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A-STC: auction-based spanning tree coverage algorithm for motion planning of cooperative robots

Frontiers of Information Technology & Electronic Engineering 2019年第1期20卷 18-31页

作者： Guan-qiang GAO Bin XIN School of Automation Beijing Institute of Technology Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Advanced Innovation Center for Intelligent Robots and Systems

The multi-robot coverage motion planning(MCMP) problem in which every reachable area must be covered is common in multi-robot systems. To deal with the MCMP problem, we propose an efficient, complete, and off-line algorithm, named the "auction-based spanning tree coverage(A-STC)" algorithm. First, the configuration space is divided into mega cells whose size is twice the minimum coverage range of a robot. Based on connection relationships among mega cells, a graph structure can be obtained. A robot that circumnavigates a spanning tree of the graph can generate a coverage trajectory. Then, the proposed algorithm adopts an auction mechanism to construct one spanning tree for each robot. In this mechanism, an auctioneer robot chooses a suitable vertex of the graph as an auction item from neighboring vertexes of its spanning tree by heuristic rules. A bidder robot submits a proper bid to the auctioneer according to the auction vertexes' relationships with the spanning tree of the robot and the estimated length of its trajectory. The estimated length is calculated based on vertexes and edges in the spanning tree. The bidder with the highest bid is selected as a winner to reduce the makespan of the coverage task. After auction processes, acceptable coverage trajectories can be planned rapidly. Computational experiments validate the effectiveness of the proposed MCMP algorithm and the method for estimating trajectory lengths. The proposed algorithm is also compared with the state-of-the-art algorithms. The comparative results show that the A-STC algorithm has apparent advantages in terms of the running time and the makespan for large crowded configuration spaces.

关键词： Coverage motion planning Multi-robot system Auction algorithm Spanning tree coverage algorithm

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Distributed Containment control of Markov Jump Multi-agent systems under Asynchronous Communications

Distributed Containment Control of Markov Jump Multi-agent S...

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第40届中国控制会议

作者： Chao Ma Yidao Ji Hang Fu Wei Wu School of Mechanical Engineering University of Science and Technology Beijing School of Automation and Electrical Engineering University of Science and Technology Beijing State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences

This paper studies the distributed containment control issue for general Markov jump multi-agent systems. More specifically, a novel asynchronous mode-dependent communication scheme is proposed, which is based on observed communication mode information. By means of mode-dependent Lyapunov method, sufficient containment conditions are derived and communication protocol gains are further designed by convex optimization. A simulation example is finally presented to validate the correctness of our developed control scheme.

关键词： Distributed containment Markov jump multi-agent systems asynchronous communications

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Real-Time Marker Localization Learning for GelStereo Tactile Sensing

Real-Time Marker Localization Learning for GelStereo Tactile...

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IEEE International Conference on Robotics and Biomimetics

作者： Shujuan Liu Shaowei Cui Chaofan Zhang Yinghao Cai Shuo Wang University of Science and Technology Beijing 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 Institute of Automation Chinese Academy of Sciences Beijing China Center for Excellence in Brain Science Intelligence Technology Chinese Academy of Sciences Shanghai China

ISBN: (纸本)9781665481106

Visuotactile sensing technology is becoming more popular in tactile sensing, but the effectiveness of the existing marker detection/localization methods remains to be further explored. Instead of contour-based blob detection, this paper presents a learning-based marker localization network for GelStereo visuotactile sensing called Marknet. Specifically, the Marknet presents a grid regression architecture to incorporate the distribution of the GelStereo markers. Furthermore, a marker rationality evaluator (MRE) is modelled to screen suitable prediction results. The experimental results show that the Marknet combined with MRE achieves 93.90% precision for irregular markers in contact areas, which outperforms the traditional contour-based blob detection method by a large margin of 42.32%. Meanwhile, the proposed learning-based marker localization method can achieve better realtime performance beyond the blob detection interface provided by the OpenCV library through GPU acceleration, which we believe will lead to considerable perceptual sensitivity gains in various robotic manipulation tasks.

关键词： Location awareness Sensitivity Object detection Network architecture Predictive models Robot sensing systems Real-time systems

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Blind Spectrum Sensing Based on the Statistical Covariance Matrix and K-Median Clustering Algorithm 6th

Blind Spectrum Sensing Based on the Statistical Covariance M...

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6th International Conference on Artificial Intelligence and Security, ICAIS 2020

作者： Zhuang, Jiawei Wang, Yonghua Wan, Pin Zhang, Shunchao Zhang, Yongwei Li, Yi School of Automation Guangdong University of Technology Guangzhou510006 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Hubei Key Laboratory of Intelligent Wireless Communications South-Central University for Nationalities Wuhan430074 China

ISBN: (纸本)9783030578831

Spectrum sensing is a fundamental function for cognitive radio systems, which can improve spectrum utilization. In this article, a blind spectrum sensing method based on the sample covariance matrix and K-median clustering algorithm is proposed to further improve the sensing performance. Specifically, to obtain a two-dimensional signal feature vector, the received signal matrix is rebuilt into two sub-matrices by the decomposition and reorganization (DAR) method. Moreover, two statistical covariance matrices are constructed by the sub-matrices, respectively. The ratios between the sum of some elements from the sample covariance matrices and the sum of diagonal elements from those matrices are used as a signal feature vector. It is demonstrated that the new signal feature vector and the feature vector based on the improved covariance absolute value method are equivalent. Furthermore, K-median clustering algorithm is trained by signal feature vectors to obtain a classifier. Indeed, this classifier can directly be used to detect whether the PU signal is absent or not. Simulation results report that the proposed algorithm has better sensing performance than some popular sensing algorithms based on random matrix theory or information geometry. © 2020, Springer Nature Switzerland AG.

关键词： Covariance matrix

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A Unified Weight Initialization Paradigm for Tensorial Convolutional Neural Networks

arXiv

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arXiv 2022年

作者： Pan, Yu Su, Zeyong Liu, Ao Wang, Jingquan Li, Nannan Xu, Zenglin Harbin Institute of Technology Shenzhen Shenzhen China University of Electronic Science and Technology of China Chengdu China Tokyo Institute of Technology Tokyo Japan 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 Pengcheng Laboratory Shenzhen China

Tensorial Convolutional Neural Networks (TCNNs) have attracted much research attention for their power in reducing model parameters or enhancing the generalization ability. However, exploration of TCNNs is hindered even from weight initialization methods. To be specific, general initialization methods, such as Xavier or Kaiming initialization, usually fail to generate appropriate weights for TCNNs. Meanwhile, although there are ad-hoc approaches for specific architectures (e.g., Tensor Ring Nets), they are not applicable to TCNNs with other tensor decomposition methods (e.g., CP or Tucker decomposition). To address this problem, we propose a universal weight initialization paradigm, which generalizes Xavier and Kaiming methods and can be widely applicable to arbitrary TCNNs. Specifically, we first present the Reproducing Transformation to convert the backward process in TCNNs to an equivalent convolution process. Then, based on the convolution operators in the forward and backward processes, we build a unified paradigm to control the variance of features and gradients in TCNNs. Thus, we can derive fan-in and fan-out initialization for various TCNNs. We demonstrate that our paradigm can stabilize the training of TCNNs, leading to faster convergence and better results. Copyright © 2022, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Recurrent Attentional Reinforcement Learning for Fault Diagnosis of Hydraulic System

Recurrent Attentional Reinforcement Learning for Fault Diagn...

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International Conference on control, Decision and Information Technologies (CoDIT)

作者： Zhenhui Tang Ru Liu Kang Lou Chaojian Gao Jingcheng Wang SJTU Sanya Yazhou Bay Institute of Deepsea Science and Technology Sanya China Department of Automation Shanghai Jiao Tong University Shanghai China Shanghai Marine Equipment Research Institute Shanghai China Department of Automation the Key Laboratory of System Control and Information Processing Ministry of Education of China and the Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China Autonomous Systems and Intelligent Control International Joint Research Center Xi'an Technological University Xi'an China

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

Recognizing fault types of machinery system is a fundamental but challenging task in industrial application. Although data-driven fault diagnosis method attains remarkable progress by learning fault features automatically, the existing data-driven fault diagnosis pipelines learn non-discriminative feature. To deal with these problems, a new data-driven fault diagnosis method with recurrent attentional module is proposed to find a sequence of informative features by localizing distinguishing time fragments about each individual faults iteratively. The contextual sequence dependency between the localized time fragments is enhanced. Then, the fault types are further predicted on localized time fragments. The proposed framework also explicitly models long-term dependencies among these attentional time fragments to capture historical fault information. Experiments on hydraulic system show that the proposed framework achieves superior diagnosis performance and computation efficiency.

关键词： Fault diagnosis Location awareness Accuracy Computational modeling Pipelines Reinforcement learning Hydraulic systems Computational efficiency Information technology

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An Intelligent Maritime Scene Frame Prediction Based on Digital Twins Technology

An Intelligent Maritime Scene Frame Prediction Based on Digi...

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Digital Twins and Parallel Intelligence (DTPI), IEEE International Conference on

作者： Zhong-Zheng Guo Kai Wang Jiang-Yi Qin Xian-Bin Li De-Chao Ran Yu Shen Unmanned Systems Research Center National Innovation Institute of Defense Technology Academy of Military Science Beijing China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

This paper proposes a scene frame prediction system based on digital twin technology. The system is mainly composed of an unmanned perception module, a communication link module, a command center module that provides intelligent computing services, and an interaction and visualization module for human operators. These modules together constitute a system for predicting and recovery the time-series matrix composed of scene frames and object frames. At the same time, in view of the current problems of lack of real-time information and long response time in the maritime search and rescue (SAR) field, this paper instantiates the above system to improve the existing SAR system. Satellites and unmanned vehicles perceive part of the scene data of the real cyber-physical system in real-time and divide them into multiple frames according to the timestamps. In addition, this paper also proposes an environmental graph-attention network (Env-GAT) to achieve efficient and reliable frame predicting. Finally, this paper verifies that the performance of the spatiotemporal attention graph network model has reached the optimal level through computational experiments.

关键词： Satellites Digital twin Conferences Unmanned vehicles Search problems Real-time systems Trajectory

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Detection of stealthy false data injection attacks against networked control systems via active data modification

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Information Sciences 2021年 546卷 192-205页

作者： Zhong-Hua Pang Lan-Zhi Fan Jian Sun Kun Liu Guo-Ping Liu Key Laboratory of Fieldbus Technology and Automation of Beijing North China University of Technology Beijing 100144 China State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing 100081 China Department of Artificial Intelligence and Automation Wuhan University Wuhan 430072 China

This paper investigates the design and detection problems of stealthy false data injection (FDI) attacks against networked control systems from the different perspectives of an attacker and a defender, respectively. First, a Kalman filter-based output tracking control system is presented, where stealthy FDI attacks are designed for its feedback and forward channels so as to destroy the system performance while bypassing a traditional residual-based detector. Second, to successfully detect such two-channel stealthy attacks, an active data modification scheme is proposed, by which the measurement and control data are amended before transmitting them through communication networks. Theoretical analysis is then carried out for both ideal and practical cases to evaluate the effectiveness of the detection scheme. An interesting finding is that the attacks designed based on a false model obtained from those modified data can remain stealthy. Finally, simulation results are provided to validate the proposed attack design and detection schemes.

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Stochastic DoS Attack Allocation Against Collaborative Estimation in Sensor Networks

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IEEE/CAA Journal of Automatica Sinica 2020年第5期7卷 1225-1234页

作者： Ya Zhang Lishuang Du Frank L.Lewis School of Automation Southeast Universityand also with the Key Laboratory of Measurement and Control of Complex Systems of EngineeringMinistry of EducationNanjing 210096China University of Texas at Arlington Research Institute Fort WorthTX 76118 USA IEEE

In this paper,denial of service(DoS)attack management for destroying the collaborative estimation in sensor networks and minimizing attack energy from the attacker perspective is *** the communication channels between sensors and a remote estimator,the attacker chooses some channels to randomly jam DoS attacks to make their packets randomly dropped.A stochastic power allocation approach composed of three steps is ***,the minimum number of channels and the channel set to be attacked are ***,a necessary condition and a sufficient condition on the packet loss probabilities of the channels in the attack set are provided for general and special systems,***,by converting the original coupling nonlinear programming problem to a linear programming problem,a method of searching attack probabilities and power to minimize the attack energy is *** effectiveness of the proposed scheme is verified by simulation examples.

关键词： Attack allocation denial of service(DoS)attack packet loss remote estimation sensor networks

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High-Order Disturbance Observer-Based Attitude control with Prescribed Performance for Hypersonic Vehicle

High-Order Disturbance Observer-Based Attitude Control with ...

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第40届中国控制会议

作者： GuangHui Yang Xinming Wang Beijing Aerospace Automatic Control Institute School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Eduaction

This paper presents a high-order disturbance observer-based attitude tracking controller design with prescribed performance for hypersonic vehicle. The lumped disturbances caused by parameters perturbations, external disturbances and unmodelled dynamics are estimated accurately by utilizing high-order disturbance observers. Based on the disturbances estimation,a composite attitude tracking controller with guaranteeing prescribed transient and steady performance is proposed and the stability analysis of the closed-loop system is rigorously analyzed using the Lyapunov technique. The proposed controller not only ensures the tracking errors are within the desired performance envelope but also considering the disturbance rejection. Finally,the effectiveness of the proposed method is verified by simulations.

关键词： Hypersonic Vehicle Disturbance Observer Prescribed Performance control Dynamics Surface control

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