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检索条件"机构=Key Laboratory of Robotics and Intelligent Technology College of IOT Engineering"

共 343 条记录，以下是51-60 订阅

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Macro-Mini Manipulator Based on Multi-Force Sensors for Force Control Application

Macro-Mini Manipulator Based on Multi-Force Sensors for Forc...

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IEEE International Conference on Cyber technology in Automation, Control, and intelligent Systems

作者： Junjie Dai Chin-Yin Chen Han Chen Guilin Yang Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology Ningbo Institute of Materials Technology and Engineering Ningbo China University of Chinese Academy of Sciences Beijing China College of Mechanical Engineering Zhejiang University of Technology Hangzhou China

ISBN: (数字)9798331506056

ISBN: (纸本)9798331506063

In polishing, force-controlled performance is affected by manipulator dynamics, end-effector dynamics, and workpiece uncertainty. This paper proposes a new force-controlled method by utilizing multi-force sensors for robotic polishing applications. One of the force sensors is installed between the endpoint of the manipulator and the fixed platform of the end-effector to realize the dynamic decoupling. The other one is added at the back of the workpiece to realize force tracking. The experimental results indicate that the proposed method's contact force response speed and overshoot are both better than those of the conventional force-controlled method.

关键词： Uncertainty Force Dynamics Process control Robot sensing systems End effectors Force sensors Force control intelligent systems Manipulator dynamics

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Towards High-resolution 3D Anomaly Detection via Group-Level Feature Contrastive Learning 24

Towards High-resolution 3D Anomaly Detection via Group-Level...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Zhu, Hongze Xie, Guoyang Hou, Chengbin Dai, Tao Gao, Can Wang, Jinbao Shen, Linlin National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Computer Science City University of Hong Kong Hong Kong Hong Kong Department of Intelligent Manufacturing CATL Ningde China Fuzhou Fuyao Institute for Advanced Study Fuyao University of Science and Technology Fuzhou China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

ISBN: (纸本)9798400706868

High-resolution point clouds (HRPCD) anomaly detection (AD) plays a critical role in precision machining and high-end equipment manufacturing. Despite considerable 3D-AD methods that have been proposed recently, they still cannot meet the requirements of the HRPCD-AD task. There are several challenges: i) It is difficult to directly capture HRPCD information due to large amounts of points at the sample level;ii) The advanced transformer-based methods usually obtain anisotropic features, leading to degradation of the representation;iii) The proportion of abnormal areas is very small, which makes it difficult to characterize. To address these challenges, we propose a novel group-level feature-based network, called Group3AD, which has a significantly efficient representation ability. First, we design an Intercluster Uniformity Network (IUN) to present the mapping of different groups in the feature space as several clusters, and obtain a more uniform distribution between clusters representing different parts of the point clouds in the feature space. Then, an Intracluster Alignment Network (IAN) is designed to encourage groups within the cluster to be distributed tightly in the feature space. In addition, we propose an Adaptive Group-Center Selection (AGCS) based on geometric information to improve the pixel density of potential anomalous regions during inference. The experimental results verify the effectiveness of our proposed Group3AD, which surpasses Reg3D-AD by the margin of 5% in terms of object-level AUROC on Real3D-AD. We provide the code and supplementary information on our website: https://***/M-3LAB/Group3AD. © 2024 ACM.

关键词： Contrastive Learning

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A Novel Bio-Inspired Quadruped Crawling Robot with Movable Waist

A Novel Bio-Inspired Quadruped Crawling Robot with Movable W...

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ASME/IFToMM International Conference on Reconfigurable Mechanisms and Robots, ReMAR

作者： Ruiqin Wang Hao Wu Jian S. Dai The School of Mechanical Engineering Hebei University of Technology Tianjin China School of Mechanical Engineering Hebei University of Technology Tianjin China The Shenzhen Key Laboratory of Intelligent Robotics and Flexible Manufacturing Systems Southern University of Science and Technology Shenzhen China Mechanisms and Robotics Centre for Robotics Research King's College London UK

ISBN: (数字)9798350395969

ISBN: (纸本)9798350395976

Traditional quadruped robots usually have rigid and fixed torso, which limits their mobility and flexibility. Robots with movable waist can improve the flexibility and environmental adaptability of robots. In this paper, a novel bionic crawling robot with movable waist is proposed. The movable waist innovatively adopts an anti-parallelogram mechanism. At the same time, the waist adopts a double-layer structure design to increase the stability and bearing capacity of the robot. The kinematics model of the robot is derived based on the D-H parameter method. Bas ed on the Monte Carlo method, the toe workspace of the leg is obtained in Matlab software. The results show that due to the torsion of the movable waist, the toe workspace is significantly expanded, and the robot can achieve faster and smoother motion. A prototype was made based on 3D printing technology for experimental verification.

关键词： Legged locomotion Monte Carlo methods Robot kinematics Prototypes Three-dimensional printing Stability analysis Software

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Compliance Control and Simulation of a Next-Generation Chewing Robot 42

Compliance Control and Simulation of a Next-Generation Chewi...

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42nd Chinese Control Conference, CCC 2023

作者： Wang, Xudong Xu, Weiliang Dhupia, Jaspreet Bronlund, John Han, Jianda The University of Auckland Department of Mechanical & Mechatronics Engineering Auckland New Zealand School of Food and Advanced Technology Massey University Palmerston North New Zealand Reddit Institute Palmerston North New Zealand Institute of Robotics and Automatic Information System College of Artificial Intelligence Nankai University Tianjin China Nankai University Tianjin Key Laboratory of Intelligent Robotics Tianjin China

ISBN: (纸本)9789887581543

The aim of the development of the chewing robots is to study the biomechanics of chewing process or produce the bolus for further research by in vitro experiments. Some chewing robots are open loop controlled and couldn't behave like a human. However, to make the chewing robots more bin-mimic, force feedback based closed loop control is required. To improve the efficiency of the in-vitro chewing experiments, a next generation chewing robot with cam-follower based multi-chambers was developed previously, which can be used test the effect of different chewing parameters on the chewing performance and food texture changes simultanously. In this paper, modified admittance control based on the force feedback was applied to the chewing robot. At first, to improve food particle size reduction to a distribution which closely matches that produced by human and protect the components of the robot from breakage or impact damage, a spring mass based system, called remote center compliance (RCC), was developed and applied to the robot to control the chewing force. Then, based on the knowledge of human chewing process, power-based algorithm, and modified admittance control method were used as the feedback to adjust the angular velocity of the motor and corresponding chewing velocity of the teeth. The performance of the control model was also simulated, results showed that the model is reliable to control the robot and make it bio-mimic. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Compliance control

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Marrying NeRF with Feature Matching for One-step Pose Estimation

Marrying NeRF with Feature Matching for One-step Pose Estima...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Ronghan Chen Yang Cong Yu Ren State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences University of Chinese Academy of Sciences College of Automation Science and Engineering South China University of Technology

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Given the image collection of an object, we aim at building a real-time image-based pose estimation method, which requires neither its CAD model nor hours of object-specific training. Recent NeRF-based methods provide a promising solution by directly optimizing the pose from pixel loss between rendered and target images. However, during inference, they require long converging time, and suffer from local minima, making them impractical for real-time robot applications. We aim at solving this problem by marrying image matching with NeRF. With 2D matches and depth rendered by NeRF, we directly solve the pose in one step by building 2D-3D correspondences between target and initial view, thus allowing for real-time prediction. Moreover, to improve the accuracy of 2D-3D correspondences, we propose a 3D consistent point mining strategy, which effectively discards unfaithful points reconstruted by NeRF. Moreover, current NeRF-based methods naively optimizing pixel loss fail at occluded images. Thus, we further propose a 2D matches based sampling strategy to preclude the occluded area. Experimental results on representative datasets prove that our method outperforms state-of-the-art methods, and improves inference efficiency by 90×, achieving real-time prediction at 6 FPS.

关键词： Training Solid modeling Three-dimensional displays Pose estimation Buildings Neural radiance field Real-time systems

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A Point-to-distribution Degeneracy Detection Factor for LiDAR SLAM using Local Geometric Models

A Point-to-distribution Degeneracy Detection Factor for LiDA...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Sehua Ji Weinan Chen Zerong Su Yisheng Guan Jiehao Li Hong Zhang Haifei Zhu Biomimetic and Intelligent Robotics Lab (BIRL) School of Electromechanical Engineer Guangdong University of Technology Guangzhou China JT-Innovation (Guangdong) Intelligent Technology Co. Ltd. Guangdong Key Laboratory of Modern Control Technology Institute of Intelligent Manufacturing Guangdong Academy of Sciences Guangzhou China College of Engineering South China Agricultural University China Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Limited by the working principles, LiDAR-SLAM systems suffer from the degeneration phenomenon in environments such as long corridors and tunnels, due to the lack of sufficient geometric features for frame-to-frame matching. The accuracy and sensitivity of existing degeneracy detection methods need to be further improved. In this paper, we propose a novel method for degeneracy detection using local geometric models based on point-to-distribution matching. To obtain an accurate description of local geometric models, an adaptive adjustment of voxel segmentation according to the point cloud distribution and density is designed. The codes of the proposed method is open-source and available at https://***/jisehua/***. Experiments with public datasets and self-build robots were conducted to evaluate the methods. The results exhibit that our proposed method achieves higher accuracy than the other existing approaches. Applying our proposed method is beneficial for improving the robustness of the LiDAR-SLAM systems.

关键词： Point cloud compression Accuracy Simultaneous localization and mapping Sensitivity Laser radar Geometric modeling Noise

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EEG-GNet: GCN-Based EEG Signal Fatigue Detection Network for Edge Devices

EEG-GNet: GCN-Based EEG Signal Fatigue Detection Network for...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhang, Fuquan Wu, Yu Xu, Xinxin Wang, Chuansheng Ma, Minjia Grau, Antoni Huang, Jiayan School of Computer and Big Data Minjiang University Fuzhou China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China College of New Engineering Industry Putian University Putian China Department of Automatic Control Polytechnic University of Catalonia Barcelona Spain School of Robotics Xi'an Jiaotong-Liverpool University Suzhou China College of New Engineering Industry Putian University Fujian Ruituo Information Technology Limited Company Putian China

ISBN: (纸本)9798350386226

Fatigue driving is a common potential traffic safety hazard. To detect and analysis the driver Fatigue degree in real-time, more attention have been increasing paid to application research of EEG fatigue detection device. However, existing methods suffer from complex model structure or large-scale model parameters. In the other words, they requires high computational consumption and large memory footprint, which creates a contradiction between model and portable EEG machine with limited hardware resources. In this work, we take both time and frequency domains of EEG signal into account and design an lightweight and efficient driver fatigue detection network based on GCN (EEG-GNet), which is available edge-end device application. Experiments on 4th Raspberry Pi on a public baseline dataset show that our EEG-GNet outperforms the state-of-the-art methods and takes good tradeoff between detection accuracy and resource consumption. Our EEG-GNet provides a new potential opportunity for future applications in practical portable/wearable (P/W) devices. © 2024 IEEE.

关键词： Electrocardiography

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MUP-LIO: Mapping Uncertainty-aware Point-wise Lidar Inertial Odometry

MUP-LIO: Mapping Uncertainty-aware Point-wise Lidar Inertial...

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

作者： Hekai Yao Xuetao Zhang Gang Sun Yisha Liu Xuebo Zhang Yan Zhuang Intelligent Robotic Laboratory School of Artificial Intelligence School of Control Science and Engineering Dalian University of Technology Dalian China School of Information Science and Technology Dalian Maritime University Dalian China College of Artificial Intelligence The Institute of Robotics and Automatic Information System (IRAIS) Tianjin Key Laboratory of Intelligent Robotics Nankai University Tianjin China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

This paper proposes a mapping uncertainty-aware point-wise Lidar Inertial Odometry (LIO), which synthesizes the point-wise point-to-plane match and map refreshment into a probabilistic model. As a result, it can address the issue of mismatching during point registration and remove in-frame motion distortion of Lidar sensors. Specifically, the uncertainty-aware map is designed to embody the uncertainty of map geometric features (points and planes), which comes from the Lidar point measurement and pose estimation. Then the map can be modeled in a probabilistic form. In addition, the proposed framework refreshes map at each Lidar point measurement to timely revise geometric features and provide non-delayed map. On the basis, the probabilistic point-to-plane match method is designed to seek a corresponding plane for each Lidar point in point registration, which can enhance the effectiveness of match and provide adaptive observation noises for more accurate state estimation. Comparative experiments on various public datasets are conducted to demonstrate the superior performance of the proposed framework in terms of higher accuracy and better robustness.

关键词： Laser radar Accuracy Uncertainty Probabilistic logic Distortion Robustness Sensors Odometry Distortion measurement State estimation

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CB-YOLOv5 Algorithm for Small Target Detection in Aerial Images 4

CB-YOLOv5 Algorithm for Small Target Detection in Aerial Ima...

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4th International Conference for Emerging technology, INCET 2023

作者： Li, Yingjie Wang, Yitian Zhao, Huaici Shenyang University of Chemical Technology College of Information Engineering Shenyang110142 China Chinese Academy of Sciences Key Laboratory of Opto-Electronic Information Processing Shenyang110016 China Chinese Academy of Sciences Shenyang Institute of Automation Shenyang110016 China Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Shenyang110169 China

ISBN: (纸本)9798350335750

Aerial images are often plagued by background interference, and small targets with indistinct features, leading to low accuracy, high false detection rates, and high miss detection rates. To address these challenges, a small target detection algorithm based on YOLOv5, Coordinate-attention and Bidirectional-feature-pyramid-network YOLOv5 (CB-YOLOv5), is proposed in this paper. Considering the small number of pixels occupied by small targets and their indistinct features, a fourth target detection layer is added by concatenating the feature map from quadruple down-sampling during feature extraction with the feature map output from 8-fold up-sampling during feature fusion. Additionally, a coordinate attention mechanism is introduced during the feature extraction stage to improve small target localization and enhance detection accuracy. Finally, the original Path Aggregation Networks (PANet) structure is replaced with a weighted Bidirectional Feature Pyramid Network (BiFPN) structure during the feature fusion stage to improve the network's ability to fuse feature maps of different scales. The simulation results demonstrate that the CB-YOLOv5 improves mAP50 by 9.4%, mAP75 by 9.7%, and mAP50:95 by 7.8% compared to the original YOLOv5s model. Thus, the effectiveness of the CB-YOLOv5 algorithm for detecting small targets in aerial images is validated. © 2023 IEEE.

关键词： Feature extraction

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Development of Insulation Coating Robot for Distribution Network Lines Maintenance

Development of Insulation Coating Robot for Distribution Net...

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International Conference on Mechanical Design, ICMD 2021

作者： Yue, Xiang Wang, Hongguang Wang, Lin Liu, Yanhua State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China College of Engineering Shenyang Agricultural University Shenyang110866 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China School of Mechanical Engineering Changshu Institute of Technology Changshu215500 China

ISBN: (纸本)9789811673801

At present, the surface of overhead power lines in high-voltage power grid is directly exposed to the external environment, which leads to poor stability of the transmission and distribution network and prone to tripping accidents. In order to solve this problem, this paper analyzes the characteristics of line inspection robot at home and abroad, and develops a double arm wheeled electric working robot which can coat insulating paint on overhead lines of distribution network. The dual arm wheeled mechanical structure of the robot is designed. It is mainly composed of walking mechanism, cleaning mechanism, coating device and control box. The robot can move stably and reliably on the overhead line through the cooperation of driving wheel and clamping wheel. Coating device is mainly composed of liquid pump drive system, coating system bracket, insulating paint pipeline and nozzle, which can continuously coat insulating paint on overhead line. The master–slave electronic control system of the robot body and the upper computer control platform are developed. Based on the coupling model of overhead line and robot, the quasi-static mechanical model of robot body is established. Finally, simulation and experiments are carried out and the results show the correctness of the robot station. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Robots

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