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检索条件"任意字段=2024 International Conference on Intelligent Robotics and Automatic Control"

共 5267 条记录，以下是431-440 订阅

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automatic cruise and intelligent obstacle avoidance method of geological radar auxiliary equipment based on YOLOX-nano lightweight target detection 4

Automatic cruise and intelligent obstacle avoidance method o...

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4th international conference on intelligent Power and Systems, ICIPS 2024

作者： Shu, Kai Luo, Yuhe Bai, Wenbo Wang, Yixin Ningbo Electric Power Design Institute Co. Ningbo China China Three Gorges University of Electrical Engineering & New Energy Yichang China

ISBN: (纸本)9798331532765

automatic cruise and intelligent obstacle avoidance method based on YOLOX-nano lightweight object detection, include, Single-stage (One-Stage) target detection algorithm based on regression;YOLOX-nano road detection method based on the least squares method;YOLOX-nano-obstacle recognition distance estimation method based on single visual ranging method;Autonomous cruise control strategy based on a PID control algorithm The proposed method of the present invention provides, by means of indirect perception, Integrate the lane boundary line inferred by target detection and the information of obstacle category and location size, and the obstacle distance information calculated by single visual ranging, Develop automatic cruise and intelligent obstacle avoidance strategies for geological radar auxiliary equipment, The method utilizes the powerful feature learning capabilities of deep learning methods, While having excellent generalization and robustness, Also have the interpretability of traditional methods, Easy the analysis, debugging and optimization of geological radar auxiliary equipment, Improve the system performance and stability. © 2024 IEEE.

关键词： Auxiliary equipment

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Waves filtering in heading controllers: Impact on the power production of an energy ship 15

Waves filtering in heading controllers: Impact on the power ...

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15th IFAC conference on control Applications in Marine Systems, robotics and Vehicles, CAMS 2024

作者： Juin-Gauthier, Giovanni Babarit, Aurélien Elie, Baptiste Kermorgant, Olivier Frémont, Vincent Nantes Université École Centrale de Nantes LS2N UMR 6004 Nantes44000 France Nantes Université École Centrale de Nantes LHEEA UMR 6598 Nantes44000 France Farwind Energy France Nantes Universite Ecole Centrale de Nantes LS2N UMR 6004 Nantes44000 France

This article investigates the impact on power production of an energy ship of two types of heading controllers. The first controller includes a filter which eliminates the wave-frequency dependent motion of the ship, while the other aims at compensating the effects of waves on the motion of the ship. The study is based on a numerical model of the energy ship which is presented in the paper. Results show that waves compensation reduces the generated power variation but increases rudder oscillations. In the opposite, the waves filtering controller allows for a slightly greater power production at the expense of greater power variations. © 2024 Elsevier B.V.. All rights reserved.

关键词： and control of marine systems control applications in marine renewable energy Guidance identification Modeling navigation and control (GNC) of marine vessels simulation

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Optimization of Neural Networks using Swarm Intelligence Techniques for Achieving Energy Efficiency in Smart Building Architecture 1

Optimization of Neural Networks using Swarm Intelligence Tec...

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1st IEEE international conference on Cognitive robotics and intelligent Systems, ICC - ROBINS 2024

作者： Raja, M. Karthick Shermatova, Makhbuba Al Ansari, Mohammed Saleh Abdufattokhov, Shokhjakhon Rao, Vuda Sreenivasa Raj, I. Infant Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India Turin Polytechnic University in Tashkent Civil Engineering and Architecture Department Tashkent Uzbekistan University of Bahrain College of Engineering Department of Chemical Engineering Bahrain Turin Polytechnic University Automatic Control and Computer Engineering Dept Italy Tashkent Intl. University of Education Dept of Information Technologies Tashkent Uzbekistan Koneru Lakshmaiah Education Fou. Green Fields Department of Computer Science and Engineering A.P Vaddeswaram522302 India K. Ramakrishnan College of Engineering Department of Computer Science and Engineering Tamil Nadu Trichy621 112 India

ISBN: (纸本)9798350372748

The increasing prevalence of smart building architectures, driven by the integration of Internet of Things (IoT) devices and automation systems, has led to a surge in energy consumption. This research explores the application of swarm intelligence techniques as an innovative approach to optimize neural networks, aiming to strike a balance between maintaining the desired performance levels and minimizing energy consumption. The study investigates the integration of swarm-based optimization algorithms, such as Particle Swarm Optimization (PSO) into the training and operation of neural networks. These algorithms enable the networks to dynamically adapt and optimize their parameters in response to changing environmental conditions and user requirements. The research focuses on developing a comprehensive framework that considers the specific challenges posed by smart building architectures, including real-time data processing, sensor integration, and adaptive control. The proposed approach aims to achieve optimal neural network configurations that minimize energy consumption while ensuring reliable and responsive operation of smart building systems. The results demonstrate the potential of swarm intelligence to significantly improve the energy efficiency of neural network-enabled smart building architectures, providing a promising avenue for sustainable and intelligent infrastructure. The proposed model has an accuracy of 98.23% which is 7.64% higher than that of the traditional approaches. © 2024 IEEE.

关键词： Energy efficiency

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Prescriptive Manufacturing in Society 5.0: Human Autonomous Organizations and "On-Demand" Smart Services 22

Prescriptive Manufacturing in Society 5.0: Human Autonomous ...

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22nd IFAC conference on Technology, Culture and international Stability (TECIS)

作者： Wang, Xiao Huang, Jun Yang, Jing Wang, Xiaoxing Wang, Fei-Yue Anhui Univ Engn Res Ctr Autonomous Unmanned Syst Technol Minist Educ Hefei 230601 Peoples R China Macau Univ Sci & Technol Macau 999078 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Beijing SANBODY Technol Co Ltd Beijing 214000 Peoples R China Qingdao Acad Intelligent Ind Qingdao 266109 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Society 5.0 addresses human behaviors and their consequences in social management, while emphasizing the integration of Cyber-Physical-Social Spaces to achieve a highly connected and circulated society. Prompted by the various social needs expressed on social media platforms, Prescriptive Manufacturing (PM) emerges as a proactive pre-manufacturing paradigm. This paper delves into PM within the context of Society 5.0. It highlights the development of Human Autonomous Organizations (HAOs) and the delivery of "on-demand" smart services, which are customized to meet both individual and broader societal demands. The social sensing framework of social needs across different social media platforms that enables the identification and anticipation of consumer demands in real-time is illustrated. This data-driven insight leverages artificial intelligence and robotics to facilitate customized design and on-demand production, aligning product creation with real user requirements. Furthermore, we discuss the implementation of human-centered smart services in PM, such as virtual try-on, virtual fitting, personalized retrofit, user-friendly scheduling, and proximity logistics, which not only enhance consumer experience but also optimize resource efficiency. By examining the symbiosis between technological advancements and social need fulfillment, this study sheds light on how PM and HAOs can foster a more responsive, sustainable, and inclusive economy in society, ultimately contributing to a higher quality of life and sustainable development goals of Society 5.0. Copyright (C) 2024 The Authors.

关键词： Social Needs Sensing Human Autonomous Organization Customized Design personalized fine-tuning prescriptive manufacturing Human-Centric Smart Services

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Embodied Intelligence Applications in Assistive Technologies for the Visually Impaired 6

Embodied Intelligence Applications in Assistive Technologies...

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6th international conference on robotics, intelligent control and Artificial Intelligence, RICAI 2024

作者： Zhang, Qianheng Zhou, Yiling College of Design and Innovation Tongji University Shanghai China School of International Chinese Studies East China Normal University Shanghai China

ISBN: (纸本)9798331541699

This paper investigates the application of Embodied Intelligence (EI) in assistive technologies for the visually impaired (BLV) population. The study examines the challenges faced by BLV individuals, including mobility, information access, and social interaction, while identifying the limitations of existing assistive devices. A theoretical framework is proposed, integrating EI's core components—perception, decision-making, and action execution—to deliver adaptive and personalized solutions. To validate the framework, an experimental study was conducted using an EI prototype. Participants, including those with complete and partial blindness, were observed over 60 days, with data collected both before and after using the prototype. Results, analyzed through the Visual Functioning Subscale (VFS), demonstrated significant improvements in navigation, safety, and social engagement. This research highlights the transformative potential of EI in enhancing the independence and quality of life of BLV individuals while addressing critical ethical and societal considerations for its implementation. © 2024 IEEE.

关键词： Ambient intelligence

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Automated Adaptive Navigation Leveraging Natural Language Models in Aerospace robotics System 6

Automated Adaptive Navigation Leveraging Natural Language Mo...

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6th international conference on robotics, intelligent control and Artificial Intelligence, RICAI 2024

作者： Li, Zhenglin Huang, Yangchen Zhu, Mengran Zhang, Jingyu Chang, JingHao Liu, Houze Texas A&M University TX77840 United States Columbia University NY10027 United States Miami University OH45056 United States University of Chicago IL60637 United States Kyoto College of Graduate Studies for Informatics Kyoto606-8225 Japan New York University NY11201 United States

ISBN: (纸本)9798331541699

Vision-based obstacle detection in the space environment is a fundamental task for aerospace devices to differentiate between semantically altered and unaltered regions. Large language models (LLMs) have been utilized in various domains for their exceptional feature extraction capabilities and have shown promise in numerous downstream applications. In this study, we harness the power of a pre-trained LLM, extracting feature maps from extensive datasets and employing an auxiliary network to detect changes for aerospace device. Unlike existing LLM-based change detection methods that solely focus on deriving high-quality feature maps, our approach emphasizes the manipulation of these feature maps to enhance semantic relevance. © 2024 IEEE.

关键词： Semantics

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MindArm: Mechanized intelligent Non-Invasive Neuro-Driven Prosthetic Arm System 18

MindArm: Mechanized Intelligent Non-Invasive Neuro-Driven Pr...

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18th international conference on control Automation robotics and Vision

作者： Nawaz, Maha Basit, Abdul Shafique, Muhammad New York Univ NYU Abu Dhabi Div Engn eBRAIN Lab Abu Dhabi U Arab Emirates

ISBN: (纸本)9798331518509;9798331518493

Currently, individuals with arm mobility impairments (referred to as "patients") face limited technological solutions due to two key challenges: (1) non-invasive prosthetic devices are often prohibitively expensive and costly to maintain, and (2) invasive solutions require high-risk, costly brain surgery, which can pose a health risk. Therefore, current technological solutions are not accessible for all patients with different financial backgrounds. Toward this, we propose a low-cost technological solution called MindArm, an affordable, non-invasive neuro-driven prosthetic arm system. MindArm employs a deep neural network (DNN) to translate brain signals, captured by low-cost surface electroencephalogram (EEG) electrodes, into prosthetic arm movements. Utilizing an Open Brain Computer Interface and UDP networking for signal processing, the system seamlessly controls arm motion. In the compute module, we run a trained DNN model to interpret filtered micro-voltage brain signals, and then translate them into a prosthetic arm action via serial communication seamlessly. Experimental results from a fully functional prototype show high accuracy across three actions, with 91% for idle/stationary, 85% for handshake, and 84% for cup pickup. The system costs approximately $500-550, including $400 for the EEG headset and $100-150 for motors, 3D printing, and assembly, offering an affordable alternative for mind-controlled prosthetic devices.

关键词： EEG OpenBCI non-invasive prosthetic arm deep learning low cost

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Analysis of control Dynamics of a Robotic Arm Using Linear Quadratic Optimal control Technique

Analysis of Control Dynamics of a Robotic Arm Using Linear Q...

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2024 IEEE international conference on Modeling, Simulation and intelligent Computing, MoSICom 2024

作者： Pandey, Manish Kumar Mishra, Pallavi Mahia, Ram Niwash National Institute of Technology Hamirpur Department of Electrical Engineering Hamirpur India

ISBN: (纸本)9798331533311

Arm robotics is becoming more and more popular due to the need to help humans with difficult and repetitive tasks and the desire to increase productivity. Optimal control techniques are required for better control of robot arms. This paper presents the control dynamics of a two-degree-of-freedom planar robot arm. The non-linear dynamic model of the robot manipulator is linearized using feedback linearization approach followed by Linear Quadratic Optimal control (LQ Optimal control) for controlling the robot arm. The control parameters of LQ Optimal control are derived by minimizing a quadratic cost function. Furthermore, a simulation study is presented together with its results to demonstrate the effectiveness of the suggested approach. © 2024 IEEE.

关键词： Robotic arms

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Dynamics-Based Trajectory Planning for Vibration Suppression of a Flexible Long-Reach Robotic Manipulator System

Dynamics-Based Trajectory Planning for Vibration Suppression...

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2024 international conference on intelligent Robots and Systems

作者： Chen, Anthony Siming Pulgarin, Erwin Jose Lopez Herrmann, Guido Lanzon, Alexander Carrasco, Joaquin Lennox, Barry Camera-Knowles, Benji Brotherhood, John Sakaue, Tomoki Zhang, Kaiqiang Univ Manchester Dept Elect & Elect Engn Control Syst & Robot CSR Grp Manchester M13 9PL Lancs England Jacobs 601 Faraday St Warrington WA3 6GN Cheshire England Tokyo Elect Power Co TEPCO Holdings Fukushima Japan United Kingdom Atom Energy Author UKAEA Culham Campus Abingdon OX14 3DB Oxon England

ISBN: (纸本)9798350377712;9798350377705

We address the unique challenge of vibration suppression for a flexible long-reach robotic manipulator system, namely, the through-wall deployment (TWD) system that is used in nuclear environments. This paper proposes a novel dynamics-based trajectory optimization approach, which minimizes both the acceleration and the jerk at the manipulator's joints, as well as the vibrations of the flexible long-reach boom where the manipulator's base is mounted. Firstly, we create an integrated model for the system dynamics based on the knowledge of the robotic manipulator and the acceleration data from the vibration tests. We then develop an original procedure for generating the high-order polynomial trajectory that guarantees the zero-boundary condition for a flexible number of optimization parameters and waypoints. Following the simulation of a multi-objective optimization scheme, the optimized trajectory is experimentally validated on the practical TWD system with around 28% vibration reduction on average compared to the benchmark. Importantly, this reduction is achieved without compromising on the average speed of motion. The methodology is transferable to a wider range of flexible robotic manipulator systems with similar characteristics.

关键词： Flexible robotics trajectory planning optimization vibration suppression nuclear robotics

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Expansion-GRR: Efficient Generation of Smooth Global Redundancy Resolution Roadmaps

Expansion-GRR: Efficient Generation of Smooth Global Redunda...

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2024 international conference on intelligent Robots and Systems

作者： Zhong, Zhuoyun Li, Zhi Chamzas, Constantinos Worcester Polytech Inst WPI Dept Robot Engn Worcester MA 01609 USA

ISBN: (纸本)9798350377712;9798350377705

Global redundancy resolution (GRR) roadmap is a novel concept in robotics that facilitates the mapping from task space paths to configuration space paths in a legible, predictable, and repeatable way. Such roadmaps could find widespread utility in applications such as safe teleoperation, consistent path planning, and motion primitives generation. However, previous methods to compute GRR roadmaps often necessitate a lengthy computation time and produce non-smooth paths, limiting their practical efficacy. To address this challenge, we introduce a novel method EXPANSION- GRR that leverages efficient configuration space projections and enables a rapid generation of smooth roadmaps that satisfy the task constraints. Additionally, we propose a simple multi-seed strategy that further enhances the final quality. We conducted experiments in simulation with a 5-link planar manipulator and a Kinova arm. We were able to generate the GRR roadmaps up to 2 orders of magnitude faster while achieving higher smoothness. We also demonstrate the utility of the GRR roadmaps in teleoperation tasks where our method outperformed prior methods and reactive IK solvers in terms of success rate and solution quality.

关键词： Remote control

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