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

共 24666 条记录，以下是461-470 订阅

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SMARTINDUSTRY 2024 - Proceedings of the 1st international conference on Smart automation and robotics for Future Industry

SMARTINDUSTRY 2024 - Proceedings of the 1st International Co...

引用

1st international conference on Smart automation and robotics for Future Industry, SMARTINDUSTRY 2024

The proceedings contain 16 papers. The topics discussed include: artificial intelligence for the future of construction;cobots and industrial robots;predictive maintenance for wind turbine bearings: an MLOps approach with the DIAFS machine learning model;development of an artificial intelligence tool and sensing in informatization systems of mobile robots;PCA-NuSVR framework for predicting local and global indicators of tunneling-induced building damage;design and deployment of data development toolkit in cloud manufacturing environments;research and development of image processing algorithms for effective recognition of various gestures in real time;machine learning models for the recognition of commands in smart home technologies;responsive dehydration: sensor-driven optimisation of production cycles in a solar dehydrator;and formation of the method of description and control of the relative position of the links of the upper limbs of the grip of an anthropomorphic robot.

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Research Progress in Cell Classification Detection Based on Deep Learning 14

Research Progress in Cell Classification Detection Based on ...

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14th IEEE international conference on Cyber Technology in automation, Control, and Intelligent Systems, CYBER 2024

作者： Gai, Dongze Zhang, Chuang Zhang, Yiwei Guo, Jiaduo Zhang, Qi Qin, Hengshen Dang, Dan School of Computer Science and Engineering Shenyang Jianzhu University Shenyang110168 China Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang110016 China University of Chinese Academy of Sciences Beijing100049 China School of Mechanical Engineering Shenyang Jianzhu University Shenyang110168 China School of Science Shenyang Jianzhu University Shenyang110168 China

ISBN: (纸本)9798331506056

This paper reviews the application of deep learning in cell classification detection, highlights the importance of convolutional neural networks in image analysis, and discusses the application of image preprocessing, feature extraction and deep learning models. Despite the progress made by deep learning techniques in improving cell classification accuracy, challenges such as data quality, model generalization and interpretability remain. This article suggests future research directions, including combining medical knowledge with deep learning and developing multimodal learning to improve the accuracy and efficiency of cell detection. Meanwhile, this article also explores the potential of label-free cell classification methods combining optically induced optically-induced dielectrophoresis (ODEP) techniques with deep learning to achieve cell classification without labeling, providing a new perspective on cell analysis. optically induced. © 2024 IEEE.

关键词： Contrastive Learning

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Efficient algorithms for distributed virtual force-based self-spreading of robotic sensor/actuator networks 7

Efficient algorithms for distributed virtual force-based sel...

引用

7th international conference on robotics, Control and automation Engineering, RCAE 2024

作者： Konovalov, P.A. Matveev, A.S. "sirius University" Mathematical Robotics Science Division Krasnodar Region Sirius City Russia Saint Petersburg University Department of Mathematics and Mechanics St. Petersburg Russia

ISBN: (纸本)9798350355642

Node deployment is a key issue in building sensor/actuator networks. To improve coverage and reduce costs, the use of autonomous mobile robotic nodes is an attractive option. This paper presents two new virtual force-based (VFB) distributed self-deployment algorithms for robotic networks. Their performance is compared with the baseline behaviors of five existing cutting-edge VFB methods. This analysis is conducted using a whole lot of standard criteria: elapsed time, coverage percentage, energy consumption, mean distance, and distance uniformity. Also, we propose and use a new criterion that assesses the balance of network's resources allocated to serve various equally valid subareas. Monte-Carlo computer experiments show that the proposed algorithms demonstrate the best performance regarding the coverage uniformity and percentage. © 2024 IEEE.

关键词： Multi agent systems

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Bouncing Robots in Rectilinear Polygons 26

Bouncing Robots in Rectilinear Polygons

引用

26th international conference on methods and models in automation and robotics

作者： Cagirici, Onur Bahoo, Yeganeh LaValle, Steven M. Ryerson Univ Dept Comp Sci Toronto ON Canada Univ Oulu Ctr Ubiquitous Comp Oulu Finland

ISBN: (数字)9781665468589

ISBN: (纸本)9781665468596;9781665468589

In this paper, we describe a bouncing strategy (smart strategy) for a mobile robot that uses one bit of memory for feedback, and guarantees that the robot will traverse all the rooms (and doorways) of a 2D environment. The environment is modeled as a rectilinear polygon (also called orthogonal polygon), and the rooms and the doorways are defined by the decomposition algorithm we describe. Such a decomposition helps the robot to not go back to a room after leaving. We also define the notion of "virtual doors" that have the ability to let the robot through, or make the robot bounce from them. We compared three different types of bouncing rules: smart, random, billiard. The smart strategy grantees to reach to target. Although the random strategy on average behaves the same as the smart strategy, there are rectilinear polygons in which the robot cannot reach the target in the expected time steps. On the other hand, the billiard bouncing strategy can cause the robot to become trapped.

关键词： automation Mobile robots Robots

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Novel Flapping-Wing Micro Aerial Vehicles Utilising Dynamic Amplification 9

Novel Flapping-Wing Micro Aerial Vehicles Utilising Dynamic ...

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9th international conference on Mechanical Engineering and robotics Research, ICMERR 2025

作者： Park, Moonsoo Abolfathi, Ali Mechanical Engineering University College London London United Kingdom

ISBN: (纸本)9798331509262

This paper introduces a novel design for flapping wing micro aerial vehicles (FWMAVs) that employs dynamic amplification. By utilising mechanical resonance, the design amplifies wing stroke and pitching motions, reducing the energy requirement while enhancing lift production. This approach also minimises the maximum strain on the actuator, thereby improving its structural integrity. A comprehensive mathematical model of the flapping mechanism is developed, incorporating the electromechanical characteristics of piezoelectric materials, nonlinear structural dynamics and aerodynamics. Numerical simulations using MATLAB and Simulink demonstrate the advantages of dynamic amplification, showing a significant improvement in lift generation compared to rigidly structured flapping systems. Using a practical actuator and wing setup, the model predicts a stroke angle of 45 °, a pitching angle of 67 °, a lift generation of 28.4 mN, and average input and output powers of 6.7 mW and 2.9 mW, respectively. This design approach offers a promising pathway for developing high-performance FWMAVs, with potential applications in autonomous flight operations. © 2025 IEEE.

关键词： Numerical models

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Continuous-Time Model-Free Adaptive Control for Nonlinear Plants 6

Continuous-Time Model-Free Adaptive Control for Nonlinear Pl...

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6th international conference on robotics, Control and automation Engineering, RCAE 2023

作者： Yu, Hao Li, Wangjiang School of Automation Beijing Institute of Technology Beijing China

ISBN: (纸本)9798350315301

This paper studies continuous-Time model-free adaptive control (MFAC) framework for solving set-point tracking problems of second-order nonlinear time-invariant plants. First, the dynamical linearization process for continuous-Time nonlinear plants is established for the first time. On the basis of the proposed dynamical linearization models, continuous-Time MFAC, which includes the forms of control inputs and adaptive laws for the involved parameter estimation, is designed. In the proposed continuous-Time MFAC, only input/output data is utilized and there are no parameters that need to be tuned. Then, a Lyapunov-based theoretical analysis is provided to show the global stability of the closed-loop system and the convergence of tracking errors. Finally, numerical simulations are given to illustrate the efficiency and feasibility of the proposed results. © 2023 IEEE.

关键词： Continuous time systems

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HYPER: Learned Hybrid Trajectory Prediction via Factored Inference and Adaptive Sampling 39

HYPER: Learned Hybrid Trajectory Prediction via Factored Inf...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Huang, Xin Rosman, Guy Gilitschenski, Igor Jasour, Ashkan McGill, Stephen G. Leonard, John J. Williams, Brian C. MIT Comp Sci & Artificial Intelligence Lab Cambridge MA 01239 USA Toyota Res Inst Cambridge MA 02139 USA

ISBN: (纸本)9781728196817

Modeling multi-modal high-level intent is important for ensuring diversity in trajectory prediction. Existing approaches explore the discrete nature of human intent before predicting continuous trajectories, to improve accuracy and support explainability. However, these approaches often assume the intent to remain fixed over the prediction horizon, which is problematic in practice, especially over longer horizons. To overcome this limitation, we introduce HYPER, a general and expressive hybrid prediction framework that models evolving human intent. By modeling traffic agents as a hybrid discrete-continuous system, our approach is capable of predicting discrete intent changes over time. We learn the probabilistic hybrid model via a maximum likelihood estimation problem and leverage neural proposal distributions to sample adaptively from the exponentially growing discrete space. The overall approach affords a better trade-off between accuracy and coverage. We train and validate our model on the Argoverse dataset, and demonstrate its effectiveness through comprehensive ablation studies and comparisons with state-of-the-art models.

关键词： Adaptation models Maximum likelihood estimation automation Predictive models Probabilistic logic Prediction algorithms Inference algorithms

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Dynamic Temporal Convolutional Networks for Fault Diagnosis of Rolling Bearings 7

Dynamic Temporal Convolutional Networks for Fault Diagnosis ...

引用

7th international conference on robotics, Control and automation Engineering, RCAE 2024

作者： Hu, Jingxiang Chen, Haojie Chen, Yongyi Zhang, Dan Zhejiang University of Technology Department of Automation Hangzhou310023 China

ISBN: (纸本)9798350355642

Since rolling bearing plays a crucial role in rotating machinery, the fault diagnosis research of it has been widely concerned in recent years. However, the collected fault signals often contain a lot of noise interference due to the complex and changeable operating environment. The anti noise capability of traditional deep learning methods is weak, resulting in poor feature extraction performance. Therefore, the dynamic temporal convolutional network (DyTCN) is presented for rolling bearing fault diagnosis in this article. Specifically, the causal dilation convolution is first designed to acquire fault feature of vibration signal by enlarging receptive field of the network. Then, the dynamically adjusted convolution kernel parameters are used to achieve adaptive acquisition of dynamic features and improve the robustness. Extensive experimental results on real dataset demonstrate that DyTCN has excellent accuracy in fault diagnosis. © 2024 IEEE.

关键词： Rotating machinery

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Employing Generative Artificial Intelligence in Replacement of Traditional Backend Systems 28th

Employing Generative Artificial Intelligence in Replacement ...

引用

2024 international conference automation

作者： Moroz, Artur Solohubov, Illia Oliinyk, Andrii Subbotin, Sergey Skrupsky, Stepan Natl Univ Zaporizhzhya Politech Zaporizhzhya Ukraine

ISBN: (纸本)9783031782657;9783031782664

This paper investigates the application and benefits of using Generative Artificial Intelligence (GenAI) techniques, with an emphasis on large language models (LLMs) as an innovative approach in the development of information systems. The focus lies primarily on systems employing open datasets where data confidentiality is not a focal concern. By replacing the traditional programmed logic with these advanced AI models, this paper presents the measurable improvements in the efficiency and adaptability of system design, development, and maintenance. The key advantages discussed include an enhanced rate and accuracy in query processing and response provision. Despite accentuating these substantial benefits, the paper acknowledges potential limitations with the alignment of these models to certain user-specific needs. A noteworthy component of this study includes an in-depth exploration of Large Language models usage and its implications in system interactivity and personalisation realms. The results suggest that the implementation of LLMs not only ensures precise and contextually relevant responses to user queries, but it also contributes to a significant reduction in time and resources expended during system design and development phases. Thus, replacing standard programmed logic with LLMs in systems utilizing open datasets accelerates the development timeline, simplifies maintenance procedures and boost the overall dynamism of the system.

关键词： Generative Artificial Intelligence Large Language models Information System Development Open Datasets Systems Query Processing Response Provision System Efficiency and Dynamism

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Search-Based Task Planning with Learned Skill Effect models for Lifelong Robotic Manipulation 39

Search-Based Task Planning with Learned Skill Effect Models ...

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IEEE international conference on robotics and automation (ICRA)

作者： Liang, Jacky Sharma, Mohit LaGrassa, Alex Vats, Shivam Saxena, Saumya Kroemer, Oliver Carnegie Mellon Univ Robot Inst Pittsburgh PA 15213 USA

ISBN: (纸本)9781728196817

Robots deployed in many real-world settings need to be able to acquire new skills and solve new tasks over time. Prior works on planning with skills often make assumptions on the structure of skills and tasks, such as subgoal skills, shared skill implementations, or task-specific plan skeletons, which limit adaptation to new skills and tasks. By contrast, we propose doing task planning by jointly searching in the space of parameterized skills using high-level skill effect models learned in simulation. We use an iterative training procedure to efficiently generate relevant data to train such models. Our approach allows flexible skill parameterizations and task specifications to facilitate lifelong learning in general-purpose domains. Experiments demonstrate the ability of our planner to integrate new skills in a lifelong manner, finding new task strategies with lower costs in both train and test tasks. We additionally show that our method can transfer to the real world without further fine-tuning.

关键词： Training Adaptation models Uncertainty Costs Data models Skeleton Planning

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