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检索条件"任意字段=Proceedings of the 3rd International Conference on Mechatronics and Robotics Engineering"

共 6212 条记录，以下是41-50 订阅

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Zonotope-based fault estimation for Lipschitz nonlinear systems 3

Zonotope-based fault estimation for Lipschitz nonlinear syst...

引用

2024 3rd international conference on Aerospace and Control engineering, ICoACE 2024

作者： Ma, Siyuan Zhang, Wei Jiang, Qiang Gao, Sheng School of Automatic and Electrical Engineering Shenyang Ligong University Shenyang110158 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China China-Portugal Belt and Road Joint Laboratory on Space & Sea Technology Advanced Research Shanghai200120 China

An estimation of fault intervals using a T-N-L structured observer is suggested for Lipschitz nonlinear systems. By augmenting state vectors, the augmentation system and its observer are established, enabling the observer to concurrently estimate fault and system states. The L∞ performance is applied to enhance the resilience of the fault interval estimation to external disturbances and unanticipated inputs. An adequate requirement for the observer design is also presented. The interval estimation of states and faults is ultimately created by using a zonotope. The proposed method exhibits advantages and feasibility when compared to existing simulation techniques. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Nonlinear systems

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Yueqian AI teaching assistant system for improvement of teaching effectiveness 3

Yueqian AI teaching assistant system for improvement of teac...

引用

3rd international conference on Artificial Intelligence and Education, ICAIE 2024

作者： Ma, Haohao Li, Chaoqun Zhang, Xiang Luo, Haiyu Wu, Xuping Yang, Lijuan Yan, Xiaoxu Kang, Zhilin School of Mechatronics and Automotive Engineering Tianshui Normal University Gansu Tianshui China School of Resources and Environmental Engineering Tianshui Normal University Gansu Tianshui China

ISBN: (纸本)9798400712692

With the widespread application of Artificial Intelligence (AI) technology in production and life, this study is committed to exploring the application of AI technology in the field of education, especially higher education. The current mainstream AI education functions are analyzed, including knowledge graphs, intelligent question and answer, digital human teaching, etc. The research team developed the Yueqian AI teaching assistant system in combination with the current Large Language Model (LLM) technology. To evaluate and validate the practicality of the system, two groups of students who took the basic mechanical design course were selected as samples. Statistical results show that students using the Yueqian AI teaching assistant system show higher participation and satisfaction than students using traditional teaching methods. In the future, the Yueqian AI teaching assistant team will try more exploration and exploitation to improve teaching effectiveness. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： knowledge graphs Teaching assistant Teaching efficiency Yueqian AI

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A Self-Aware Robotic Machining Architecture Based on Physics-Informed Neural Networks

A Self-Aware Robotic Machining Architecture Based on Physics...

引用

mechatronics, Control and robotics (ICMCR), international conference on

作者： Yuanhui Zhang Mingfeng Wang Kai Wu Shien-Ming Wu School of Intelligent Engineering South China University of Technology Guangzhou China Department of Mechanical and Aerospace Engineering Brunel University London UK

ISBN: (数字)9798331544546

ISBN: (纸本)9798331544553

Industrial robots have been widely used in various industrial scenarios due to their flexibility and expandability. However, robots are susceptible to instability at high speeds and load conditions due to their low stiffness and dynamic characteristics variation with posture. Enhancing the processing stability of industrial robots through data-driven modeling and physical modeling approaches suffers from different drawbacks, such as solution complexity and weak interpretability. With the emergence of physics-informed neural networks (PINNs), new methodologies can be developed to enhance the self-aware processing of robots. In this paper, typical industry application scenarios and dynamics of robotics as well as PINNs are introduced and analyzed, and a framework and method based on PINNs are proposed to enhance the self-aware operation of industrial robots. This framework and methodology contribute to the researcher's efforts to apply PINNs more intensively to robot operation in the future to improve the stability and intelligence of robot operation.

关键词： mechatronics Service robots Neural networks Machining Milling Industrial robots Prediction algorithms Stability analysis Thermal stability Self-aware

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Evaluating the Efficiency of Dual-Zone Registration System with Timer Mechanism in Wireless Networks 3

Evaluating the Efficiency of Dual-Zone Registration System w...

引用

3rd international conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Raman, Ramakrishnan Vinoth, T. Barve, Amit Tiwari, Vinita Pune India Chennai Institute of Technology Department of Mechatronics Engineering Kundrathur Chennai India Paruluniversity Faculty of Engineering and Technology Department of Computer Science and Engineering Vadodara India Pune India

ISBN: (纸本)9798331508456

The increasing demand for efficient communication in wireless networks necessitates optimized registration systems that ensure smooth connectivity and scalability. This paper introduces a novel Dual-Zone Registration System with a Dynamic Timer Mechanism designed to enhance registration efficiency in wireless networks. The system classifies devices into two zones: a primary zone for immediate registration and a secondary zone for devices requiring delayed registration. Leveraging a dynamic timer mechanism, the system regulates registration attempts to reduce congestion and optimize resource allocation. Simulations reveal that the proposed system significantly improves key performance metrics, including registration success rate, average registration time, packet loss, and network load. The dual-zone system achieved a 95% registration success rate, reducing packet loss to 3% compared to 12% in centralized systems and 8% in simple timer-based systems. Average registration time decreased to 6.3 seconds, outperforming centralized (12.5 seconds) and simple timer-based systems (9.8 seconds). Additionally, the system maintained a lower network load of 45 requests per second, showcasing its scalability and efficiency. By dynamically adjusting registration delays based on real-time network conditions, the proposed system ensures efficient resource utilization and balanced load distribution, making it a promising solution for large-scale wireless networks. These findings validate the effectiveness of the dual-zone approach in improving the scalability, reliability, and overall performance of modern wireless networks. © 2025 IEEE.

关键词： Resource allocation

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Underwater Object Detection Using Deep Learning Techniques 3

Underwater Object Detection Using Deep Learning Techniques

引用

3rd international conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Samanta, Sayan Malik, Parveen Samanta, Pravin Kumar KIIT Deemed to be University School of Electronics Engineering Odisha Bhubeneswar India TIET Deemed to be University ECE Department Punjab Patiala India

ISBN: (纸本)9798331523893

Underwater image detection plays a vital role in various marine applications such as oceanography, underwater robotics, and environmental monitoring. The challenges of underwater imaging, such as low visibility, distortion, and noise due to water quality and lighting conditions, make effective detection techniques crucial. This paper proposes an underwater image detection approach using the YOLOv8 (You Only Look Once version 8) model, a state-of-the-art deep learning architecture known for its efficiency in real-time object detection tasks. YOLOv8's ability to balance high accuracy and computational efficiency is leveraged to detect various objects in underwater environments, addressing challenges like water turbidity and poor lighting. A dataset of underwater images with annotated objects is used to train the model, evaluating its accuracy, speed, and robustness to distortions. Experimental results demonstrate that YOLOv8 outperforms traditional methods in detecting underwater objects with mAP50 of 80% and faster inference times, making it a promising solution for real-time underwater image analysis in diverse applications. © 2025 IEEE.

关键词： Deep Learning Model Efficiency Object Detection RE-DETR Underwater Image Detection YOLO Algorithm YOLONAS YOLOv5 YOLOv8

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Adaptive Reduced Fuzzy Decoupling Control for Lower Limb Exoskeleton with Uncertainties 3rd

Adaptive Reduced Fuzzy Decoupling Control for Lower Limb Exo...

引用

3rd international conference on Advances in Information and Communication Technology, ICTA 2024

作者： Nguyen, Van-Truong Tran, Duc-Duong Le, Ngoc-Duy Dang, Van-Truong School of Mechanical and Automotive Engineering Hanoi University of Industry Hanoi Viet Nam Faculty of Engineering Technology Hung Vuong University Viet Tri Viet Nam

ISBN: (纸本)9783031809422

In this paper, an adaptive reduced fuzzy decoupling controller is developed for a lower limb rehabilitation exoskeleton robot (2-DOF) in the case of uncertainties. With decoupled control, the initial overall MIMO system is separated into a set of individual MISO subsystems for independent control. A simplified adaptive fuzzy is proposed to reduce computation time, addressing the issues of traditional fuzzy systems. The controller has good control quality, tracking the human gait motion trajectory with uncertainty. Simulation results show that the proposed controller performs better than the traditional fuzzy controller. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Exoskeleton (robotics)

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Temporal Object Detection in Videos Using Spatio-Temporal Transformers 3

Temporal Object Detection in Videos Using Spatio-Temporal Tr...

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3rd international conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Shankar, K. Sekar, K. Renuka Devi, S. Monish, B. Prathap, V. Praveen, R. National Institute of Technology Department of Eie Silchar India Rajalakshmi Engineering College Department of Aiml Chennai India Rajalakshmi Engineering College Department of Aids Chennai India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508456

Object detection in videos presents challenges such as motion blur, occlusions, and varying viewpoints, which traditional image-based models struggle to handle due to their inability to utilize temporal information. To overcome these limitations, this work introduces Spatio-Temporal Transformer-based Object Detection (ST-TOD), a novel framework that integrates spatial and temporal features for enhanced object detection and tracking in video sequences. The model combines Convolutional Neural Networks (CNNs) for spatial feature extraction with Transformer architectures to capture long-range dependencies across frames. A deformable attention mechanism dynamically focuses on key regions, mitigating motion blur and occlusions by emphasizing salient features. Experimental evaluations on benchmark video datasets demonstrate the effectiveness of ST-TOD, achieving 97 % accuracy, 87 % precision, and 90 % recall, significantly improving detection accuracy and robustness in complex environments. Its high efficiency makes it suitable for real-time applications such as video surveillance, autonomous driving, and robotics. By leveraging the strengths of spatial and temporal modeling, ST-TOD offers a powerful and adaptive solution for advanced video object detection tasks. © 2025 IEEE.

关键词： Convolutional neural networks

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2014 3rd international Congress of engineering mechatronics and Automation, CIIMA 2014 - conference proceedings

2014 3rd International Congress of Engineering Mechatronics ...

引用

2014 3rd international Congress of engineering mechatronics and Automation, CIIMA 2014

ISBN: (纸本)9781479979325

The proceedings contain 49 papers. The topics discussed include: implementation of an image based focusing algorithm for non-mydriatic retinal imaging;comparison of a super-twisting differentiator and a Luenberger observer for an inertia wheel pendulum: an experimental approach;design and construction of a classifier of pieces using visual servoing;decentralized cooperative control technique for non-holonomic mobile formation;robotic explorer to search people through face detection;analysis for the design of a unipersonal electric vehicle prototype with photovoltaic panels;localization and mapping approximation for autonomous ground platforms, implementing slam algorithms;design and simulation of a discrete time controller for regulating temperature in a scale greenhouse;and motion capture system for applications in robotics.

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Using Sliding Mode Controller to Trajectory Tracking of the Four Mecanum Wheel Mobile Robot Applied in Automotive Plants

Using Sliding Mode Controller to Trajectory Tracking of the ...

引用

mechatronics, Control and robotics (ICMCR), international conference on

作者： Tran Vo Dang Huy Bui Thanh Luan HUTECH Institute of Engineering HUTECH University Ho Chi Minh City Viet Nam Industrial Maintenance Training Center Ho Chi Minh City University of Technology (HCMUT) Ho Chi Minh City Vietnam Vietnam National University Ho Chi Minh (VNU-HCM) Thu Duc city Ho Chi Minh City Vietnam

ISBN: (数字)9798331544546

ISBN: (纸本)9798331544553

This research paper proposes a mobile robot configuration tailored to optimize transportation processes for the Skoda automobile manufacturing plant. When operating in a factory, the FMWMR is subject to external disturbances and uncertainties, so the sliding mode control (SMC) method is introduced. The control law is derived by selecting an appropriate Lyapunov function based on the robot's dynamic model. Simulation results in Matlab demonstrate the effectiveness of the proposed control algorithm in enabling the robot to follow predefined trajectories.

关键词： Uncertainty Trajectory tracking Simulation Wheels Production facilities Hardware Manufacturing Mobile robots MATLAB Sliding mode control

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Geometric Control Methods for On-Orbit Assembly of Modular Spacecraft Cluster

Geometric Control Methods for On-Orbit Assembly of Modular S...

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mechatronics, Control and robotics (ICMCR), international conference on

作者： Jingxian Wang Rong Chen Yuzhu Bai Yong Zhao College of Aerospace Science and Engineering National University of Defense Technology Changsha China

ISBN: (数字)9798331544546

ISBN: (纸本)9798331544553

In this paper, a cluster geometry control method is proposed for the problem of on-orbit assembly of modular spacecraft clusters. The method bases the cluster system on a manifold. A collision avoidance constraint that does not generate terminal position mutual exclusion is designed for the characteristics of the assembly problem. Assembly and task time constraints are further considered to integrate the non-convex multiple constraints with the system metric matrix. Finally, model predictive control is used to realize tracking control. Simulation results show that the method can arrive at the target position to complete the on-orbit assembly within any initial position and different fixed time constraints and achieve the purpose of energy optimization.

关键词： Space vehicles Manifolds Target tracking Self-assembly Trajectory planning Simulation Trajectory Time factors Collision avoidance Assembly

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