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

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Robust Fast-Terminal Back-Stepping Control for Three-Omni-Wheel Robot

Robust Fast-Terminal Back-Stepping Control for Three-Omni-Wh...

引用

2024 international conference on Control, robotics and Informatics, ICCRI 2024

作者： Hoang, Quoc-Dong Gam, Nguyen Thi Tuan, Le Anh An, Pham Duc Anh, Vu Tuan Institute of Mechanical Engineering Vietnam Maritime University Automation Group-Technology Innovation Team-Samsung VN Viet Nam School of Electrical and Electronic Engineering HUST Department of Automation Hanoi Viet Nam Institute of Mechanical Engineering Vietnam Maritime University Hai Phong Viet Nam Vietnam Maritime University Division of Automation in Shipbuilding Shipbuilding Faculty Viet Nam

ISBN: (纸本)9798350380309

This paper presents a robust fast-terminal back-stepping control approach applied to a three-omni-wheel robot. For highly maneuverable and flexible robotic systems as three-omni-wheel robots, achieving effective control and precision tracking is a significant challenge. Through analyzing the kinematic and dynamic features of the system, mathematical models are thoughtfully proposed. Based on the Lyapunov stability and finite-time stability criterion and back-stepping scheme, we developed a control algorithm that allows the robot to track to the desired velocity trajectory, thereby ensuring the robot follows the expected shape on the global coordinate despite the disturbance impacts. The numerical simulations compared with classical sliding mode control are then implemented to validate the effectiveness of the whole work. © 2024 IEEE.

关键词： Sliding mode control

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Innovative Approaches to Tourism Demand Prediction: A Genetic Algorithm Perspective 3

Innovative Approaches to Tourism Demand Prediction: A Geneti...

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3rd international conference on robotics, automation and Artificial Intelligence (RAAI)

作者： Al Jassim, Rasha S. Al Mansoory, Shqran El Hajjar, Said Al Maqbali, Hilal A. Al-Shanfari, Lamya UTAS Suar Dept Informat Technol Suhar Oman UTAS Shinas Dept Informat Technol Shinas Oman City Univ Dept Finance & Accounting Ajman U Arab Emirates UTAS Suhar Dept Informat Technol Suhar Oman UTAS Shalalah Dept Informat Technol Shalalah Oman

ISBN: (纸本)9798350307573;9798350307566

This paper emphasizes the importance of tourism demand forecasting, particularly in the context of sustainable development. It uses AI tools and machine learning to explore linear regression models and their optimization using Genetic Algorithms (GAs). In spite of the fact that GAs have been applied in a wide range of fields, their application in tourism research still needs to be explored. The primary objective of this study involves a comparative analysis of three distinct methodologies: LR-GA, linear regression implemented using SPSS Statistics, and linear regression implemented using SAS. This comparative evaluation aims to provide insights into the efficacy of these methods in addressing challenges relevant to the field of tourism. Additionally, the study seeks to investigate the potential contributions of genetic operators, namely the blend crossover and elitism selection mechanisms, in enhancing the predictive accuracy and overall performance of LR-GA. The results showcase the superior performance of the LR-GA algorithm, highlighting its potential for generating accurate predictive outcomes and facilitating informed decision-making in the tourism sector. The comprehensive presentation of the research methodology, findings, and their implications serves to advance the field's predictive capabilities and decision-making processes, fostering sustainable and data-driven practices within the tourism industry.

关键词： Tourism Demand Forecasting Linear Regression models Genetic Algorithms

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Modeling the dynamics of soft robots by discs and threads 39

Modeling the dynamics of soft robots by discs and threads

引用

IEEE international conference on robotics and automation (ICRA)

作者： Schultz, Joshua A. Sanders, Haley Phuc Duc Hong Bui Layer, Brett Killpack, Marc Univ Tulsa Dept Mech Engn Tulsa OK 74104 USA Brigham Young Univ Dept Mech Engn Provo UT 84602 USA

ISBN: (纸本)9781728196817

In this paper, we propose a new tractable ordinary differential equation formulation for dynamic simulation of fabric-reinforced inflatable soft robots. The method performs a lumped-parameter discretization of the continuum robot into discrete discs (inertia), spring elements, and threads (representing the inextensible fabric reinforcement). Using the repetition in the structure of the Lagrangian formulation of the dynamic equations of motion, a method is developed that outputs machine-readable analytical expressions for the equations of motion. The method does not require symbolic computation of derivatives. The recursive nature allows us to scale the model to an arbitrary number N discs, and can represent buckling, twisting, and pleating that is commonly seen in very soft robots. The expressions generated were validated against manually-derived equations of motion for the two-disc case using both Lagrangian and Newton-Euler means. A simulation environment which parses and evaluates the analytical expressions generated at run-time was used to numerically integrate and predict the response of a four-disc example robot. Trajectories observed varied smoothly and plausibly predicted the behavior envisioned in robots like these.

关键词： Computational modeling Dynamics Soft robotics Predictive models Mathematical models Numerical models Behavioral sciences

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Feature Extraction and Fusion for Anomaly Detection Using Hybrid Multi-Scale CNN-Transformer Model 7

Feature Extraction and Fusion for Anomaly Detection Using Hy...

引用

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

作者： Xu, Ning Chen, Fuyang Wang, Zijie He, Yi Qin, Shize Nanjing University of Aeronautics and Astronautics College of Automation Engineering Nanjing China

ISBN: (纸本)9798350355642

This paper explores the challenge of effectively detecting and analyzing human intrusion events using vibration signals from Distributed Fiber Optic Sensors (DFOS) in high speed train security monitoring. A novel Hybrid Multi-Scale CNN-Transformer Model is proposed to capture both frequency-domain and time-domain features from vibration signals. The model employs parallel networks - a multi-scale Convolutional Neural Network (CNN) for frequency-domain analysis and a Transformer network for time-domain analysis. The multi-scale CNN uses different convolution kernels to learn features at various time scales, enhancing its ability to capture diverse signal characteristics. Concurrently, the Transformer effectively models temporal dependencies and long-range relationships in the time domain, capturing sequential patterns and dynamics in the vibration signals. This dual approach aims to enhance signal understanding and analysis by integrating comprehensive information from both domains. It effectively handles the diversity and complexity of signals by capturing latent features even in inconsistent signal conditions. The experimental results show that the proposed method achieves superior classification performance. © 2024 IEEE.

关键词： Frequency domain analysis

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DeepCerviNet: Enhanced Cervical Cancer Classification Using Supervised Learning Model 3

DeepCerviNet: Enhanced Cervical Cancer Classification Using ...

引用

3rd IEEE international conference on robotics, automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2024

作者： Rahaman, Md Anikur Binte Rabbani Nuha, Maksura Tahsin, Md Hossain, Al Islam, Raihan Ul Rifat Ahmmad Rashid, Mohammad Reza, Ahmed Wasif Ripon, Shamim H. East West University Department of Computer Science and Engineering Dhaka Bangladesh International Islamic University Chittagong Department of Computer Science and Engineering Chittagong Bangladesh

ISBN: (纸本)9798331534400

Cervical cancer is a female-specific malignancy that poses a substantial threat worldwide, especially in less privileged and underdeveloped regions. Despite continuous progress and advances in medical science research, the rate of death from cervical cancer remains strikingly high among women. Therefore, accurate early-stage cervical cancer identification is imperative for effective diagnosis. It facilitates cost-effective and timely treatment to increase the survival rate of patients, leading to an optimal diagnosis improvement in disease classification. Deep learning approaches demonstrate superior accuracy in medical data classification, motivating us to consider these techniques in this research. In our study, we propose DeepCerviNet, a custom CNN model that achieves 99% precision, which exceeds the accuracy of other deep learning models, MobileNetV2 (97%), ResNET34 (95%), and GoogleNet (94%). Furthermore, to assess our model's generalizability, we use another Multicancer dataset where the proposed model classifies various cancers with 99% accuracy. The DeepCerviNet model's strong performance and adaptability can assist in identifying more aggressive or recurrent cervical cancer types, aiding the development of targeted therapeutic strategies. © 2024 IEEE.

关键词： Self-supervised learning

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Grouptron: Dynamic Multi-Scale Graph Convolutional Networks for Group-Aware Dense Crowd Trajectory Forecasting 39

Grouptron: Dynamic Multi-Scale Graph Convolutional Networks ...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Zhou, Rui Zhou, Hongyu Gao, Huidong Tomizuka, Masayoshi Li, Jiachen Xu, Zhuo Univ Calif Berkeley Dept Elect Engn & Comp Sci Berkeley CA 94720 USA Univ Michigan Dept Aerosp Engn Ann Arbor MI 48109 USA Univ Calif Berkeley Dept Mech Engn Berkeley CA 94720 USA Stanford Univ Dept Aeronaut & Astronaut Stanford CA 94305 USA

ISBN: (纸本)9781728196817

Accurate, long-term forecasting of pedestrian trajectories in highly dynamic and interactive scenes is a long-standing challenge. Recent advances in using data-driven approaches have achieved significant improvements in terms of prediction accuracy. However, the lack of group-aware analysis has limited the performance of forecasting models. This is especially nonnegligible in highly crowded scenes, where pedestrians are moving in groups and the interactions between groups are extremely complex and dynamic. In this paper, we present Grouptron, a multi-scale dynamic forecasting framework that leverages pedestrian group detection and utilizes individual-level, group-level and scene-level information for better understanding and representation of the scenes. Our approach employs spatio-temporal clustering algorithms to identify pedestrian groups, creates spatio-temporal graphs at the individual, group, and scene levels. It then uses graph neural networks to encode dynamics at different scales and aggregate the embeddings for trajectory prediction. We conducted extensive comparisons and ablation experiments to demonstrate the effectiveness of our approach. Our method achieves 9.3% decrease in final displacement error (FDE) compared with state-of-the-art methods on ETH/UCY benchmark datasets, and 16.1% decrease in FDE in more crowded scenes where extensive human group interactions are more frequently present.

关键词： automation Heuristic algorithms Clustering algorithms Benchmark testing Predictive models Prediction algorithms Graph neural networks

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Sensitivity Analysis and Extended Evaluation of the Two-Level Stochastic Model for the Lateral Movement of Vehicles within their Lane 10

Sensitivity Analysis and Extended Evaluation of the Two-Leve...

引用

10th international conference on automation, robotics, and Applications, ICARA 2024

作者： Neis, Nicole Beyerer, Juergen Porsche Engineering Group GmbH Weissach Germany Fraunhofer IOSB and Vision and Fusion Lab Karlsruhe Germany

ISBN: (纸本)9798350394245

The range of vision of vehicle sensors used by auto-mated driving functions is considerably influenced by the lateral movement of vehicles within their lane, both of the recording vehicle and of vehicles around it. To cover this phenomenon in simulations for virtual validation, a two-level stochastic model for the lateral movement of vehicles within their lane under homogeneous traffic conditions has recently been proposed by Neis et al. [1]. It consists of the superposition of a Markov model for the discrete and systematic coarse movement, and a noise model for the stochastically independent fine movement. The general importance of the parameter choice on the performance of the two-level model has already been noted when the model was introduced. More details are added by the present work performing a sensitivity analysis on the model's parameters. Moreover, the evaluation of the model is extended by assessing its capability to describe the lateral speed. The work deepens the understanding of the model's way of working and its sensitivity to the parameters, and motivates the parameter choice in previous work. Besides this, potential for improvement of the model beyond what has already been discussed in the introductory paper is identified based on the extended investigation. © 2024 IEEE.

关键词： Stochastic models

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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...

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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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Bouncing Robots in Rectilinear Polygons 26

Bouncing Robots in Rectilinear Polygons

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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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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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