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检索条件"任意字段=18th International Conference on Control Automation Robotics and Vision"

共 5311 条记录，以下是221-230 订阅

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Aspect-Ratio and Scale Aware Rotated Defect Detection for Fiber-reinforced Composites 5

Aspect-Ratio and Scale Aware Rotated Defect Detection for Fi...

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5th international conference on Computer vision, Image and Deep Learning, CVIDL 2024

作者： Xu, Yuanye Liu, Yuanhao University of Chinese Academy of Sciences Key Laboratory of Networked Control System Shenyang Institute of Automation Chinese Academy of Sciences Shenyang Institute of Automation Chinese Acad. of Sci. Institutes for Robotics and Intelligent Mfg. Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9798350373820

Detecting surface defects in carbon fiber-reinforced composites (FRCs) during prepreg stacking is crucial for ensuring product quality. Traditional methods using horizontal bounding boxes (HBBs) for defect detection often lead to overlapping boxes and inaccurate results. this paper proposes an Aspect-Ratio and Scale Aware Detector network (ARSADet) specifically designed for rotated defect detection in FRCs. the network incorporates a Gabor convolutional network for feature extraction, an aspect-ratio and scale-aware label assignment method, and a newly established FRCs Oriented Defect Detection dataset (FODD). Evaluation on the FODD dataset demonstrates superior performance compared to existing methods. © 2024 IEEE.

关键词： Aspect ratio

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Design of an Automatic Locking Nut Tightening control System for a vision Guided Robot 8

Design of an Automatic Locking Nut Tightening Control System...

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8th international conference on automation, control and robotics Engineering, CACRE 2023

作者： Chang, Kai Lu, Bolin Yin, Jian Tan, Guangchao Chinese Academy of Sciences Key Laboratory on Intelligent Detection Equipment Technology of Liaoning Province Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Shenyang China

ISBN: (纸本)9798350302776

A set of automatic locking nut tightening control system for a vision guided robot is designed and implemented. through hand-eye calibration, the coordinate system is unified. By the designed tool plate and tapered tooling, the calibrated reference location for the tightening tool is realized. According to the position and orientation of the reference screw, the vision system detects the offset of the current screw position, and guides the robot to adjust the pose of the tightening tool. By setting up the network topology of the control system, the data interaction of each subsystem is completed, and the automatic workflow is realized. the application of the system can avoid the relative movement between the tool and screw during the tightening process, and improve the tightening quality. © 2023 IEEE.

关键词： Calibration

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Intelligent control Systems for Industrial automation and robotics 10

Intelligent Control Systems for Industrial Automation and Ro...

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10th IEEE Uttar Pradesh Section international conference on Electrical, Electronics and Computer Engineering, UPCON 2023

作者： Biradar, Vijaykumar S. Al-Jiboory, Ali Khudhair Sahu, Gaurav Tilak Babu, S.B.G. Mahender, Kommabatla Natrayan, L. N B NavaleSinhgad College of Engineering Department of Electrical Engineering Maharashtra Solapur413255 India University of Diyala Department of Mechanical Engineering Baqubah Iraq B K Birla Institute of Engineering & Technology Department of Electronics& Communication Engineering Rajasthan Pilani India Aditya Engineering College Department of ECE Surampalem India Sumathi Reddy Institute of Technology for Women Department of ECE Telangana India Saveetha School of Engineering Simats Department of Mechanical Engineering Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350382471

Intelligent control systems are a game-changer for robotics and industrial automation. this abstract explores how computer vision (CV) and artificial neural network (ANN) algorithms may be used to improve automation and robotics in terms of efficiency, accuracy, and flexibility. the goal of industrial automation is to streamline processes, decrease the need for human interaction, and increase output, all of which have progressed dramatically over the years. Integrating intelligent control systems that make use of computer vision and artificial neural networks is crucial to this transformation. the ability of these systems to detect and understand their environments is made possible by computer vision (CV). In order to make sound judgements, CV algorithms analyse data captured by cameras and other sensors. CV lets robots recognise things, navigate hazardous terrain, and carry out precise industrial tasks. CV has become an integral part of industrial automation, used for anything from monitoring production quality to navigating warehouses autonomously. Artificial neural networks (ANN s) mimic the human brain in many ways, including their ability to learn and make decisions on their own. ANNs are built from networks of nodes (neurons) that work together to analyse and process information. ANN s may learn to identify patterns, refine their control settings, and adjust to new circumstances. Predictive maintenance, problem identification, and control strategy optimisation are just some of the ways in which ANN s are put to use in industrial automation and robotics. Combining CV with ANN algorithms makes for a formidable tool with many practical uses in industry. the automated examination of produced goods is one significant use. Cracks and imperfections are easy targets for CVs, while ANNs can analyse the data in real time to make judgements about the product's quality. As a result, we can maintain constant quality control, cut down on waste, and boost output. Combining C

关键词： Artificial Neural Networks (ANN) Computer vision (CV) etc Industrial automation Intelligent control Systems robotics

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Traffic Flow Estimation using Graph Neural Network with Aggregation of Traffic Features 17

Traffic Flow Estimation using Graph Neural Network with Aggr...

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17th international conference on control, automation, robotics and vision (ICARCV)

作者： Putri, Adiyana Brahmana, Ferio Joelianto, Endra Sutarto, Herman Y. Inst Teknol Bandung Fac Ind Technol Engn Phys Doctoral Program Bandung Indonesia Inst Teknol Bandung Fac Ind Technol Artificial Intelligence Control & Automat Lab Bandung Indonesia Inst Teknol Bandung Fac Ind Technol Instrumentat & Control Res Grp Bandung Indonesia PT Pusat Riset Energi PRE Dept Intelligent Syst Bandung Indonesia

ISBN: (纸本)9781665476874

the increasing vehicle volume every year affects the prediction of the traffic system. the purpose of predicting traffic flow is to estimate the lost data caused by sensor malfunctions due to connection disruptions or ages. In estimating traffic flow, obtaining and explaining the historical data from the nearest segment sensor is necessary. the graph model can describe the spatial relationship between segments by connecting nodes with edges. Graph Neural Network (GNN) has been used as a learning method to predict the traffic flow in the segment sensor. GNN gets input in the form of spatial data built through a graph model, and the features are traffic density, average vehicle speed, and delay time on the road network. this paper shows that GNN's capability performs better in extracting spatial features and predicting the traffic flow by aggregating the features through connectivity nodes and edges.

关键词： Graph Neural Networks Traffic Features Traffic Flow Prediction

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Autonomous Landing of an Unmanned Aerial Vehicle on a Mobile Platform 7

Autonomous Landing of an Unmanned Aerial Vehicle on a Mobile...

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7th international conference on automation, control and Robots, ICACR 2023

作者： Dalang, Felix Sihitshuwam Kulathunga, Geesara Khusainov, Ramil Innopolis University Robotics and Computer Vision Innopolis Russia Innopolis University Center of Autonomous Technologies Innopolis Russia Robotics Innopolis University Innopolis Russia

ISBN: (纸本)9798350302882

Landing an Unmanned Aerial Vehicle (UAV) on a mobile vehicle, has been an active research area due to its much needed applicability in numerous directions such as landing an aerial vehicle on the international space station or a ship at sea. Such research is conducted mainly in two directions: control-based and vision-based. vision-based approaches require sensing from cameras that detect features, such as visual markers, on the mobile vehicle to estimate and track the mobile vehicle pose till the landing pose. the visual markers are usually utilized for feature detection, which most mobile vehicles might not have. this paper proposes a new approach for achieving autonomous landing of a UAV by generating constrained uniform B-spline trajectories assuming known pose of both the UAV and the UGV. then applying PID controller to regulate velocity set point to land the UAV on the mobile vehicle. the overall effectiveness of the proposed approach is demonstrated using ROS/Gazebo simulations, with a UAV (Px4 enabled IRIS) and a mobile vehicle (Jackal from ClearPath robotics). Landing tasks for static and dynamic cases are carried out and the findings and limitations are stated thereafter. © 2023 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Efficient algorithms for distributed virtual force-based self-spreading of robotic sensor/actuator networks 7

Efficient algorithms for distributed virtual force-based sel...

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

作者： Konovalov, P.A. Matveev, A.S. "sirius University" Mathematical Robotics Science Division Krasnodar Region Sirius City Russia Saint Petersburg University Department of Mathematics and Mechanics St. Petersburg Russia

ISBN: (纸本)9798350355642

Node deployment is a key issue in building sensor/actuator networks. To improve coverage and reduce costs, the use of autonomous mobile robotic nodes is an attractive option. this paper presents two new virtual force-based (VFB) distributed self-deployment algorithms for robotic networks. their performance is compared with the baseline behaviors of five existing cutting-edge VFB methods. this analysis is conducted using a whole lot of standard criteria: elapsed time, coverage percentage, energy consumption, mean distance, and distance uniformity. Also, we propose and use a new criterion that assesses the balance of network's resources allocated to serve various equally valid subareas. Monte-Carlo computer experiments show that the proposed algorithms demonstrate the best performance regarding the coverage uniformity and percentage. © 2024 IEEE.

关键词： Multi agent systems

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Self-Recalibrating Micromanipulator System for Resilient Robotic vision-Based control 18

Self-Recalibrating Micromanipulator System for Resilient Rob...

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IEEE 18th international conference on automation Science and Engineering (IEEE CASE)

作者： Wang, Tiexin Li, Haoyu Pu, Tanhong Ding, Jingjing Du, Shoukang Chau, Zhong Hoo Tan, U-Xuan Chew, Ting Gang Yang, Liangjing Zhejiang Univ ZJU UIUC Inst Int Campus Haining Zhejiang Peoples R China Zhejiang Univ Sch Med Zhejiang Univ Univ Edinburgh Inst Hangzhou 310058 Peoples R China Singapore Univ Technol & Design Engn Prod Dev Singapore Singapore

ISBN: (数字)9781665490429

ISBN: (纸本)9781665490429

A self-recalibrating micromanipulator system is developed in this work to perform vision-based control under unpredictable scenes and poor imaging conditions. To ensure resilient vision-based control in such challenging conditions, we proposed a self-recalibrating mechanism that combines the estimates from both the robot manipulator and microscope vision. Our method is demonstrated for micropipette cell aspiration. In the initialization stage, the system automatically locates the tip and selects the templates for visual tracking. At the self-calibration phase, accurate tracking of the tip is achieved by the background subtraction template matching (BSTM) method in order to estimate the calibration matrix in complex environments. During the operation, the control error is detected according to the visual information and the self-recalibration is executed when the error exceeds the threshold value. Experimental analysis shows that the BSTM method for self-calibration achieves accurate tracking with an average error of less than 1 pixel (0.18 mu m). And the system has an average control error of 4.18 pixels ( 0.75 mu m) under complex calibration backgrounds, satisfying the accuracy requirements for cell aspiration. Moreover, the system achieved stable tracking in complex operating environments, including cell interaction, occlusion, tip leaving the focal plane or beyond the field-of-view. By designing a self-recalibrating micromanipulator equipped with resilient vision-based control system, we hope to robotize the procedures for the study of cell mechanics during micropipette aspiration and pave the path for the advancement of robotic micromanipulation in biomechanical research.

关键词： Biomechanics Visualization Computer aided software engineering automation Micromanipulators Microscopy control systems

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An Autonomous Irrigation Robot Based on ROS2, YOLOv8 and Distributed ESP32 WMN Architecture 8

An Autonomous Irrigation Robot Based on ROS2, YOLOv8 and Dis...

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8th international conference on Smart Internet of things

作者： Fang, Yisu Hafizh, Hadyan Ateeq, Muhammad Xian Jiaotong Liverpool Univ Sch Internet Things Suzhou Peoples R China Sunway Univ Sch Engn & Technol Bandar Sunway Malaysia

ISBN: (纸本)9798350366457;9798350366440

this paper presents the design and implementation of an autonomous indoor houseplant irrigation robot operating under the ROS2 framework. the robot prototype integrates locomotion, water delivery, power management, sensors, and high-level control systems. A YOLOv8-based vision system, trained on a custom houseplant dataset, detects and localizes plants for precise irrigation. Robot localization and mapping use ROS2 SLAM Toolbox with 2D LIDAR, for autonomous navigation and irrigation via ROS2's Nav2 path planning. Wireless communication is achieved through an ad-hoc mesh network connecting the main computer with distributed microcontrollers. the prototype demonstrates key autonomous functions, as well as acknowledging limitations, presenting opportunities for future enhancements to improve robustness and practical applicability in home automation.

关键词： Wireless Sensor Network Computer vision Embedded Systems Machine Learning robotics Internet of things

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Bare Printed Circuit Board Defects Localization and Detection Using YOLOv5 Models 6

Bare Printed Circuit Board Defects Localization and Detectio...

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6th international conference on robotics and automation in Industry, ICRAI 2024

作者： Khan, Wajih Ahmed Hamza, Amir Akram, Muhammad Usman Khan, Umar Shahbaz Nawaz, Tahir National University of Sciences and Technology Dept. of Mechatronics Engineering Islamabad Pakistan National University of Sciences and Technology Dept. of Computer Engineering Islamabad Pakistan

ISBN: (纸本)9798350355703

A printed circuit board (PCB) is a vital component of any electronic device. Over the years, the need to manufacture PCBs in large volumes has become a necessity due to technological advancement and the expansion of the consumer market. Small defects in the PCB signal trace can have adverse consequences on the functionality of an electronic device. Initially, the quality control process of PCB inspection was carried out manually which was not only tedious but also susceptible to human error. To automate this process, different techniques were developed using classical machine vision that comprised reference comparison, subtraction of images and morphological operations. the methods were effective and reduced inspection time but were sensitive to noise, rotation, and scaling of images. It was difficult to detect defects in complex patterns and only a small fraction of defects could be detected through classical techniques. In this study, four YOLOv5 models were tested for the classification and localization of defects in PCBs. Transfer learning on the bare PCB defect dataset was implemented using pretrained weights of YOLOv5. the PCB dataset was composed of 2,079 images with 6 classes of PCB defects. Data augmentations were carried out to increase the dataset which enhanced performance and generalizability of the models. the models were trained and tested at different image resolutions. Promising experimental results were achieved with the medium YOLOv5 model reaching a mean average precision of 96.92%. © 2024 IEEE.

关键词： Machine vision

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Optimal RTGC controller using Robust PID H∞ Integral-Backstepping under Payload Mass and Rope Length Uncertainties 17

Optimal RTGC Controller using Robust PID H∞ Integral-Backst...

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17th international conference on control, automation, robotics and vision (ICARCV)

作者： Bandong, Steven Nazaruddin, Yul Yunazwin Joelianto, Endra Inst Teknol Bandung Fac Ind Technol Engn Phys Doctoral Program Bandung Indonesia Inst Teknol Bandung Fac Ind Technol Instrumentat & Control Res Grp Bandung Indonesia Inst Teknol Bandung Natl Ctr Sustainable Transportat Technol Bandung Indonesia

ISBN: (纸本)9781665476874

the increase in goods traffic between countries and islands requires the availability of port automation so that the distribution of goods is faster and the quality is guaranteed. One of the control problems for automating the port is controlling the position and sway angle of the container on the Rubber Tyred Gantry Crane (RTGC). However, the RTGC system's model is often uncertain because of the uncertainty in the mass of the payload and the length of the hauling rope when operating. this paper proposes a robust PID controller based on H-infinity integral backstepping to overcome this problem. Particle Swarm Optimization ( PSO), Simulated Annealing (SA) and Genetic Algorithms (GA) are used to optimize the parameter of H-infinity integral backstepping so that the system response follows the desired trajectory. the results show that the control system designed can provide a satisfactory solution to variations in the mass and length of the rope. PSO and SA give better optimal and robust performance than GA.

关键词： H-infinity integral backstepping Robust PID RTGC control system Simulated Annealing Particle Swarm Optimization Genetic Algorithm

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