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检索条件"机构=Automation Robotics Technology"

共 3411 条记录，以下是151-160 订阅

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Tool Wear Estimation Using Few-Shot Learning in Deep Neural Network Architectures

Tool Wear Estimation Using Few-Shot Learning in Deep Neural ...

引用

MIT Art, Design and technology School of Computing International Conference (MITADTSoCiCon)

作者： Aarin Pandey Ananya Shetty Sameer Sayyad Vivek Warke Satish Kumar Arunkumar Bongale Dept. of Robotics and Automation Symbiosis Institute of Technology Pune India

ISBN: (数字)9798350362879

ISBN: (纸本)9798350362886

This research leverages the specialized machine learning technique known as few-shot learning to make precise predictions of cutting tool wear during milling, even when provided with limited training data. Employing a meta-learning approach, the model demonstrates exceptional generalization capabilities from a constrained dataset, enabling seamless adaptation to new tasks with minimal examples. Utilizing a foundational deep neural network trained on a small dataset, novel models are constructed, showcasing remarkable accuracy when presented with previously unseen data. These findings underscore the distinct advantages of few-shot learning over traditional methodologies, delivering highly accurate tool wear predictions while significantly reducing computational time. Furthermore, this approach shows potential for dealing with data-intensive circumstances in real-world applications. Ultimately, the research has the potential to revolutionize machining processes by increasing tool longevity, reducing downtime, and lowering tool replacement costs.

关键词： Metalearning Adaptation models Analytical models Accuracy Time series analysis Training data Artificial neural networks

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Design of linear systems with desired poles and zeros of the transfer matrices

Design of linear systems with desired poles and zeros of the...

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International Conference on Methods and Models in automation and robotics (MMAR)

作者： Tadeusz Kaczorek Department of Automation and Robotics Bialystok University of Technology Białystok Poland

A new method for the design of linear systems with desired poles and zeros of their transfer matrices is proposed. Conditions for the existence of the solution to the problem and the procedure for computation of the d... 详细信息

ISBN: (数字)9798350362343

ISBN: (纸本)9798350362350

关键词： Linear systems automation Design methodology Computational modeling Numerical models Poles and zeros Robots

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Modeling of dynamic-elastic deformation error for a collaborative robot 18

Modeling of dynamic-elastic deformation error for a collabor...

引用

18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023

作者： Zhu, Shijing Pan, Xinan Wang, Hongguang Li, Dianqi School of Mechanical Engineering Shenyang University of Technology Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences China

ISBN: (纸本)9798350312201

In the operation of a collaborative robot, the inertial force, gravity force, and other factors can cause elastic deformation of links and joints, and hence results in considerable deviations from the desired trajectory. However, the effects of inertial force, gravity force, and Jacobian matrix are rarely considered in common deformation analyses. This paper proposes a method for modeling dynamic elastic deformation of collaborative robots including joint torsional stiffness and complementary stiffness. Firstly, the dynamics equation was established based on the Newton-Euler method, and the driving torque of the joint was analyzed. Secondly, a dynamic-elastic deformation error model was established based on the joint torsional stiffness and complementary stiffness. Finally, the simulation results show that the established dynamic error model has good predictability, and the relative error is less than 1.63% compared with the ADAMS simulation results. © 2023 IEEE.

关键词： Errors

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Corrosion Image Classification using Deep Learning and Transfer Learning Approaches

Corrosion Image Classification using Deep Learning and Trans...

引用

Innovation in technology (ASIANCON), Asian Conference on

作者： Aarya Misal Sameer Sayyad Dept. of Robotics and Automation Symbiosis Institute of Technology Pune India

ISBN: (数字)9798350354218

ISBN: (纸本)9798350354225

Corrosion, a pervasive challenge across numerous industries, undermines material integrity and incurs substantial economic costs. Traditional methods of corrosion detection are often labour-intensive and prone to human error. Innovative solutions for timely and accurate identification are needed. This study explores applying Deep Learning (DL) and Transfer Learning (TL) techniques for classifying corrosion images into five distinct classes. This work uses the Worcester Polytechnic Institute's dataset, which consists of 600 corrosion image samples expertly rated according to the ASTM D1654 standard. This dataset is used to develop and compare the performance of custom Convolutional Neural Networks (CNNs) with TL approaches using VGG16 and ResNet50 models. The results indicate that TL models significantly outperform the custom CNN in training and validation accuracy, with VGG16 achieving the highest validation accuracy of 90% after fine-tuning. This work highlights the efficacy of leveraging pre-trained models and fine-tuning them for specific tasks. The results lay the groundwork for developing robust and generalized models capable of excelling in diverse real-world scenarios, enhancing maintenance strategies, improving safety, and significantly reducing costs across various industries.

关键词： Deep learning Training Accuracy Corrosion Biological system modeling Transfer learning Maintenance Convolutional neural networks Residual neural networks Image classification

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A Predictive Model for Morphogenesis of a Robot Sequence Mimicking Protein Folding 14

A Predictive Model for Morphogenesis of a Robot Sequence Mim...

引用

14th IEEE International Conference on Cyber technology in automation, Control, and Intelligent Systems, CYBER 2024

作者： Ni, Zihao Han, Zhi Wang, Shixian Gu, Yuping Yang, Yongliang School of Artificial Intelligence Shenyang University of Technology Liaoning Shenyang110023 China Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang110016 China

ISBN: (纸本)9798331506056

The swarm phenomena observed in nature have inspired many control algorithms for diverse robot swarm systems. However, most of these endeavors are qualitative simulations aimed at understanding the mechanisms underlying biological swarm behavior. Reliably predicting morphologies of robot swarms from their features remains a formidable challenge. In molecular biology, the 3D structure of a protein molecule, which is a main factor of its functions, is mainly determined by its sequence of amino acids. Inspired by this, our lab developed RoboFold, a robot swarm platform for developing protein-folding-inspired methods controlling the morphologies of robot swarms. Using the data collected from this platform, we proposed a deep learning approach to predict the morphologies of swarm robotics based on their initial sequences. The goal of this research is predicting the initial sequence of robots forming the desired morphology. Preliminary results presented here demonstrated the efficacy of this method and underscore the feasibility of applying protein-folding-inspired methods to program robot swarms. © 2024 IEEE.

关键词： Microrobots

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A Locust-Inspired Energy Storage Joint for Variable Jumping Trajectory Control 16th

A Locust-Inspired Energy Storage Joint for Variable Jumping ...

引用

16th International Conference on Intelligent robotics and Applications, ICIRA 2023

作者： Yang, Yongzun Feng, Zhiyuan Jin, Cheng Tang, Lingqi Ma, Songsong Li, Yao School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen518052 China State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin150001 China Guangdong Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics Harbin Institute of Technology Shenzhen518052 China

ISBN: (纸本)9789819964949

Jumping is a good solution for small robots over obstacles. Most of the current jumping robots are not energy store adjustable due to the design of the energy storage elements and structures, which limits the effective working space of the robot. The locust is good at jumping. Thanks to the excellent structure of the hind legs, the locust can change the degree of compression of the simi-lunar process (SLP) and change the energy storage while maintaining the same jumping stance. Herein, we design a locust-inspired energy storage joint and verified its function on a jumping robot. The motors and wires were used to imitate the muscles and the torsion springs were used to imitate SLP. To accurately describe the energy stored, a static model of the torsion springs was developed. Furthermore, the number of motor revolutions and the stored energy value were also calculated and could be used for subsequent precise control. Six jumping experiments with different compression angles for torsion springs proved the feasibility of the static model. This locust-inspired energy storage joint is the basis for the next robot capable of the omnidirectional, continuous autonomous jumping. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Energy storage

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Deep Reinforcement Learning for Mapless Navigation of Autonomous Mobile Robot 27

Deep Reinforcement Learning for Mapless Navigation of Autono...

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27th International Conference on System Theory, Control and Computing, ICSTCC 2023

作者： Yadav, Harsh Xue, Honghu Rudall, Yan Bakr, Mohamed Hein, Benedikt Rueckert, Elmar Nguyen, Ngoc Thinh Inst. for Robotics and Cognitive Systems University of Lübeck Luebeck Germany Robotics and Automation KION Group AG Hamburg Germany Inst. of Automation Technology Helmut-Schmidt-University Hamburg Germany Inst. for Cyber Physical Systems Montanuniversität Leoben Leoben Austria

ISBN: (纸本)9798350337983

This paper presents a study on the mapless navigation of autonomous mobile robot using Deep Reinforcement Learning in an intralogistics setting. The task is to make an autonomous mobile robot learn to navigate to a goal without prior knowledge of the environment. In this paper, a controller using the Soft Actor-Critic algorithm is designed, trained, and applied for navigating the robot equipped with 360° LiDAR and front camera sensors. The controller is successfully validated in an almost fully observable environment under extensive simulations. Furthermore, we investigate the performance of the proposed controller in a partially observable environment and possible limitations. We use a 3D Temporal Convolution Network for processing the time series image data from visual observations. Besides Partial Observability, we also address the problem of sparse positive rewards in training the Deep Reinforcement Learning algorithm with a combined approach of Automatic Curriculum Learning and Dual Prioritized Experience Replay. © 2023 IEEE.

关键词： Reinforcement learning

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Comparative Study of Anti-Swing Controllers for a Triple Link Rotary Pendulum System 8

Comparative Study of Anti-Swing Controllers for a Triple Lin...

引用

8th IEEE International Conference on Engineering Technologies and Applied Sciences, ICETAS 2023

作者： Hazem, Zied Ben Bingul, Zafer University of Technology Bahrain Department of Mechatronics Engineering 18041 Bahrain Kocaeli University Automation and Robotics Laboratory Department of Mechatronics Engineering Kocaeli41001 Turkey

ISBN: (纸本)9798350327090

This paper presents a study of the development of anti-swing control of a triple link rotary pendulum system (TLRPS). The motion nonlinear equations of the TLRPS are obtained using the Euler-Lagrange method based on the kinematic model and Denavit-Bartenberg (DH) convention. In this study three anti-swing controllers, linear-quadratic regulator (LQR), fuzzy linear-quadratic regulator (FLQR), and radial basis neuro-fuzzy linear quadratic regulator (RBNFLQR) are developed to control and eliminate the swing angles of the three links during the rotation. Moreover, the performance characteristics of the controllers are compared and examined in terms of their time response specifications parameters and swing angle elimination. Moreover, the robustness of controllers is tested under external torque disturbance. The simulation and experiment results of the response of the TLRPS are presented in time domains. Simulation and experiment results exhibit excellent control performance and robustness in terms of position control and damping oscillations elimination. © 2023 IEEE.

关键词： Controllers

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Enhanced Surveillance Abilities Through Object Detection in Autonomous Robots

Enhanced Surveillance Abilities Through Object Detection in ...

引用

IEEE International Conference on System, Computation, automation and Networking (ICSCAN)

作者： J.V. Pesha Golvainbec Jotham Thirumalai C.K. Nithish Kumar. V Sasirekha. M Dept. of Robotics and Automation Manakula Vinayagar Institute of Technology Puducherry

ISBN: (数字)9798331510022

ISBN: (纸本)9798331510039

The system emphasizes the operation of an autonomous environment monitoring robot with the capability of object detection and tracking using OpenCV and mobile application, we gather real-time video data from the environment with the help of an ESP-32 cam module and cast the data to the respective mobile application forum for the visualization and analysis of the surrounding space with the help of WIFI (wireless fidelity). We use an ESP-32 camera-based autonomous surveillance robot integrated with a camera and ESP-32 microcontroller for real-time object detection using OpenCV with the help of pretrained models like mobile net, tensor flow, etc. The ESP-32 cam module processes images controls motors for movement and communicates wirelessly for security applications offering a versatile solution for tracking and monitoring operations.

关键词： Tensors Tracking Surveillance Object detection Streaming media Real-time systems Mobile applications Trajectory Security Wireless fidelity

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Milling Tool Wear Estimation Using Machine Learning with Feature Extraction Approach

Milling Tool Wear Estimation Using Machine Learning with Fea...

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MIT Art, Design and technology School of Computing International Conference (MITADTSoCiCon)

作者： Aarya Misal Hrishikesh Karandikar Sameer Sayyad Arunkumar Bongale Satish Kumar Vivek Warke Dept. of Robotics and Automation Symbiosis Institute of Technology Pune India

ISBN: (数字)9798350362879

ISBN: (纸本)9798350362886

A cutting tool in any milling operation eventually becomes less efficient due to wear, which could result in reduction of quality and dimensional accuracy of the product during manufacturing. Precise and continuous monitoring is required to avoid these issues in machining. Manual inspection and arbitrary assessment have traditionally been used to identify faults and categorise machine tool wear. However, this method is time consuming and is prone to errors. The purpose of this study is to offer more effective, reliable, and accurate methods to predict tool wear using various data-driven machine learning models, which can then be used to determine Remaining Useful Life (RUL) of the milling tool. This paper implements the IEEE NUAA Ideahouse tool wear dataset containing various sensor readings and makes use of the TSFEL (Time Series Feature Extraction Library) for automatic feature extraction in the temporal, spectral and statistical domain, followed by the implementation of PCC (Pearson's Correlation Coefficient) for the selection of features that have a significant impact on tool wear. ML models such as K-Nearest Neighbors, Decision Tree, and Random Forest provides fairly good results with scaled data followed by feature extraction. All the models are evaluated based on MSE, MAPE and R 2 score, by passing unscaled and scaled selected features in temporal, spectral and statistical domains to find out the model best suited for accurate tool wear prediction in order to assist in predictive maintenance.

关键词： Accuracy Time series analysis Milling Manuals Predictive models Feature extraction Manufacturing

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