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检索条件"任意字段=International Conference on Methods and Models in Automation and Robotics"

共 24829 条记录，以下是4041-4050 订阅

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Determination of the Strain Energy and Stress Intensity Factors with the Crack's Inclination under biaxial loading

Determination of the Strain Energy and Stress Intensity Fact...

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international conference on robotics and Artificial Intelligence (ICRAI)

作者： Salman Khan Massab Junaid Fahd Nawaz Khan Tauheed Shehbaz Faculty of Mechanical Engineering (FME) Ghulam Ishaq Khan (GIK) Institute of Engineering Sciences and Technology Swabi Pakistan

All components have certain flaws either interior or exterior on the surface which causes stress intensity under loading. The stress intensity exceeds the fracture toughness of the materials and causes failure of the component. A finite element analysis (FEA) was performed on Inconel 718 plate with a central crack under biaxial loading to compute the stress intensity factor (SIF) and the strain energy released rate (SERR) with the crack inclination ranging (ß) 0 °-150° and loading factor (B). The applied load is 10 MPa in the Y-direction (αy) while a range of -30 - +30 MPa in the X -direction (σ x ) with B of -3 to +3 was studied using COMSOL Multiphysics. The ratio of height to width of the plate is 1.25. The results show that the opening displacement was maximum at negative B (compression) while the minimum for positive B (tension) was at 90°. For mode I, the SIFs increase in compression and maximum at 90° while decreasing in tension and minimum at 90°. Beyond 90°, the SIFs increase under tensile while decreasing in compression load. For mode II, the SIFs were maximum at 45° for negative B while minimum at positive B.

关键词： Industries Loading Market research Mathematical models Distance measurement Finite element analysis Numerical models

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Enhancing Online Load Model Parameter Identification with Deep Reinforcement Learning: A DDPG-Based Approach

Enhancing Online Load Model Parameter Identification with De...

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international conference of Electrical, Electronic and Networked Energy Systems, EENES 2024

作者： Li, Zihao Liu, Xingwei Huang, Yupeng Hu, Jianxiong Tang, Yi School of Electrical Engineering Southeast University Nanjing China Beijing Key Laboratory of Research and System Evaluation of Power Dispatching Automation Technology Beijing China China Electric Power Research Institute Beijing China

ISBN: (纸本)9789819620791

To cope with the complex dynamic characteristics brought about by the sharp increase in penetration of various power electronic devices, online dynamic security analysis in modern power systems has put forward higher requirements for the accuracy of simulation models. Traditional methods for load model parameter identification (PI) struggle with model complexity, computational pressure, and low generalization, which can hinder their effectiveness in online applications. To overcome these challenges, this paper proposes a novel online load model PI method utilizing the Deep Deterministic Policy Gradient (DDPG) algorithm. Initially, a composite load model incorporating various distributed energy resources (DERs) is developed as the model basis. Subsequently, a DDPG-based framework for online load model PI is designed, featuring meticulously crafted interface functions to improve both convergence and generalization capabilities. Case studies on the EPRI-36 buses test system demonstrate the proposed method's efficiency and accuracy compared to traditional heuristic optimization methods. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Deep reinforcement learning

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Weighted Feature Ranking Merging for Supervised Machine Learning 19th

Weighted Feature Ranking Merging for Supervised Machine Lea...

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19th international conference on Soft Computing models in Industrial and Environmental Applications, SOCO 2024

作者： Coto-Palacio, Jessica Ortiz-Tandazo, Daniel Alejandro Bautista-Juárez, Alejandro Grangetto, Agustina Cabello-Solorzano, Kelsy León-Castro, Diana Santana-Morales, Paola Tallón-Ballesteros, Antonio J. International University of Andalusia Palos de la Frontera Huelva Spain University of Huelva Huelva Spain Department of Electronic Computer Systems and Automation Engineering University of Huelva Huelva Spain

ISBN: (纸本)9783031750090

This paper introduces a novel approach to ranking-based feature selection. Numerous metrics exist to assess univariate filters. This contribution identifies the top best features by employing two distinct methods, computes the global weights of each feature, and sorts these weights in decreasing order for those which are common to both procedures. According to the first part, this work is a feature selection proposal and concerning the last, it is besides a feature sorting approach. The benchmark data sets originate from NIPS 2003. This approach has been tested on problems with up to 20000 features and up to 7000 samples. The test results are very promising since, in some cases the outcomes significantly outperform the raw scenario, and in others, they surpass those of an approach which may be considered to be related to the current contribution. Finally, this study opens new avenues for research, particularly in examining the importance of specific feature sorting methods for certain supervised machine learning algorithms. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Learning Temporal Cues by Predicting Objects Move for Multi-camera 3D Object Detection

Learning Temporal Cues by Predicting Objects Move for Multi-...

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IEEE international conference on robotics and automation (ICRA)

作者： Seokha Moon Hongbeen Park Jaekoo Lee Jinkyu Kim Department of Computer Science and Engineering Korea University Seoul Republic of Korea College of Computer Science Kookmin University Seoul Republic of Korea

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

In autonomous driving and robotics, there is a growing interest in utilizing short-term historical data to enhance multi-camera 3D object detection, leveraging the continuous and correlated nature of input video streams. Recent work has focused on spatially aligning BEV-based features over timesteps. However, this is often limited as its gain does not scale well with long-term past observations. To address this, we advocate for supervising a model to predict objects’ poses given past observations, thus explicitly guiding to learn objects’ temporal cues. To this end, we propose a model called DAP (Detection After Prediction), consisting of a two-branch network: (i) a branch responsible for forecasting the current objects’ poses given past observations and (ii) another branch that detects objects based on the current and past observations. The features predicting the current objects from branch (i) is fused into branch (ii) to transfer predictive knowledge. We conduct extensive experiments with the large-scale nuScenes datasets, and we observe that utilizing such predictive information significantly improves the overall detection performance. Our model can be used plug-and-play, showing consistent performance gain.

关键词： Solid modeling Three-dimensional displays Refining Object detection Predictive models Streaming media Performance gain

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Efficient Traversability Mapping for Enhanced Robotic Firefighting Operations

Efficient Traversability Mapping for Enhanced Robotic Firefi...

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international conference on Control, automation and Systems ( ICCAS)

作者： Youngjoo. Choi Hanwool Lee Hwang Jung Hoon Mincheol. Park Intelligent Robotics Research Center Korea Electronics Technology Institute Bucheon Korea

ISBN: (数字)9788993215380

ISBN: (纸本)9798331517939

This paper addresses the critical need for safer firefighting practices, highlighted by the increase in firefighter injuries and fatalities reported in the 2023 Fire Service Statistical Yearbook. With 239 injuries and three fatalities in 2022, the study underscores the urgency of integrating robotics into firefighting operations to enhance safety. Our focus is on rapidly generating accurate traversability maps. We employ advanced techniques, such as Inpaint and Smoothing Filters, to effectively process raw elevation data, ensuring continuous and reliable traversability calculations. Validation through LiDAR data from a Unitree Go1 robot in various environments demonstrates the effectiveness of our approach. Moreover, our Traversability Filter Model processes grid_map messages to produce probabilistic traversability values, offering improved granularity for training. Comparative results highlight the success of our method in enhancing traversability estimation, thereby optimizing robotic firefighting operations.

关键词： Training Laser radar Filters Accuracy Smoothing methods Navigation Estimation Surface roughness Robots Injuries

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Unsupervised Learning of Depth and Pose Based on Monocular Camera and Inertial Measurement Unit (IMU)

Unsupervised Learning of Depth and Pose Based on Monocular C...

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IEEE international conference on robotics and automation (ICRA)

作者： Yanbo Wang Hanwen Yang Jianwei Cai Guangming Wang Jingchuan Wang Yi Huang University of Michigan-Shanghai Jiao Tong University Joint Institute Shanghai Jiao Tong University Shanghai China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Weitong Vision Technology Co. Ltd.

The main content of the research in this paper is the estimation of depth and pose based on monocular vision and Inertial Measurement Unit (IMU). The usual depth estimation network and pose estimation network require depth ground truth or pose ground truth as a supervised signal for training, while the depth ground truth and pose ground truth are hard to obtain, and monocular vision based depth estimation cannot predict absolute depth. In this paper, with the help of IMU, which is inexpensive and widely used, we can obtain angular velocity and acceleration information. Two new supervision signals are proposed and the calculation expressions are given. Among them, the model trained with acceleration constraint shows a good ability to estimate the absolute depth during the test. It can be considered that the model can estimate the absolute depth. We also derive the method of estimating the scale factor during the test from the acceleration constraint, and also achieve good results as the acceleration constraint does. In addition, this paper also studies the method of using IMU information as pose network input and as selecting conditions. Moreover, it analyzes and discusses the experimental results. At the same time, we also evaluate the effect of the pose estimation of the relevant models. This article starts by reviewing the achievements and deficiencies of the work in this field, combines the use of IMU, puts forward three new methods such as a new loss function, and conducts a test analysis and discussion of relevant indicators on the KITTI data set.

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An Iterative Variable Step Error Compensation Approach for Enhancing Robot Trajectory Accuracy 3

An Iterative Variable Step Error Compensation Approach for E...

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3rd international conference on automation, robotics and Computer Engineering, ICARCE 2024

作者： Wang, Chengzhi Zhao, Sikai Zheng, Tianjiao Cai, Hegao Zhao, Jie Zhu, Yanhe Harbin Institute of Technology State Key Laboratory of Robotics and Systems Harbin China

ISBN: (纸本)9798331529505

In this paper, an iterative error compensation approach is analyzed and designed to improve trajectory accuracy of industrial robots. Advanced offline programming and robotic machining tasks require high trajectory accuracy of industrial robots, which is significantly affected by parameter inaccuracy, stiffness deformation, backlash, and so on. Such complicated coupling makes it impossible to model each factor separately, thus data-driven direct error compensation is the major solution to improve trajectory accuracy. Previous methods utilize external measurement equipment to obtain tracking error and rely on the error consistency assumption to compensate for the error by modifying the trajectory commands directly. However, such consistency assumption does not hold. Even a minor incremental modification of the initial commands can lead to unexpected influence on the robot's tracking performance, owning to unaccounted factors such as the robot's dynamic stiffness and natural frequency. This paper decomposes trajectory tracking errors based on servo encoder feedback and external laser tracker, and proposes a variable step error compensation scheme to improve tracking performance. Physical experiments are conducted on a standard 6 degree-of-freedom industrial robot to validate the effectiveness of the proposed method. The results of various error compensation approaches are analyzed to draw a possible error correlation model, in order to account for the error inconsistency in the compensation process. © 2024 IEEE.

关键词： Industrial robots

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Recursive Least Squares with Log-Determinant Divergence Regularisation for Online Inertia Identification

Recursive Least Squares with Log-Determinant Divergence Regu...

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IEEE international conference on robotics and automation (ICRA)

作者： Namhoon Cho Taeyoon Lee Hyo-Sang Shin Centre for Autonomous and Cyber-Physical Systems School of Aerospace Transport and Manufacturing Cranfield University Cranfield Bedfordshire United Kingdom NAVER LABS Gyeonggi-do South Korea Cho Chun Shik Graduate School of Mobility Korea Advanced Institute of Science and Technology Daejeon South Korea

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

This study presents a recursive algorithm for solving the regularised least squares problem for online identification of rigid body dynamic model parameters with emphasis on the physical consistency of estimated inertial parameters. One of the geometric approaches is to use a regulariser that represents how close the pseudo-inertia matrix is to a given reference on the feasible manifold in the regression problem. The proposed extension enables memory-efficient online learning in addition to the benefits of geometry-aware convex regularisation using the log-determinant divergence of the pseudo-inertia matrix. Also, the recursive version endows the estimator with the capability to deal with time-variation of parameters by introducing an optional forgetting mechanism. The characteristics of the recursive regularised least squares algorithm is demonstrated using the MIT Cheetah 3 leg swinging experiment dataset and compared to the existing batch optimisation method.

关键词： Manifolds Legged locomotion Heuristic algorithms Linear regression Optimization methods Benchmark testing Computational efficiency

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Pest Detection and Identification Guided by Feature Maps 12

Pest Detection and Identification Guided by Feature Maps

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12th international conference on Image Processing Theory, Tools and Applications, IPTA 2023

作者： Chen, Miao Chen, Yanan Guo, Minghui Wang, Jianji Xi'an Jiaotong University National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics China Xi'an Jiaotong University School of Software Engineering Xi'an China

ISBN: (纸本)9798350325416

Effective pest detection and identification are of great significance for agricultural activities, and morden machine learning methods, especially the deep neural network, undoubtedly provide convenient and effective ways for this issue. Most of the mainstream methods for pest detection require annotation of manual bounding box. However, the public pest datasets usually lack the labeling information. In this work, we propose a method for detecting pests with weak supervision. We obtain the key regions of the pest targets by deeply mining the information in the feature maps of the convolutional neural networks (CNNs). First, using the class activation mapping technique to generate activation maps of the target class during forward propagation of the model, and then obtaining the key regions of the target based on the hotspots extracted from the activation maps. In addition, our proposed method can be deployed as a general strategy to other CNNs, and experimental results demonstrate that our method can steadily improve the classification accuracy of the model. By applying our method to the single Resnet50, we can achieve 74.27% classification accuracy on the publicly accessible IP102 dataset. Another attribution of this paper is that we propose a lightweight approach to find solutions for pest detection in agricultural automation applications. © 2023 IEEE.

关键词： Deep neural networks

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Design and Visual Servoing Control of a Hybrid Dual-Segment Flexible Neurosurgical Robot for Intraventricular Biopsy

Design and Visual Servoing Control of a Hybrid Dual-Segment ...

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IEEE international conference on robotics and automation (ICRA)

作者： Jian Chen Mingcong Chen Qingxiang Zhao Shuai Wang Yihe Wang Ying Xiao Jian Hu Danny Tat Ming Chan Kam Tong Leo Yeung David Yuen Chung Chan Hongbin Liu School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences (CASIA) Beijing China Centre of AI and Robotics Hong Kong Institute of Science and Innovation Chinese Academy of Sciences (CAIR-HKISI-CAS) HKSAR Department of Biomedical Engineering City University of Hong Kong HKSAR CAIR-HKISI-CAS HKSAR State Key Laboratory of Multimodal Artificial Intelligence Systems CASIA Beijing China Department of Surgery The Chinese University of Hong Kong HKSAR State Key Laboratory of Management and Control for Complex Systems CASIA Beijing China School of Biomedical Engineering and Imaging Sciences King’s College London London UK

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Traditional rigid endoscopes have challenges in flexibly treating tumors located deep in the brain, and low operability and fixed viewing angles limit its development. This study introduces a novel dual-segment flexible robotic endoscope MicroNeuro, designed to perform biopsies with dexterous surgical manipulation deep in the brain. Taking into account the uncertainty of the control model, an image-based visual servoing with online robot Jacobian estimation has been implemented to enhance motion accuracy. Furthermore, the application of model predictive control with constraints significantly bolsters the flexible robot’s ability to adaptively track mobile objects and resist external interference. Experimental results underscore that the proposed control system enhances motion stability and precision. Phantom testing substantiates its considerable potential for deployment in neurosurgery.

关键词： Jacobian matrices Visualization Endoscopes Estimation Interference Predictive models Control systems

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