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检索条件"任意字段=International Symposium on Computational Intelligence in Robotics and Automation"

共 3095 条记录，以下是21-30 订阅

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IEEE Joint 22nd international symposium on computational intelligence and Informatics and 8th international Conference on Recent Achievements in Mechatronics, automation, Computer Science and robotics, CINTI-MACRo 2022 - Proceedings

IEEE Joint 22nd International Symposium on Computational Int...

引用

Joint 22nd IEEE international symposium on computational intelligence and Informatics and 8th IEEE international Conference on Recent Achievements in Mechatronics, automation, Computer Science and robotics, CINTI-MACRo 2022

ISBN: (纸本)9798350398823

The proceedings contain 64 papers. The topics discussed include: noise-based logic versus quantum supremacy;modeling cerebro-vascular autoregulation after postural change perturbations;a review on emotion based harmful speech detection using machine learning;comparison of automatic question generation techniques;transparent slide detection and gripper design for slide transport by robotic arm;proposing a new model for estimation of oil rate passing through wellhead chokes in an Iranian heavy oil field;real-time multi-user 3D visualization software in medicine;evaluation of the use of an intelligent system in the calibration of a refined car-following model;inertial sensor-based movement classification with dimension reduction based on feature aggregation;and on the simulation of lower order control strategies for higher order systems.

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Literature Review on Advanced Cloud robotics for Enhanced Industrial automation 3

Literature Review on Advanced Cloud Robotics for Enhanced In...

引用

3rd international Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Manonmani, A. Akash, S. Aswinraj, A. Manikandan, A. Saveetha Engineering College Department of Electronics and Instrumentation Engineering Chennai India

ISBN: (纸本)9798331509675

Cloud robotics is transforming industrial automation by combining cloud computing with robotic systems. This paper reviews recent advancements in cloud robotics and their industrial applications, highlighting the synergy between cloud infrastructure and robotic platforms. Key technologies like artificial intelligence, machine learning, and the Internet of Things are explored for their roles in enhancing scalability, computational efficiency, and real-time data processing. The paper also addresses challenges such as latency and data security, proposing solutions to overcome them. The findings suggest that cloud robotics can significantly improve productivity, cost-efficiency, and flexibility in manufacturing and other sectors, with future research directions aimed at promoting its broader adoption. © 2025 IEEE.

关键词： Cloud platforms

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HVUE Planner: 3D UAV Path Planner Using Hierarchical Visibility Graph in Unknown Environments 17th

HVUE Planner: 3D UAV Path Planner Using Hierarchical Visibil...

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17th international Conference on Intelligent robotics and Applications

作者： Pang, Jiaxue Chen, Liming Jia, Yijie Chen, Weihuang Zhang, Xuchong Sun, Hongbin Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian 710049 Peoples R China 208 Res Inst China Ordnance Ind Beijing 102202 Peoples R China

ISBN: (纸本)9789819607853;9789819607860

In recent years, research on UAV path planning in known environments has matured. However, the demand for planning techniques in unknown environments is increasing in practical engineering applications, where numerous planning needs arise in unknown environments. In such scenarios, the related technologies remain challenging. This paper introduces an efficient 3D UAV Path Planner for Unknown Environments that utilizes a single-layer visibility graph to efficiently handle not only unknown but also partially known environments. It supplements local obstacle handling by using 2D projection maps, aiming to address the high map update storage and computational complexity issues in traditional methods. Extensive experimental results have been tested in a moderately sized virtual campus simulation environment and show that our method is highly efficient with less computational load and map storage compared to state-of-the-art planning methods.

关键词： Path Planning Aerial Systems Unknow Enviroment-based Planning Visibility Graph

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Artificial intelligence in robotics: Revolutionizing Retail Store automation and Beyond 22

Artificial Intelligence in Robotics: Revolutionizing Retail ...

引用

22nd international Learning and Technology Conference, L and T 2025

作者： Ali, Khalufi Nasser Jazan University College of Business Department of Management and Marketing Jazan Saudi Arabia

ISBN: (纸本)9798331532215

AI in robotics involves the application of efficient algorithm and computational techniques that allows robots to operate independently, learn from the environment and make efficient decisions. However, in current studies, mostly contact methods that cannot be learned and do not respond to change are used despite the progress made in this regard. These approaches often fail to respond in real-time to а certain issue, which can result in inefficiency and low overall operational performance. To overcome these shortcomings, this paper presents a new technique in which DRL (Deep Reinforcement Learning) is used for motion control of a robotic arm and limb in retail automation. DRL allows the robotic system to learn the best motion profiles from playing a game with its surroundings with reduced chances of a less optimum setting due to changing environmental conditions. The most important peculiarities of the proposed approach include integration of enhanced neural structures and realization of automatic learning and decision-making. The effectiveness of the DRL approach is shown to be considerably higher than that of traditional ones, with accuracy reaching 99.3%. Such outcomes speak to the prospects to transform robotics through DRL knowledge and skills to enhance real-time adaptation and increase effectiveness. Lastly, this research enhances the existing literature in AI & robotics and accordingly establishes the foundation for further developments of intelligent automation systems in various fields. © 2025 IEEE.

关键词： Deep reinforcement learning

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Identification of Wiener-Hammerstein Model Using Stochastic Variational Bayesian Learning 17th

Identification of Wiener-Hammerstein Model Using Stochastic ...

引用

17th international Conference on Intelligent robotics and Applications

作者： Li, Junhao Zhang, Fukai Wang, Cong Liu, Yuehu Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian Peoples R China Shandong Univ Sch Control Sci & Engn Jinan Peoples R China

ISBN: (纸本)9789819607822;9789819607839

The complexity of probability estimation has limited the application of Bayesian learning in nonlinear system identification. This paper addresses Wiener-Hammerstein (WH) nonlinear process identification in the presence of process noise and measurement noise, we propose a Stochastic Variational Inference (SVI) method inspired by stochastic optimization. The SVI method leverages probabilities of intermediate variables to estimate natural gradients of model parameters and updates the posterior probabilities of hidden variables. Compared to the traditional Variational Inference (VI) method, our proposed approach significantly reduces computational complexity. The effectiveness of the SVI method is verified by two numerical simulations and the WH benchmark problem, thereby providing a fresh perspective for efficiently identifying nonlinear systems with large-scale uncertain data.

关键词： Bayesian learning Stchastic optimization nonlinear system identification Wiener-Hammerstein model

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Fabrication of Sensorsless Automated Guided Vehicle for Small Warehouse Rack Management

Fabrication of Sensorsless Automated Guided Vehicle for Smal...

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2025 IEEE international Conference on computational, Communication and Information Technology, ICCCIT 2025

作者： Sahu, Neelesh Kumar Chaturvedi, Piyush Gupta, Vaishali Patel, Ruchi Verma, Ashok Shrivastava, Megha Gyan Ganga Institute of Technology and Sciences Artificial Intelligence and Robotics MP Jabalpur India IPS Academy Institute of Engineering & Science Department of Computer Sc. & Engg. MP Indore India Gyan Ganga Institute of Technology and Sciences MP Jabalpur India Medicaps University Department of Mathematics MP Indore India

ISBN: (纸本)9798331512965

Automated guided vehicles (AGVs) are an essential part of today's logistics networks because they save time, reduce wear and maintenance expenses, and maximize efficiency in route *** creation of AGVthat is capable of effectively managing warehouse racks without the utilization of sensors is the primary emphasis of this project. In order to travel and carry out duties, the AGVruns on predetermined pathways and makes use of a button-based interface. For the purpose of offering an efficient and costeffective solution for the automation of small warehouses, it is outfitted with a lever mechanism that is operated by a servo motor and features the ability to connect and move racks. © 2025 IEEE.

关键词： Automatic guided vehicles

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Dynamic bimanual human-to-robot object handovers using motion prediction deep neural networks

Dynamic bimanual human-to-robot object handovers using motio...

引用

2025 IEEE/SICE international symposium on System Integration, SII 2025

作者： Mavsar, Matija Uchibe, Eiji Morimoto, Jun Ude, Ales Jožef Stefan Institute Dept. of Automatics Biocybernetics and Robotics Ljubljana Slovenia ATR Computational Neuroscience Laboratories Dept. of Brain-Robot Interface Kyoto Japan Kyoto University Graduate School of Informatics Kyoto Japan

ISBN: (纸本)9798331531614

Facilitating dynamic bimanual handovers between humans and robots is a complex endeavor that requires integrating human pose estimation, motion prediction, and generating appropriate trajectories for receiving robots. This study introduces a method designed to predict required bimanual receiver robot motion during handover tasks, leveraging pose estimation and motion prediction networks. Additionally, we propose a real-time control approach for a dual-arm humanoid robot to dynamically adjust its receiving trajectories. We evaluate the ability of neural networks to accurately predict receiver trajectories and thus improve the handover process. We compare long short-term memory (LSTM) and transformer architectures for motion prediction and also assess prediction accuracy of both absolute and relative receiving trajectories as well trajectories for each separate robot arm, and show that the use of absolute and relative coordinates is beneficial for generating more accurate receiver motions. © 2025 IEEE.

关键词： Trajectories

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Literature Review on Advanced Cloud robotics for Enhanced Industrial automation

Literature Review on Advanced Cloud Robotics for Enhanced In...

引用

Electronics and Renewable Systems (ICEARS), international Conference on

作者： A. Manonmani S. Akash A. Aswinraj A. Manikandan Department of Electronics and Instrumentation Engineering Saveetha Engineering College Chennai India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682