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American Control Conference (ACC)

作者： Xinyang Wang Martin Guay Shimin Wang Hongwei Zhang Guangdong Provincial Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen Guangdong China Department of Chemical Engineering Queen's University Kingston Ontario Canada

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

This paper proposes a novel reinforcement learning approach to solve the optimal output synchronization problem for discrete-time heterogeneous multi-agent systems. Different from existing learning methods, the optimal control protocol is obtained to guarantee zero synchronization error by solving the augmented algebraic Riccati equations (AREs). The proposed adaptive dynamic programming (ADP) method can stabilize the output synchronization error and solve the output regulator equations implicitly. To eliminate the dependency on information of system dynamics, an online Q-function-based policy iteration (PI) algorithm is developed. Finally, a numerical example is provided to demonstrate the advantages of the proposed ADP over traditional ADP in terms of synchronization performance.

关键词： Regulators Q-learning Protocols System dynamics Riccati equations Optimal control Mathematical models

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2D-3D Object Shape Alignment for Camera-Object Pose Compensation in Object-Visual SLAM

2D-3D Object Shape Alignment for Camera-Object Pose Compensa...

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IEEE International Conference on robotics and automation (ICRA)

作者： Hanyeol Lee Jae Hyung Jung Chan Gook Park Department of Aerospace Engineering and Automation and Systems Research Institute Seoul Natioinal University Seoul South Korea Smart Robotics Lab School of Computation Information and Technology Technical University of Munich Germany

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

In this study, we propose an object shape alignment method through a robust optimization scheme for 6-degrees-of-freedom (DOF) object pose compensation. Although the pose estimation of the 3D object by the camera has been rapidly improved in recent years with the development of deep learning, the estimate still contains errors due to several factors. To compensate for this, we perform a shape alignment between the 2D segmentation of the object and the projection of the 3D object in the image plane. To avoid convergence to a local minimum in nonlinear optimization, we separate the pose into translation and rotation. This approach derives the optimization of a linear form in terms of a translation with reduced computational cost. For the rotation, the parallel optimization is performed with multiple initial values, reflecting to the uncertainty of an initial value. We formulate an invariant extended Kalman filter (EKF)-based object-visual simultaneous localization and mapping (SLAM) with a camera-object relative pose as the measurement model. To verify the performance of the proposed algorithm, we present the improved results of camera-object relative pose accuracy and localization and mapping accuracy in the several sequences of YCB-video dataset.

关键词： Location awareness Image segmentation Three-dimensional displays Simultaneous localization and mapping Uncertainty Accuracy Shape

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Fault Detection and Isolation for a Standard Quadrotor Using a Deep Neural Network Trained on a Momentum-based Estimator

Fault Detection and Isolation for a Standard Quadrotor Using...

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IEEE International Conference on automation Science and engineering (CASE)

作者： Vincenzo Scognamiglio Jonathan Cacace Fabio Ruggiero Vincenzo Lippiello Department of Electrical Engineering and Information Technology PRISMA Lab and CREATE Consortium University of Naples Federico II Naples Italy Robotics and Automation Unit Eurecat Centre Tecnològic de Catalunya

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Rapid identification of motor failures holds significant importance for ensuring the safety of multi-rotor unmanned aerial vehicles. This study introduces a method for detecting and isolating motor faults in standard quadrotors, utilizing an external wrench estimator and a recurrent neural network with long short-term memory nodes. The proposed approach treats partial or complete motor failure as an external disturbance affecting the quadrotor. Consequently, the external wrench estimator trains the network to quickly discern whether the estimated wrench results from a motor fault, identifying the specific motor involved, or if it stems from unmodeled dynamics or external factors such as wind or contacts. The training and testing of this method were conducted in a simulation environment equipped with a physics engine.

关键词： Training Recurrent neural networks Rotors Motors Safety Vehicle dynamics Standards Long short term memory Quadrotors Testing

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Study on TIG welding parameters for joining different kind of aluminium tube with a tube plate

Study on TIG welding parameters for joining different kind o...

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2022 International Conference on Design, Manufacturing and Materials engineering, ICDMME 2021

作者： Chandran, Jishu Muniamuthu, Sumathy Padmanaban, G. Kishore Kumar, A. Mohan Kumar, M. Department of Mechanical Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Tamil Nadu Avadi600062 India Department of Mechanical Engineering Karpagam College of Engineering Tamil Nadu Coimbatore641032 India Department of Robotics and Automation Sri Ramakrishna Engineering College Tamil Nadu Coimbatore641022 India Department of Electronics & Communication Engineering Hindusthan College of Engineering and Technology Tamil Nadu Coimbatore641032 India

Welding is a vital metal joining technique with a wide range of industrial uses. In this study, numerous process factors such as current, feed rate, and distance between tube and work piece, among others, were used to investigate in Tungsten Inert Gas (TIG) welding. We used Aluminum 6063 tube to Aluminum 6061 tube plate for this TIG welding. After the welding is finished, a radiographic test is performed to show any welding faults that might lead to defect-free welds. The most critical control factors that would result in better joint strength were identified to use a Taguchi L9 orthogonal matrix. Furthermore, the most relevant process parameter was determined using quantitative independent test (ANOVA). In addition, the mechanical tests were performed to determine the welding strength. The value of the best welded connection strength for tubes with holes welded at interference was found to be 318.716 MPa. © 2022 Published under licence by IOP Publishing Ltd.

关键词： Analysis of variance (ANOVA)

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SPONGE: Sequence Planning with Deformable-ON-Rigid Contact Prediction from Geometric Features

arXiv

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arXiv 2023年

作者： Le, Tran Nguyen Abu-Dakka, Fares J. Kyrki, Ville Intelligent Robotics Group The Department of Electrical Engineering and Automation School of Electrical Engineering Aalto University Finland MIRMI Technische Universität München München80992 Germany

Planning robotic manipulation tasks, especially those that involve interaction between deformable and rigid objects, is challenging due to the complexity in predicting such interactions. We introduce SPONGE, a sequence planning pipeline powered by a deep learning-based contact prediction model for contacts between deformable and rigid bodies under interactions. The contact prediction model is trained on synthetic data generated by a developed simulation environment to learn the mapping from point-cloud observation of a rigid target object and the pose of a deformable tool, to 3D representation of the contact points between the two bodies. We experimentally evaluated the proposed approach for a dish cleaning task both in simulation and on a real Franka Emika Panda with real-world objects. The experimental results demonstrate that in both scenarios the proposed planning pipeline is capable of generating high-quality trajectories that can accomplish the task by achieving more than 90% area coverage on different objects of varying sizes and curvatures while minimizing travel distance. Code and video are available at: https://***/sponge/. Copyright © 2023, The Authors. All rights reserved.

关键词： Pipelines

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Incipient Fault Monitoring Method Based on Quadratic Kernel Entropy Component Analysis and Its Application 9

Incipient Fault Monitoring Method Based on Quadratic Kernel ...

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9th International Conference on Mechanical and Electronics engineering, ICMEE 2023

作者： Chen, Xuhang Xu, Min Yang, Mingyi Li, Yong Feng, Fuzhou Shenyang Institute Of Automation Chinese Academy Of Science University Of Chinese Academy Of Sciences Shenyang China Beijing Institute Of Robotics And Intelligent Manufacturing Innovation Chinese Academy Of Science Shenyang China Army Academy Of Armored Forces Of The Pla Department Of Equipment Support And Remanufacturing Beijing China Army Academy Of Armored Forces Of The Pla Department Of Vehicle Engineering Beijing China

ISBN: (纸本)9798350307146

Aiming at the characteristics that the incipient faults of mechanical equipment are small and easy to be submerged by noise, this paper proposes a quadratic kernel entropy component analysis algorithm based on kernel entropy component analysis, multi-scale sample entropy, and a comprehensive index combining SPE statistics and T2 statistics, and constructs a data-driven early fault monitoring model. Firstly, the kernel entropy component analysis is used to complete the dimensionality reduction of the data and the initial feature extraction. Secondly, the multi-scale sample entropy is used to completely characterize the complexity of the data samples and the non-linear specificity of the data samples to zoom in on the early micro-failure and carry out the secondary feature extraction. Finally, the comprehensive index combining SPE statistics and T2 statistics is used to comprehensively monitor the incipient fault. The model is validated by public datasets and has been verified to have good early fault monitoring capability in engineering examples. © 2023 IEEE.

关键词： Entropy

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Generalization of Impact Response Factors for Proprioceptive Collaborative Robots

Generalization of Impact Response Factors for Proprioceptive...

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IEEE International Conference on robotics and automation (ICRA)

作者： Carlos Relaño Daniel Sanz-Merodio Miguel López Concepción A. Monje Systems Engineering and Automation Department University Carlos III of Madrid Madrid Spain ARC Robotics Arquimea Research Center Santa Cruz de Tenerife Spain

Physical Human-Robot Interaction(pHRI) re-quires taking safety into account from the design board to the collaborative operation of any robot. For collaborative robotic environments, where human and machine are sharing space and interacting physically, the analysis and quantification of impacts becomes very relevant and necessary. Furthermore, analyses of this kind are a valuable source of information for the design of safer, more efficient pHRI. In the definition of the first parameter for dynamic impact analysis, the dynamic impact mitigation capacity was considered for certain configurations of the robot, but the design characteristics of the robot, such as the inertia of actuators, were not included. This paradigm changed when MIT presented the “impact mitigation factor” (IMF) with which, in addition to considering the ability of a certain robot to mitigate impacts for every configuration, it was possible to quantify backdriveability by taking the inertia of actuators into account for the calculation of the factor. However, IMF was proposed as a method to analyse floating robots like. This paper presents the Generalised Impact Absorption Factor (GIAF), suitable for both floating and fixed-base robots. GIAF is a valuable design parameter, as it provides information about the backdriveability of each joint, while allowing the comparison of impact response between floating and fixed-base robotic platforms. In this work, the mathematical definition of GIAF is developed and examples of possible uses of GIAF are presented.

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A Vision-Based Two-Stage Fruit Outer Diameter Detection Method

A Vision-Based Two-Stage Fruit Outer Diameter Detection Meth...

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2024 IEEE International Conference on robotics and Biomimetics, ROBIO 2024

作者： Hao, Yifei Yan, Shuaizheng Wu, Zhengxing He, Bingwei Li, Chenghua Yu, Junzhi Fuzhou University Department of Mechanical Engineering Fuzhou350108 China Institute of Automation Chinese Academy of Sciences Laboratory of Cognitive and Decision Intelligence for Complex Systems Beijing100190 China Institute of Automation Chinese Academy of Sciences Key Laboratory of Cognition and Decision Intelligence for Complex Systems Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China Guangzhou Shenghui Cleaning Service Co. Ltd. Guangzhou511436 China BIC-ESAT College of Engineering Peking University State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and Robotics Beijing100871 China

ISBN: (纸本)9781665481090

Regarding the mobile monitoring system in agricultural automation, the outer diameter monitoring of greenhouse vegetables has significant implications for accurately regulating fruit growth and cultivation plans. In this paper, a novel vision-based two-stage outer diameter detection method is proposed, including the segmentation and outer diameter calculation of greenhouse tomato images. To address the issues of multiple tomatoes in a single image and the similarity in color between tomatoes and the background, a YOLOv8 network embedded with convolutional block attention module is constructed for detection and segmentation in the first stage. Additionally, to solve the problem of inaccurate outer diameter detection caused by different tomato poses and pedicles, a defined central axis method is proposed, which can adaptively filter out the maximum outer diameter candidates and calculate accurate values utilizing depth information in the world coordinate system. The backbone network is fine-tuned on the pre-trained YOLOv8 model with 1000 tomato images. Extensive measurement experiments, involving 215 tomatoes in 80 images, is conducted to validate the effectiveness of our proposed approach. Compared to the ground truth, we obtain a measurement error of 4.27 mm, the coefficient of determination value R2 is 0.9349. © 2024 IEEE.

关键词： Fruits

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Analyzing Risk Level from RoI and VoI for Robust Data Privacy using Fuzzy Inference System

Analyzing Risk Level from RoI and VoI for Robust Data Privac...

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International Conference on robotics,Electrical and Signal Processing Techniques (ICREST)

作者： Rabab Khan Rongon M. Akhtaruzzaman Krishna Das Shekh Tanjil Sharif Department of Computer Science and Engineering Military Institute of Science and Technology Dhaka Bangladesh Quantum Robotics and Automation Research Group (QRARG) Dhaka Bangladesh International University of Business Agriculture and Technology (IUBAT) Dhaka Bangladesh

ISBN: (数字)9798331530952

ISBN: (纸本)9798331530969

In the modern digital environment, securing data privacy has become increasingly critical as organizations confront rising threats from cyber-attacks and data breaches. To safeguard sensitive information, businesses must invest in robust data protection measures, yet balancing these investments with their potential benefits remains challenging. This research addresses this issue by proposing a novel framework that integrates Return on Investment (RoI) and Value of Information (VoI) within a Fuzzy Inference System (FIS) to assess data privacy risks effectively. RoI measures the financial return relative to the cost of data protection investments, indicating the economic value derived from such measures. VoI, on the other hand, evaluates the worth of the information protected by these investments, reflecting its impact on decision-making and operational efficiency. By employing a FIS, which accommodates uncertainties and imprecise data, this framework provides a nuanced approach to risk assessment, enabling organizations to optimize their data privacy strategies. The integration of RoI and VoI within the FIS allows for a comprehensive evaluation of risk levels, facilitating more informed decision-making and improved risk management. This research aims to enhance data privacy practices by offering a methodical and adaptable tool for evaluating and balancing investments in data protection.

关键词： Fuzzy logic Analytical models Uncertainty Decision making Data protection Signal processing algorithms Organizations Mathematical models Risk management Investment

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Advanced Brain Tumor Diagnosis using Haralick Texture Features and Multiclass SVM in MRI Imaging

Advanced Brain Tumor Diagnosis using Haralick Texture Featur...

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IEEE International Conference on automation, Quality and Testing, robotics, AQTR

作者： Shaik Karimullah Mohammed Mahaboob Basha Fahimuddin Shaik Srinivasulu Gundala Anca Stan Ovidiu Petru Stan Departmrnt of ECE AITS Rajampet Andhra Pradesh India Department of ECE Sreenidhi Institute of Science and Technology India Department of ECE Lakireddy Bali Reddy College of Engineering Mylavaram Andhra Pradesh Industrial Engineering Robotics and Production Management Technical University of Cluj Napoca Romania Department of Automation Automation and Computer Science Technical University of Cluj Napoca Romania

ISBN: (数字)9798350361933

ISBN: (纸本)9798350361940

This study employs contemporary computer techniques and magnetic resonance imaging (MRI) to tackle the critical issue of brain tumor classification. The proposed technique involves obtaining databases, preprocessing the data, extracting features, applying machine learning for classification, and conducting a thorough performance evaluation. The method enhances the visibility of tumors and enhances the characteristics of images by acquiring a diverse collection of MRI images and applying a series of preprocessing operations, including Otsu Binarization, Gray Level Thresholding, Morphological Operations, and Principal Component Analysis (PCA). Subsequently, the Haralick texture characteristics are extracted to depict the textural patterns observed in the pictures. The concept is based on utilizing a multiclass Support Vector Machine (SVM) to achieve accurate tumor classification. Our proposed technique demonstrated efficacy in accurately classifying brain tumors, as evidenced by an impressive accuracy rate of 99.764% and near-perfect ROC curve performance, with area under the curve (AUC) values close to 1. The findings emphasize the potential practicality of the technique and contribute to the advancement of computational methods and medical imaging in the field of brain tumor detection.

关键词： Support vector machines Training Accuracy Thresholding (Imaging) Magnetic resonance imaging Machine learning Feature extraction

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