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

共 24757 条记录，以下是401-410 订阅

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Frequency weighted model reduction in approximation of nabla difference-based discrete-time fractional-order systems 26

Frequency weighted model reduction in approximation of nabla...

引用

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

作者： Stanislawski, Rafal Rydel, Marek Bialic, Grzegorz Opole Univ Technol Dept Elect Control & Comp Engn Ul Proszkowska 76 PL-45758 Opole Poland

ISBN: (数字)9781665468589

ISBN: (纸本)9781665468596;9781665468589

The article presents new results in terms of Frequency Weighted (FW) model reduction method in application to approximate LTI discrete-time non-commensurate fractional-order systems based on the Grunwald-Letnikov nabla fractional-order difference. The method applies the Fourier-based decomposition of the system and the FW model order reduction method. The main advantage of the proposed modeling approach is the specific representation of the fractional-order system enabling a simple, analytical formula for the determination of the Gramians. This significantly improves the computational efficiency of the proposed FW reduction method. The simulation experiments confirm the low computational effort of the introduced processing and its high modeling accuracy

关键词： Linear systems automation Finite impulse response filters Computational modeling Systems modeling Controllability Reduced order systems

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Advances and Challenges in Automated Agriculture: Integrating 5G Wireless Technology with IoT, Cloud Computing and robotics 5

Advances and Challenges in Automated Agriculture: Integratin...

引用

5th international conference on Electrical, Communication and Computer Engineering, ICECCE 2024

作者： Li, Xinwei Kang, Chia Chao Ng, Zi Neng Minhad, Khairun Nisa' School of Electrical Engineering and Artificial Intelligence Xiamen University Malaysia Sepang Malaysia

ISBN: (纸本)9798331529437

The agricultural sector is undergoing a transition as automation and robotics converge and evolve. The current labor shortage and the growing need to increase yields are driving innovations in automated agriculture. Automated systems are gradually replacing traditional farming that relies on manpower. The Internet of Things (IoT) with 5G technology has become the key to these traditional automated irrigation systems. The system collects data from soil moisture, rainfall and temperature sensors thus uploads it to the cloud to store data and analyze it to make intelligent irrigation decisions. The proposed innovative system uses iterative learning algorithms and training models to identify pests, diseases and flower types. This article discusses in depth the model evolution of automation technology in the agricultural sector, IoT and cloud computing applications in automated agriculture and the growing use of robotics in agricultural operations. Besides that, it also discusses future directions and challenges. In addition, this article explores the potential for cross-platform collaboration and data sharing to advance the progress of this automation model. However, the field still faces challenges due to the decentralized nature of technology development. © 2024 IEEE.

关键词： Smart agriculture

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Analysing the Roles of robotics in Manufacturing Organizations in the Era of Industry 4.0

Analysing the Roles of Robotics in Manufacturing Organizatio...

引用

2024 international conference on Science, Engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Adetunla, Adedotun Akinlabi, Esther Jen, Tien Chien Ajibade, Samuel-Soma University of Johannesburg Dept. of Mechanical Engineering Science South Africa Northumbria University Dept. of Mechanical & Construction Engineering Newcastle United Kingdom Sunway University School of Engineering & Technology Dept. of Computing & Information Systems Malaysia

ISBN: (纸本)9798350358155

The era of Industry 4.0 has ushered in a new approach in manufacturing, leveraging advanced technologies to transform traditional production methods. robotics is the science of using robots to improve manufacturing processes with automation, flexibility, and efficiency. This article aims to comprehensively examine the role of robotics in manufacturing within the context of Industry 4.0. It seeks to explore the fundamental principles underlying robotics, their integration with Industry 4.0 technologies, applications across diverse manufacturing sectors, challenges faced in implementation, and the future directions of robotics in shaping the manufacturing landscape. © 2024 IEEE.

关键词： Smart manufacturing

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Intelligent Humanoid Robots in Manufacturing Addressing the Worker Shortage and Skill Gaps in Assembly Cells 19

Intelligent Humanoid Robots in Manufacturing Addressing the ...

引用

19th Annual ACM/IEEE international conference on Human-Robot Interaction (HRI)

作者： Malik, Ali Ahmad Masood, Tariq Brem, Alexander Oakland Univ Dept Ind & Syst Engn Rochester MI 48309 USA Univ Strathclyde Design Mfg & Engn Management Glasgow Lanark Scotland Univ Stuttgart Inst Entrepreneurship & Innovat Sci Stuttgart Germany

ISBN: (纸本)9798400703232

Technological evolution in the field of robotics is emerging with major breakthroughs in recent years. This was especially fostered by revolutionary new software applications leading to humanoid robots. Humanoids are being envisioned for manufacturing applications to form human-robot teams. But their implication in the manufacturing practices especially for industrial safety standards and lean manufacturing practices have been minimally addressed. Humanoids will also be competing with conventional robotic arms and effective methods to assess their return on investment are needed. To study the next generation of industrial automation, we used the case context of Tesla's humanoid robot. The company has recently unveiled its project on an intelligent humanoid robot named 'Optimus' to achieve an increased level of manufacturing automation. This article proposes a framework to integrate humanoids for manufacturing automation and also presents the significance of safety standards of human-robot collaboration. A case of lean assembly cell for the manufacturing of an open-source medical ventilator was used for human-humanoid automation. Simulation results indicate that humanoids can increase the level of manufacturing automation. Managerial and research implications are presented.

关键词： automation Humanoid Robots Assembly Lean

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Deep Learning Classification of Concrete Crack Images using Pre-trained AlexNet and GoogleNet models

Deep Learning Classification of Concrete Crack Images using ...

引用

2024 international conference on Control and automation, Electronics, robotics, Internet of Things, and Artificial Intelligence, CERIA 2024

作者： Jusman, Yessi Tyassari, Wikan Nadia, Liza Kurniawati, Ranti Yossa Sura, H. Nusaiba, Dzulfikar Firza Universitas Muhammadiyah Yogyakarta Center of Artificial Intelligence and Robotics Studies Department of Electrical Engineering Faculty of Engineering D.I. Yogyakarta Indonesia Pauh Faculty of Electrical Engineering and Technology 02600 Malaysia Universitas Muhammadiyah Yogyakarta Department of Electrical Engineering Faculty of Engineering D.I. Yogyakarta Indonesia

ISBN: (纸本)9798331511166

Cracking in concrete walls is a common issue in construction, often leading to structural weakening. Such damage can significantly impact both maintenance and repair costs. Concrete wall damage can be categorized in various ways, and detecting these cracks is crucial for timely intervention. This study employed deep learning neural networks to accelerate and simplify the detection and classification of concrete crack images. Two deep learning models, AlexNet and GoogleNet, were trained to classify cracks according to three categories: severe cracks (class 1), fine cracks (class 2), and normal concrete (class 3), using a total of 10,601 concrete images. Among the models, GoogleNet performed than AlexNet. The average performance for GoogleNet is 94.04% for accuracy, 91.96% for precision, 91.97% for recall, 95.39% for specificity, and 91.77% for F-score. However, despite having a lower accuracy than GoogleNet, AlexNet exhibited faster training times, which may be advantageous in certain applications where processing speed is prioritized over precision. These results demonstrate that while GoogleNet provides higher accuracy, AlexNet offers a more time-efficient solution. © 2024 IEEE.

关键词： AlexNet Concrete Crack Deep learning GoogleNet

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Compensating for Material Deformation in Foldable Robots via Deep Learning - A Case Study 39

Compensating for Material Deformation in Foldable Robots via...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Sharifzadeh, Mohammad Jiang, Yuhao Lafmejani, Amir Salimi Aukes, Daniel M. Arizona State Univ Polytech Sch Fulton Sch Engn Mesa AZ 85212 USA Arizona State Univ Sch Engn Matter Transport & Energy Fulton Sch Engn Tempe AZ 85281 USA Arizona State Univ Sch Elect Comp & Energy Engn Fulton Sch Engn Tempe AZ 85281 USA

ISBN: (纸本)9781728196817

Foldable, origami-inspired, and laminate mechanisms are highly susceptible to deformation under external loading, which can lead to position or orientation errors if idealized kinematic models are used. According to dimensional scaling laws, laminate devices can often be treated as rigid bodies at millimeter and smaller scale deformations. However, foldable mechanisms enter the territory of soft robots at larger scales. In this paper, we show the effect of external loads applied to a laminate, 2-DOF parallel robot and the corresponding errors during a pointing task. We then present two control methods, based on deep learning, that compensates for errors caused by the material deformation in foldable robots. For each proposed control method, a Deep Neural Network (DeepNN) is trained to learn the end-effector's deformation model in no-load and loaded conditions. A DeepNN called an updating network is trained and applied in real-time using measured sensor data, in order to transfer updated weights into another DeepNN called the target network. The target network generates control signals with the aim of compensating for the end-effector's error in tracking a desired trajectory. We evaluate our proposed control methods when applied to a laminate robotic end-effector under different external loading conditions in tracking spiral paths. The experimental results show the effectiveness of our proposed control methods in compensating for material deformation in foldable robots.

关键词： Deformable models Deep learning Loading Robot sensing systems Trajectory Laminates Task analysis

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Variable Stiffness of Continuum robotics: A Review 17th

Variable Stiffness of Continuum Robotics: A Review

引用

17th international conference on Intelligent robotics and Applications

作者： Huang, Dongbao Yang, Laihao Sun, Yu Xi An Jiao Tong Univ Natl Key Lab Aerosp Power Syst & Plasma Technol Xian Peoples R China

ISBN: (纸本)9789819607792;9789819607808

Continuum robots have advantages such as strong adaptability, high safety, and excellent flexibility, allowing them to move flexibly in unstructured environments, thereby attracting extensive attention. In practical applications, continuum robots need high compliance to adapt to complex and narrow environments while maintaining a certain degree of stiffness to ensure stable and precise operations. Therefore the ability to achieve the transition between rigidity and flexibility in continuum manipulators is crucial for ensuring the safety of human-robot interaction and functional performance. This paper reviews the mechanisms of variable stiffness in continuum robots, including material-based methods, structural methods, and hybrid methods. The comparison of different variable stiffness methods is based on the application needs, design requirements, and performance metrics of continuum robots, including stiffening capability, response time, space occupation, and implementation safety. A comprehensive analysis of existing methods is provided, and future development directions are proposed.

关键词： Continuum Robots Variable Stiffness Function-Structure Integration

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Utilizing Large Language models for Robot Skill Reward Shaping in Reinforcement Learning 17th

Utilizing Large Language Models for Robot Skill Reward Shapi...

引用

17th international conference on Intelligent robotics and Applications

作者： Guo, Qi Liu, Xing Hui, Jianjiang Liu, Zhengxiong Huang, Panfeng Northwestern Polytech Univ Res Ctr Intelligent Robot Sch Astronaut Xian 710072 Peoples R China Beijing Inst Tracking & Telecommun Technol Beijing 100094 Peoples R China

ISBN: (纸本)9789819607822;9789819607839

In this paper, we examines the integration of LLMs in designing reward functions for reinforcement learning (RL) to enhance robotic applications with minimal human input. In RL, the reward function is pivotal, guiding the agent's learning trajectory by evaluating the desirability of behaviors within specific environments. Traditional reward functions, often sparse, lead to slow convergence as agents require extensive interactions to learn effectively. By leveraging LLM's ability to generate code from task semantics, we propose a new method that reduces the complexity of reward design, allowing even non-experts to create effective reward policies using semantic prompts. We utilize the Soft ActorCritic (SAC) algorithm, known for its efficiency and stability, to train agents under these conditions. To validate the efficacy of our method, we compare it with traditional techniques like Trajectory-ranked reward extrapolation (T-REX). Our findings indicate that the LLM-generated rewards enable quicker convergence and are as effective as those crafted through conventional methods, demonstrating the potential of LLMs to revolutionize reward shaping in RL. Furthermore, we transferred the robot door-opening task from the real-world simulation environment to a real robot, achieving *** approach allows for the rapid deployment of robotic systems, making sophisticated robotics technology more accessible and feasible for a wider range of applications. This study underscores the transformative impact of integrating advanced language models into the realm of robotics and RL, opening up new avenues for future research and application.

关键词： Large language model(LLMs) reward shaping reinforcement learning

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Navigating the Unknown: Uncertainty-Aware Compute-in-Memory Autonomy of Edge robotics

Navigating the Unknown: Uncertainty-Aware Compute-in-Memory ...

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27th Design, automation and Test in Europe conference and Exhibition (DATE)

作者： Darabi, Nastaran Shukla, Priyesh Jayasuriya, Dinithi Kumar, Divake Stutts, Alex C. Trivedi, Amit Ranjan UIC AEON Lab Chicago IL 60607 USA

ISBN: (纸本)9798350348606;9783981926385

This paper addresses the challenging problem of energy-efficient and uncertainty-aware pose estimation in insect-scale drones, which is crucial for tasks such as surveillance in constricted spaces and for enabling non-intrusive spatial intelligence in smart homes. Since tiny drones operate in highly dynamic environments, where factors like lighting and human movement impact their predictive accuracy, it is crucial to deploy uncertainty-aware prediction algorithms that can account for environmental variations and express not only the prediction but also confidence in the prediction. We address both of these challenges with Compute-in-Memory (CIM) which has become a pivotal technology for deep learning acceleration at the edge. While traditional CIM techniques are promising for energy-efficient deep learning, to bring in the robustness of uncertainty-aware predictions at the edge, we introduce a suite of novel techniques: First, we discuss CIM-based acceleration of Bayesian filtering methods uniquely by leveraging the Gaussian-like switching current of CMOS inverters along with co-design of kernel functions to operate with extreme parallelism and with extreme energy efficiency. Secondly, we discuss the CIM-based acceleration of variational inference of deep learning models through probabilistic processing while unfolding iterative computations of the method with a compute reuse strategy to significantly minimize the workload. Overall, our co-design methodologies demonstrate the potential of CIM to improve the processing efficiency of uncertainty-aware algorithms by orders of magnitude, thereby enabling edge robotics to access the robustness of sophisticated prediction frameworks within their extremely stringent area/power resources.

关键词： Compute-in-memory Edge robotics Autonomous Navigation Visual Odometry Particle Filtering

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Automatic Acquisition of a Repertoire of Diverse Grasping Trajectories through Behavior Shaping and Novelty Search 39

Automatic Acquisition of a Repertoire of Diverse Grasping Tr...

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IEEE international conference on robotics and automation (ICRA)

作者： Morel, Aurelien Kunimoto, Yakumo Coninx, Alex Doncieux, Stephane Sorbonne Univ CNRS Inst Syst Intelligents & Robot ISIR F-75005 Paris France Ecole Polytech Fed Lausanne CH-1015 Lausanne Switzerland

ISBN: (纸本)9781728196817

Grasping a particular object may require a dedicated grasping movement that may also be specific to the robot end-effector. No generic and autonomous method does exist to generate these movements without making hypotheses on the robot or on the object. Learning methods could help to autonomously discover relevant grasping movements, but they face an important issue: grasping movements are so rare that a learning method based on exploration has little chance to ever observe an interesting movement, thus creating a bootstrap issue. We introduce an approach to generate diverse grasping movements in order to solve this problem. The movements are generated in simulation, for particular object positions. We test it on several simulated robots: Baxter, Pepper and a Kuka Iiwa arm. Although we show that generated movements actually work on a real Baxter robot, the aim is to use this method to create a large dataset to bootstrap deep learning methods.

关键词： Learning systems Training Simulation Grasping Diversity methods Predictive models Behavioral sciences

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