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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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A Piecewise-Markovian Lyapunov Approach to Reliable Output Feedback control for Fuzzy-Affine Systems With Time-Delays and Actuator Faults

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IEEE TRANSACTIONS ON CYBERNETICS 2018年第9期48卷 2723-2735页

作者： Wei, Yanling Qiu, Jianbin Shi, Peng Wu, Ligang State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin China School of Electrical and Electronic Engineering University of Adelaide Adelaide SA Australia Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin China

This paper addresses the problem of delay-dependent robust and reliable H-infinity static output feedback (SOF) control for a class of uncertain discrete-time Takagi-Sugeno fuzzy-affine (FA) systems with time-varying delay and actuator faults in a singular system framework. The Markov chain is employed to describe the actuator faults behaviors. In particular, by utilizing a system augmentation approach, the conventional closed-loop system is converted into a singular FA system. By constructing a piecewise-Markovian Lyapunov-Krasovskii functional, a new H-infinity performance analysis criterion is then presented, where a novel summation inequality and S-procedure are succeedingly employed. Subsequently, thanks to the special structure of the singular system formulation, the piecewise-affine SOF controller design is proposed via a convex program. Lastly, illustrative examples are given to show the efficacy and less conservatism of the presented approach.

关键词： Piecewise Markovian Lyapunov functional reliable output feedback control time-varying delay T-S fuzzy-affine (FA) systems

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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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Compliant net for AUV retrieval using a UAV

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IFAC-PapersOnLine 2018年第29期51卷 431-437页

作者： Ivanovic, Antun Polic, Marsela Salah, Omar Orsag, Matko Bogdan, Stjepan Laboratory for Robotics and Intelligent Control Systems Faculty of Electrical Engineering and Computing University of Zagreb Unska 3 Zagreb10 000 Croatia Mechanical Engineering Department Faculty of Engineering Assiut University Assiut Egypt

This paper addresses the problem of retrieving an autonomous underwater vehicle (AUV) with an unmanned aerial vehicle (UAV). The design of the compliant net that naturally adapts to the payload morphology is proposed as a retrieval tool. We constructed the proposed compliant net and conducted experiments with an underwater vehicle model as payload. Furthermore, mathematical model of a system comprised of the UAV and the payload is presented and a mid-ranging controller is utilized for such a system. We also present simulation results and analyze the stability of the UAV with the cable attached payload. © 2018

关键词： Autonomous underwater vehicles Antennas Unmanned aerial vehicles (UAV) Autonomous underwater vehicles (AUV) Marine systems Mechanical design Retrieval tool Soft robotics Underwater vehicles

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A Grasping CNN with Image Segmentation for Mobile Manipulating Robot

A Grasping CNN with Image Segmentation for Mobile Manipulati...

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IEEE International Conference on robotics and Biomimetics

作者： Yingying Yu Zhiqiang Cao Shuang Liang Zhicheng Liu Junzhi Yu Xuechao Chen State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing China Beijing Advanced Innovation Center for Intelligent Robots and Systems Intelligent Robotics Institute BIT Beijing China

This paper presents a grasping convolutional neural network with image segmentation for mobile manipulating robot. The proposed method is cascaded by a feature pyramid network FPN and a grasping network DrGNet. The FPN network combined with point cloud clustering is used to obtain the mask of the target object. Then, the grayscale map and the depth map corresponding to the target object are combined and sent to the DrGNet network for providing multi-scale images. On this basis, depthwise separable convolution is used for encoding. The results of encoders are refined according to the light-weight RefineNet as well as sSE, which can achieve a better grasp detection. The proposed method is verified by the experiments on mobile manipulating robot.

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Seeking the Analytical Approximation of the Stance Dynamics of the 3D Spring-Loaded Inverted Pendulum Model By Using Perturbation Approach

Seeking the Analytical Approximation of the Stance Dynamics ...

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2019 IEEE/RSJ International Conference on intelligent Robots and Systems (IROS)

作者： Haitao Yu Shengjun Wang Kaizheng Shan Jun Li Lixian Zhang Haibo Gao State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin China Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin China

The Spring-Loaded Inverted Pendulum (SLIP) has been widely exploited in both biomechanical and robotics research due to its simple form in mathematics and high accuracy in fitting experimental biology data. However the intrinsic nonlinearity of the SLIP dynamics makes accurate analytical representation unavailable. Traditional methods take advantage of numerical integration to handle this issue while several existing analytical approximations focusing on 2D-SLIP model. The 3D-SLIP suitable to physical reality is rarely investigated. This paper presents a novel perturbation-based approach to obtain the closed-form analytical approximations of the 3D-SLIP model in stance phase. In contrast to existing work ignoring the gravitational forces, the proposed approach just relies on assumptions of small leg compression and small leg swept angle. The performance of the derived approximations has been evaluated via comprehensive numerical analysis. The quality of accurate apex prediction promises the approximation as an advantageous and reliable tool for locomotion control of legged robots.

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Efficient dense frontier detection for 2D graph SLAM based on occupancy grid submaps

arXiv

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arXiv 2019年

作者： Oršulić, Juraj Miklić, Damjan Kovačić, Zdenko Faculty of Electrical Engineering and Computing Laboratory for Robotics and Intelligent Control Systems University of Zagreb Croatia RoMb Technologies d.o.o. Zagreb Croatia

In autonomous robot exploration, the frontier is the border in the world map dividing the explored and unexplored space. The frontier plays an important role when deciding where in the environment the robots should go explore next. We consider a modular control system pipeline for autonomous exploration where a 2D graph SLAM algorithm based on occupancy grid submaps performs map building and localization, and frontier detection is one of key system components. We provide an overview of the state of the art in frontier detection and the relevant SLAM concepts and propose a fast specialized frontier detection method which is efficiently constrained to active submaps, yet robust to graph SLAM loop closures. Copyright © 2019, The Authors. All rights reserved.

关键词： Computer vision

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Autonomous docking and energy sharing between two types of robotic agents

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IFAC-PapersOnLine 2018年第29期51卷 406-411页

作者： Babi, Anja Mandi, Filip Vasiljevi, Goran Mikovi, Nikola Faculty of Electrical Engineering and Computing LABUST Laboratory for Underwater Systems and Technologies University of Zagreb Unska 3 Zagreb Croatia Faculty of Electrical Engineering and Computing LARICS Laboratory for Robotics and Intelligent Control Systems University of Zagreb Unska 3 Zagreb Croatia

In order to achieve long-term autonomy after deployment, a method of energy sharing within a heterogeneous swarm of marine robots was devised and implemented. This paper describes hardware and software solutions as well as guidance and control algorithms developed with the goal of allowing powerful energy-rich overactuated surface platforms to approach, dock, and wirelessly recharge less mobile sensor-rich agents upon request using visual servoing. The basic image processing algorithm uses data acquired by the Microsoft Kinect sensor alternating between RGB and infra-red camera modes, depending on lighting conditions. The full search and docking procedure developed for long-term operation is described. The results of a series of experiments done both in an indoor testbed and in realistic outdoor conditions are discussed and some goals for future work are given. © 2018

关键词： Autonomous agents

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Modeling and analysis of colored petri net based on the semi-tensor product of matrices

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Science China(Information Sciences) 2018年第1期61卷 70-85页

作者： Jiantao ZHAO Zengqiang CHEN Zhongxin LIU College of Computer and Control Engineering Nankai University Key Laboratory of Intelligent Robotics of Tianjin Nankai University

This paper applies the model petri net method based on the semi-tensor product of matrices to colored petri net. Firstly, we establish the marking evolution equation for colored petri net by using the semitensor product of matrices. Then we define the concept of controllability and the control-marking adjacency matrix for colored petri net. Based on the marking evolution equation and control-marking adjacency matrix, we give the necessary and sufficient condition of reachability and controllability for colored petri net. The algorithm to verify the reachability of colored petri net is given, and we analyze the computational complexity of the algorithm. Finally, an example is given to illustrate the effectiveness of the proposed theory. The significance of the paper lies in the application of the model petri net method based on the semitensor product of matrices to colored petri net. This is a convenient way of verifying whether one marking is reachable from another one as well as finding all firing sequences between any two reachable ***, the method lays the foundations for the analysis of other properties of colored petri net.

关键词： colored petri net reachability controllability marking evolution equation semi-tensor product of matrices

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Multivariate Time Series Forecast in Industrial Process Based on XGBoost and GRU

Multivariate Time Series Forecast in Industrial Process Base...

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IEEE Joint International Information Technology and Artificial Intelligence Conference (ITAIC)

作者： Naiju Zhai Peifu Yao Xiaofeng Zhou Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China China Copper Co.LTD Kunming China

ISBN: (数字)9781728152448

ISBN: (纸本)9781728152455

In this paper, a time series prediction model that merges eXtreme Gradient Boosting (XGBoost) and Gate Recurrent Unit (GRU), XGB-GRU model, is proposed for multivariate time series prediction in industry. The XGB-GRU model uses XGBoost's strong feature extraction capabilities to extract the hidden information of multiple control variables in industrial data. Next, the model uses GRU's unique gating unit to extract the timing information in the industrial data. Finally, the importance of XGBoost output variables to guide actual production and solve the problem of inexplicability of neural networks. Predicting the temperature of the heating furnace verifies that the proposed XGB-GRU is better than a single XGBoost and GRU model. And the model has a good fit to the predicted value.

关键词： Heating systems Furnaces Time series analysis Predictive models Feature extraction Timing Data mining

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