self-attention mechanism is utilized to enhance the structure information in the point cloud and the cross-attention mechanism is designed to enhance the corresponding information between the two input point clouds. Based on which, the virtual corresponding points can be generated by a soft pointer based method, and finally, the point cloud registration problem can be solved by implementing the SVD method. Comparison results in ModelNet40 dataset validate that the proposed approach reaches the state-of-the-art in point cloud registration tasks and experiment resutls in KITTI dataset validate the effectiveness of the proposed approach in real applications.

关键词： Three-dimensional displays Aggregates Pose estimation Feature extraction Task analysis intelligent robots

Robotic Electrospinning Actuated by Non-Circular Joint Continuum Manipulator for Endoluminal Therapy

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Robotic Electrospinning Actuated by Non-Circular Joint Conti...

IEEE International Conference on Robotics and Automation (ICRA)

作者： Zicong Wu Chuqian Lou Zhu Jin Shaoping Huang Ning Liu Yun Zou Mirko Kovac Anzhu Gao Guang-Zhong Yang Shanghai Jiao Tong University Shanghai P. R. China Imperial College London and Empa-Swiss Federal Laboratories for Materials Science and Technology Precision Robotics (Hong Kong) Limited Hong Kong China Aerial Robotics Laboratory Imperial College London London United Kingdom Shanghai Jiao Tong University and the Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China Institute of Medical Robotics Shanghai Jiao Tong University Shanghai P. R. China

Electrospinning has exhibited excellent benefits to treat the trauma for tissue engineering due to its produced micro/nano fibrous structure. It can effectively adhere to the tissue surface for long-term continuous therapy. This paper develops a robotic electrospinning platform for endoluminal therapy. The platform consists of a continuum manipulator, the electrospinning device, and the actuation unit. The continuum manipulator has two bending sections to facilitate the steering of the tip needle for a controllable spinning direction. Non-circular joint profile is carefully designed to enable a constant length of the centreline of a continuum manipulator for stable fluid transmission inside it. Experiments are performed on a bronchus phantom, and the steering ability and bending limitation in each direction are also investigated. The endoluminal electrospinning is also fulfilled by a trajectory following and points targeting experiments. The effective adhesive area of the produced fibre is also illustrated. The proposed robotic electrospinning shows its feasibility to precisely spread more therapeutic drug to construct fibrous structure for potential endoluminal treatments.

关键词： Drugs Electric potential Fluids Tissue engineering Medical treatment Bending Manipulators