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IEEE International Conference on robotics and Automation (ICRA)

作者： Hu Cao Guang Chen Zhijun Li Jianjie Lin Alois Knoll Chair of Robotics Artificial Intelligence and Real-Time Systems Technische Universität München München Germany State Key Laboratory of Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Tongji University Shanghai China University of Science and Technology of China China

This paper proposes an efficient, fully convolutional neural network to generate robotic grasps by using 300×300 depth images as input. Specifically, a residual squeeze-and-excitation network (RSEN) is introduced for deep feature extraction. Following the RSEN block, a multi-scale spatial pyramid module (MSSPM) is developed to obtain multi-scale contextual information. The outputs of each RSEN block and MSSPM are combined as inputs for hierarchical feature fusion. Then, the fused global features are upsampled to perform pixel-wise learning for grasping pose estimation. The experimental results on Cornell and Jacquard grasping datasets indicate that the proposed method has a fast inference speed of 5ms while achieving high grasp detection accuracy of 96.4% and 94.8% on Cornell and Jacquard, respectively, which strikes a balance between accuracy and running speed. Our method also gets a 90% physical grasp success rate with a UR5 robot arm.

关键词： Automation Conferences Pose estimation Grasping Manipulators Feature extraction Inference algorithms

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A Non-Intrusive Appliances Load Monitoring Method Based on Hourly Smart Meter Data

A Non-Intrusive Appliances Load Monitoring Method Based on H...

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作者： Chunhe Song Zhongfeng Wang Shuji Liu Libo Xu Dapeng Zhou Peng Zeng Key Laboratory of Networked Control Systems Shenyang Institute of automationChinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences State Grid Liaoning Electric Power Co. Ltd.

Peak load management is very important for the electric power *** paper analyzes the impact of residential swimming pool pumps(RSPPs) on the peak *** this paper analyzes the challenges of non-intrusive energy consumption estimation for ***,a novel reference based change-point(RCP) model is proposed for non-intrusive SPPs energy consumption *** advantages of the proposed RCP model are that it does not require high sampling rate data or prior information of the *** show that during pool season,under the assumption that the ratio of base loads(defined as the power consumption which is independent of the outdoor temperature) of houses with and with PPs remains the same during no pool season and pool season,6.3%of the total energy is consumed by PPs,while under the assumption that for houses with and without PPs,the ratio of base loads is equal to the ratio of the temperature-dependent power consumption during pool season,9.08% of the total energy is consumed by ***,we show that by shifting PPs activity period,under the first assumption,at least 1.27% of peak demand can be reduced,while under the second assumption,at least 4.53% of peak demand can be reduced.

关键词： Smart grid Non-intrusive Appliances Load Monitoring Peak Load

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Urban Digital Twins for Intelligent Road Inspection

Urban Digital Twins for Intelligent Road Inspection

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IEEE International Conference on Big Data

作者： Rui Fan Yikang Zhang Sicen Guo Jiahang Li Yi Feng Shuai Su Yanting Zhang Wenshuo Wang Yu Jiang Mohammud Junaid Bocus Xingyi Zhu Qijun Chen the Department of Control Science & Engineering Frontiers Science Center for Intelligent Autonomous Systems and State Key Laboratory of Intelligent Autonomous Systems Robotics & Artificial Intelligence Laboratory (RAIL) Tongji University Shanghai P. R. China Department of Computer Science & Technology Donghua University Shanghai P. R. China Department of Civil Engineering McGill University Montreal Canada CTO office at ClearMotion Billerica MA USA Department of Electrical & Electronic Engineering the University of Bristol BS8 1UB U.K Key Laboratory of Road and Traffic Engineering of Ministry of Education Tongji University Shanghai P. R. China

ISBN: (纸本)9781665480468

Urban digital twin (UDT) technologies offer new opportunities for intelligent road inspection (IRI). This paper first reviews the state-of-the-art algorithms used in the two key components of UDT-based IRI systems: (1) multi-temporal, multi-dimension, multi-score, and heterogeneous road data acquisition, and (2) road distress detection. This paper then summarizes the UDTIRI competition, organized in conjunction with IEEE Bigdata 2022. More details on our competition are available at ***/view/udtiri-workshop/bigdata-2022.

关键词： Roads Data acquisition Inspection Big Data Rough surfaces Digital twins

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SHUYU Robot:An Automatic Rapid Temperature Screening System

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Chinese Journal of Mechanical Engineering 2020年第2期33卷 1-4页

作者： Zhao Gong Songwen Jiang Qizhi Meng Yanlei Ye Peng Li Fugui Xie Huichan Zhao Chunzhe Lv Xiaojie Wang Xinjun Liu The State Key Laboratory of Tribology&Tsinghua University(DME)‑Siemens Joint Research Center for Advanced Robotics Department of Mechanical Engineering(DME)Tsinghua UniversityBeijing 100084China Beijing Key Lab of Precision/Ultra‑precision Manufacturing Equipments and Control Tsinghua UniversityBeijing 100084China Yantai Tsingke+Robot Joint Research Institute Co. Ltd.Yantai 264000ShandongChina The First Affiliated Hospital of Tsinghua University Beijing 100016China

The health of people around the world and the global economy are under substantial threat from the outbreak of pandemics[1].controlling pandemics is extremely challenging,with preventing the spread of pathogens the most important and critical *** all preventative actions,body temperature screening is undoubtedly highly necessary and effective[2].

关键词： Robot doubt critical

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DTCLMapper: Dual Temporal Consistent Learning for Vectorized HD Map Construction

arXiv

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arXiv 2024年

作者： Li, Siyu Lin, Jiacheng Shi, Hao Zhang, Jiaming Wang, Song Yao, You Li, Zhiyong Yang, Kailun The School of Robotics The National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha410082 China The College of Computer Science and Electronic Engineering Hunan University Changsha410082 China The State Key Laboratory of Extreme Photonics and Instrumentation The National Engineering Research Center of Optical Instrumentation Zhejiang University Hangzhou310027 China The Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe76131 Germany The College of Computer Science Zhejiang University Hangzhou310027 China The USC Viterbi School of Engineering The University of Southern California Los AngelesCA90089 United States

Temporal information plays a pivotal role in Bird’s-Eye-View (BEV) driving scene understanding, which can alleviate the visual information sparsity. However, the indiscriminate temporal fusion method will cause the barrier of feature redundancy when constructing vectorized High-Definition (HD) maps. In this paper, we revisit the temporal fusion of vectorized HD maps, focusing on temporal instance consistency and temporal map consistency learning. To improve the representation of instances in single-frame maps, we introduce a novel method, DTCLMapper. This approach uses a dual-stream temporal consistency learning module that combines instance embedding with geometry maps. In the instance embedding component, our approach integrates temporal Instance Consistency Learning (ICL), ensuring consistency from vector points and instance features aggregated from points. A vectorized points pre-selection module is employed to enhance the regression efficiency of vector points from each instance. Then aggregated instance features obtained from the vectorized points preselection module are grounded in contrastive learning to realize temporal consistency, where positive and negative samples are selected based on position and semantic information. The geometry mapping component introduces Map Consistency Learning (MCL) designed with self-supervised learning. The MCL enhances the generalization capability of our consistent learning approach by concentrating on the global location and distribution constraints of the instances. Extensive experiments on well-recognized benchmarks indicate that the proposed DTCLMapper achieves state-of-the-art performance in vectorized mapping tasks, reaching 61.9% and 65.1% mAP scores on the nuScenes and Argoverse datasets, respectively. The source code is available at https://***/lynn-yu/DTCLMapper. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Stiffness Estimation and Intention Detection for Human-Robot Collaboration

Stiffness Estimation and Intention Detection for Human-Robot...

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Xiongjun Chen Yiming Jiang Chenguang Yang Key Laboratory of Autonomous Systems and Networked Control College of Automation Science and Engineering South China University of Technology Guangzhou China National Engineering Laboratory for Robot Visual Perception and Control Hunan University Changsha China Bristol Robotics Laboratory University of the West of England Bristol UK

ISBN: (数字)9781728151694

ISBN: (纸本)9781728151700

In this paper, we propose a stiffness estimation and intention detection method for human-robot collaboration. The human arm endpoint stiffness can be obtained according to the muscle activation levels of the upper arm and the human arm configurations. The estimated endpoint stiffness of human arm is matching to the robot arm joint stiffness through an appropriate mapping. The motion intention of human arm is detected based on the wrist configuration which is recognized by a Myo armband attached at the forearm of the operator. In order to reduce the time of feature engineering to ensure the performance of real-time collaboration, the wrist configuration recognition is realised based on the neural learning algorithm. The sEMG of the human forearm is directly fed into the neural network after processing by filters and sliding windows. The force sensor at the end of the robot arm is embedded in the feedback loop to make the robot arm better adapted to the operator's movement. The results of experiments performed on Baxter robot platform illustrate a good performance and verifies the proposed method.

关键词： Wrist Neural networks Collaboration Estimation Robot sensing systems Manipulators Robots

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Online Parameter Estimation For Uncertain Robot Manipulators With Fixed-time Convergence

Online Parameter Estimation For Uncertain Robot Manipulators...

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Chengzhi Zhu Yiming Jiang Chenguang Yang Key Laboratory of Autonomous Systems and Networked Control College of Automation Science and Engineering South China University of Technology Guangzhou China National Engineering Laboratory for Robot Visual Perception and Control Hunan University Changsha China Bristol Robotics Laboratory University of the West of England Bristol UK

ISBN: (数字)9781728151694

ISBN: (纸本)9781728151700

For traditional parameter estimation schemes of uncertain robot, most of them were proposed to identify unknown parameter with desired precision, but few of them focused on the convergence time. Recently finite-time estimation techniques have been proposed by scholars to achieve estimation in finite time. In this paper, we proposed a novel estimation scheme for uncertain robot systems with fixed time instead of finite time. In order to avoid using acceleration signals during the estimation, a kind of auxiliary filtering technique was employed. Besides, a continuous and recursive update law was employed for the parameter estimation such that the computational burdens of real-time inversion of square matrices could be avoided. Finally the effectiveness of the identification algorithm is verified based on a 2-DOF uncertain robot model.

关键词： Parameter estimation Filtering Heuristic algorithms Estimation Real-time systems Manipulator dynamics Convergence

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Beyond the Field-of-View: Enhancing Scene Visibility and Perception with Clip-Recurrent Transformer

arXiv

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arXiv 2022年

作者： Shi, Hao Jiang, Qi Yang, Kailun Yin, Xiaoting Wang, Ze Wang, Kaiwei The State Key Laboratory of Extreme Photonics and Instrumentation the National Engineering Research Center of Optical Instrumentation Zhejiang University Hangzhou310027 China The School of Robotics The National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha410082 China Shanghai SUPREMIND Technology Company Ltd Shanghai201210 China

Vision sensors are widely applied in vehicles, robots, and roadside infrastructure. However, due to limitations in hardware cost and system size, camera Field-of-View (FoV) is often restricted and may not provide sufficient coverage. Nevertheless, from a spatiotemporal perspective, it is possible to obtain information beyond the camera’s physical FoV from past video streams. In this paper, we propose the concept of online video inpainting for autonomous vehicles to expand the field of view, thereby enhancing scene visibility, perception, and system safety. To achieve this, we introduce the FlowLens architecture, which explicitly employs optical flow and implicitly incorporates a novel clip-recurrent transformer for feature propagation. FlowLens offers two key features: 1) FlowLens includes a newly designed Clip-Recurrent Hub with 3D-Decoupled Cross Attention (DDCA) to progressively process global information accumulated over time. 2) It integrates a multi-branch Mix Fusion Feed Forward Network (MixF3N) to enhance the precise spatial flow of local features. To facilitate training and evaluation, we derive the KITTI360 dataset with various FoV mask, which covers both outer- and inner FoV expansion scenarios. We also conduct both quantitative assessments and qualitative comparisons of beyond-FoV semantics and beyond-FoV object detection across different models. We illustrate that employing FlowLens to reconstruct unseen scenes even enhances perception within the field of view by providing reliable semantic context. Extensive experiments and user studies involving offline and online video inpainting, as well as beyond-FoV perception tasks, demonstrate that FlowLens achieves state-of-the-art performance. The source code and dataset are made publicly available at https://github. com/MasterHow/FlowLens. Copyright © 2022, The Authors. All rights reserved.

关键词： Optical flows

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Antenna Cross-correlation based Compressive Subspace Learning for Wideband Spectrum Sensing

Antenna Cross-correlation based Compressive Subspace Learnin...

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International Conference on Communication Technology (ICCT)

作者： Tierui Gong Zhifeng Liu Zhijia Yang Gengshan Wang Key Laboratory of Networked Control Systems State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China

To address the performance degradation of wide-band spectrum sensing by sub-Nyquist sampling (SNS) in wireless fading channels, two compressive subspace learning (CSL) algorithms are proposed for signal subspace learning based on antenna cross-correlations for further improving the sensing performance. Both algorithms are developed based on different organizations of SNS samples, and both exploit space diversity and noise uncorrelations between antennas. We further establish the expressions for statistical covariance matrices (SCMs) obtained by SNS samples in the multi-antenna SNS cognitive radio system. Based on the derived SCM expressions, conditions to ensure SCMs without noise contamination are given. Simulations validate the derived conditions and show the improvement on sensing performance over related works.

关键词： Wideband Robot sensing systems Broadband antennas Fading channels Spatial diversity

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Implementation of CAPI and Traditional PCIe and Delay of Comparison

Implementation of CAPI and Traditional PCIe and Delay of Com...

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IEEE International Conference on Cloud Computing and Big Data Analysis (ICCCBDA)

作者： Fudong Wang Tao Zhang Bingjie Zhao Fan Yang Qingxin Li Ziyang Li Key Laboratory of Networked Control Systemsm Chinese Academy of Sciences Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Liaoning Shenyang China Chinese Academy of Sciences Dalian University of Technology Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Liaoning Shenyang China

ISBN: (数字)9781728160245

ISBN: (纸本)9781728160252

With the development of artificial intelligence technology and heterogeneous computing, heterogeneous communication speed is becoming more and more important. The latency of transmitting data determines whether a heterogeneous computing architecture is available. Current acceleration cards usually use PCIe interface. IBM developed the CAPI communication architecture for heterogeneous computing. This paper compares the technical principles and delays of the Power CAPI architecture and a high speed PCIe communication method.

关键词： Field programmable gate arrays Kernel Computer architecture Acceleration Linux Hardware

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