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

共 5312 条记录，以下是651-660 订阅

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A Triangular Descriptor with Distance Information for Loop Closure Detection in LiDAR SLAM

A Triangular Descriptor with Distance Information for Loop C...

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robotics, Intelligent control and Artificial Intelligence (RICAI), international conference on

作者： Yucheng Xie Zhong Yang College of Automation Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (数字)9798331541699

ISBN: (纸本)9798331541705

In the field of mobile robotics, loop closure detection serves as an essential component of Simultaneous Localization and Mapping (SLAM). Compared with vision-based approaches, LiDAR-based loop closure detection offers more stable geometric features. this paper proposes a novel triangular descriptor with distance information. the proposed method extracts keypoints through adaptive Euclidean clustering, constructs distance vectors based on keypoint relationships, and generates the descriptor by integrating triangular geometric features. Experiments on the KITTI dataset demonstrate that our method outperforms existing approaches with superior maximum F1 scores across multiple sequences. Integration of the algorithm into the LeGO-LOAM system reduces cumulative drift error by 31.16%, validating the effectiveness of the proposed approach.

关键词： Point cloud compression Simultaneous localization and mapping Laser radar Clustering algorithms Feature extraction Vectors Robustness Trajectory Odometry Robots

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Visual Feedback control of Print Trajectory in FDM-Type 3D Printing Process

Visual Feedback Control of Print Trajectory in FDM-Type 3D P...

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IEEE international conference on control and robotics Engineering (ICCRE)

作者： Shinichi Ishikawa Ryosuke Tasaki Departmeny of Mechanical Engineering Aoyama Gakuin University Sagamihara Japan

In recent years, additive manufacturing of FDM: Fused Deposition Modeling type 3D printing has been rapidly developed. However, failures in molding are inevitable, leading to loss of time and materials. In this study, we propose a motion control method that suppresses failures during the build process by using visual feedback control with an RGB camera. A camera installed near the nozzle detects the molding line and generates a new path from the coordinates obtained by the camera, thereby realizing path modification in real-time. In this paper, we conducted experiments to verify the effectiveness of the proposed path modification method and evaluated the tracking and modification effects. Moreover, we conducted a modeling experiment with visual feedback and confirmed the effectiveness of the vision-based path modification method to suppress failures beforehand. this vision-based compensation system is expected to contribute to automation and quality improvement of the additive manufacturing process and the production system.

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Research on Upper Limb Rehabilitation Action Recognition Method of Unsupervised Contrast Learning Based on Time-domain Multi-scale Feature Fusion 5

Research on Upper Limb Rehabilitation Action Recognition Met...

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5th international conference on Intelligent robotics and control Engineering, IRCE 2022

作者： Sun, Yuechao Li, Xing Wang, Jianhui Li, Donglin Zhu, Yuxuan School of Information Science and Engineering Northeastern University Shen Yang China School of Electrical Engineering & Intelligentization Dongguan University of Technology Dongguan China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang China

ISBN: (数字)9781665469951

ISBN: (纸本)9781665469951

vision-based action recognition technology plays a major role in human-robot interaction during the upper limb rehabilitation training of stroke patients with the aid of an upper limb rehabilitation robot. To address the problems that supervised learning requires a large amount of manual labeling, which is time-consuming and inefficient, in this paper, we present an upper limb rehabilitation action recognition method of unsupervised contrast learning based on multi-scale feature fusion and we improves SimCLR by replacing the single-scale CNN with Inception V1 which contains the multi-scale feature fusion module and increasing the temporal dimension based on the idea of the expanded convolutional neural network to fully extract the Spatio-temporal feature information of the actions. the experiment results show that unsupervised contrast learning based on time-domain multi-scale feature fusion achieves recognition accuracy of 92.59% for 8 upper limb rehabilitation actions without extensive manual labeling. It can help patients recover their motor functions through upper limb rehabilitation training. © 2022 IEEE.

关键词： Time domain analysis

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Cooperative localization between robots using vision and path planning algorithm 6

Cooperative localization between robots using vision and pat...

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6th international conference on automation, control and robotics Engineering (CACRE)

作者： Koduru, Charles Tanveer, M. Hassan Kennesaw State Univ Robot & Mech Engn Marietta GA 30144 USA

ISBN: (纸本)9781665435765

the aim of this article is to provide a solution for obstacle avoidance in order to navigate a robot from an initial point to a final point. the considered robots are i) NAO Humanoid robot, and ii) Rosbot 2.0 a wheeled robot. the main focus of this article is to understand the collaboration between robots to reduce positioning errors. By integrating Inverse Perspective Mapping techniques (IPM) and the A-Star algorithm the image processing robot (NAO) would be able to instruct the wheeled bot (Rosbot) to navigate around obstacles more effectively. this method shows the capability to advance the collaboration between robots in order to diagnose different collisions and develop different path following techniques. Even though odometry is a widely used technique for calculating the position of robots, it has certain drawbacks in the long run. To combat this, we addressed solutions to these drawbacks by using Inverse Perspective Mapping and the A-Star algorithm. When IPM is applied, calculating the position of the obstacles relative to the wheeled robot makes the path network more accurate. Additionally, by applying the A-Star algorithm the path network can be analyzed to find the most direct route. the attractiveness of this methodology simplifies the navigation process by analyzing the data at a birds-eye view and computing decisions that will instruct the wheeled robot to take the shortest path. the performances of the proposed approach are analyzed by means of a series of experiments performed in a static environment.

关键词： Static Environment Humanoid Robot Inverse Perspective mapping Path planning obstacle avoidance

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vision-Based Autonomous Driving with Look-Ahead Distance

Vision-Based Autonomous Driving with Look-Ahead Distance

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international conference on control, automation and Systems ( ICCAS)

作者： Hyunah Kim Yongjun Lee Sunho Jang Myotaeg Lim School of Electrical Engineering Korea University Seoul Korea Korea Institute of Robotics & Technology Convergence Andong Korea

ISBN: (数字)9788993215380

ISBN: (纸本)9798331517939

this paper introduces an autonomous driving approach based on look-ahead distance-based model predictive control. this study evaluates the obstacle-dependent gaussian-potential field approach, which utilizes Gaussian functions to dynamically assess the risk from obstacles. It demonstrates significant performance in stable and reliable navigation for autonomous vehicles by combining with the look-ahead distance-based model predictive control method. the look-ahead distance-based model predictive control method employs only camera and IMU sensors to predict the road ahead, reducing the dependency on GPS and improving both ride comfort and tracking performance. the proposed method significantly improves autonomous driving in dynamic and complex road environments.

关键词： Navigation Roads control systems Cameras Sensors Reliability Vehicle dynamics Autonomous vehicles Predictive control Global Positioning System

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Efficient Real-time Detection of Complex Tire Cord Defects on Airjet Loom

Efficient Real-time Detection of Complex Tire Cord Defects o...

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automation, control and robotics Engineering (CACRE), international conference on

作者： Juan Du Yuhan Li School of Automation Science and Engineering South China University of Technology Guangzhou China

ISBN: (数字)9798350350302

ISBN: (纸本)9798350350319

the paper presents an improved YOLOv8 model tailored for the detection of defects in air-jet loom tire cord fabric, a critical component in the automotive industry. Current defect detection methods, predominantly manual, are inefficient and prone to errors, necessitating an automated solution. Leveraging advancements in machine vision and deep learning, this study presents an vision-based defect detection model that tackles the distinctive obstacles posed by the complex background, variations in target sizes, and indistinct defect features of tire cord fabric defects. the proposed model integrates a novel BiFPN (Bidirectional Feature Pyramid Network) into the YOLOv8 framework to enhance feature fusion and employs a Dynamic Head mechanism to improve scale, spatial, and task awareness. Additionally, a new loss function, Wise-Inner-Shape IoU, is introduced to refine the localization of defects. the model's effectiveness is assessed utilizing a custom dataset of air-jet loom tire cord fabric, demonstrating a notable 4.0 % enhancement over the original YOLOv8 architecture in the mean Average Precision (mAP) at a threshold of 0.5. the results show that the model is effective in achieving precise defect detection, which is essential for upholding quality standards of tire cord fabric in industrial production.

关键词： Location awareness Atmospheric modeling Feature extraction Tires Fabrics Magnetic heads Vehicle dynamics

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Personalized Autocomplete Teleoperation: Real-Time User Adaptation using Transfer Learning with Partial Feedback 11

Personalized Autocomplete Teleoperation: Real-Time User Adap...

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11th IEEE Annual international conference on CYBER Technology in automation, control, and Intelligent Systems, CYBER 2021

作者： Hussein, Mohammad Haj Elhajj, Imad H. Asmar, Daniel Vision and Robotics Lab American University of Beirut Beirut Lebanon

ISBN: (纸本)9781665425278

Autocomplete enhances teleoperation by recognizing operator intentions and automating motions when instructed. this has shown to improve performance and reduce operator load. Traditionally, recognizing operator intended motions is accomplished using pre-trained deep learning (DL) models. In this paper, we introduce personalization to the Autocomplete Teleoperation Framework. By customize the Autocom-plete DL model using transfer learning with partial feedback. this approach allows the model to adapt to the specific operator in a relatively short period. Several scenarios have been evaluated to balance the speed of learning with the accuracy of the model. Results demonstrate the effectiveness of the proposed method in adapting to the specific operator. © 2021 IEEE.

关键词： Remote control

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Research on product identification and positioning technology of machine vision based on Yolo detection framework 11

Research on product identification and positioning technolog...

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11th IEEE Annual international conference on CYBER Technology in automation, control, and Intelligent Systems, CYBER 2021

作者： Wu, Fangzheng Rutgers-the State University of New Jersey Computer Science Department PiscatawayNJ08854 United States

ISBN: (纸本)9781665425278

Machine vision is one of the core technologies for modern enterprises to develop unmanned, automated, and intelligent systems. First, conduct research on image acquisition and data expansion technology, carry out product identification analysis based on deep learning in order to further improve the unmanned and intelligent level of the production site, then perform product positioning based on the YOLO detection framework, and finally complete the application verification of product identification and positioning. Faced with the reality that the real-time requirements of product inspection on the production line are constantly increasing, the YOLO inspection framework is selected, and the model convolution structure and anchor points are added to meet the needs of product identification and positioning on the production line. the software environment and model construction are improved through the selection of light source, camera and lens, and finally the application practice of product identification and positioning is completed. Research and practice reveal that the experimental model can meet the real-time needs of intelligent manufacturing at a detection speed of more than 20 FPS under the premise of ensuring accuracy. © 2021 IEEE.

关键词： Computer vision

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Computer and vision Aided Mapping in Farmer's Market and Visually Aided Shopping Strategy for Visually Impaired Individuals

Computer and Vision Aided Mapping in Farmer's Market and Vis...

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Computer, control and robotics (ICCCR), international conference on

作者： Mei Liu Yunhua Chen Hanlin Chen Xinyue Liu Yuan Fang Jinbo Chen Shanghai Key Laboratory of Intelligent Manufacturing and Robotics School of Mechatronic Engineering and Automation Shanghai University Shanghai China

ISBN: (数字)9798350373141

ISBN: (纸本)9798350373158

Existing guide equipment and methods are not fully suitable for the visually impaired (VI) shop in the crowded and narrow farmers' market. Aim assist VI to purchase food more conveniently, this paper proposes computer and vision aided mapping (CVAM) algorithm. It utilizes threshold function to quickly convert the CAD layout of the farmer's market into a navigation-specific grid map. Furthermore, CVAM utilizes the A-star method to traverse each stall, and employs a classification camera based deep learning to identify and record the products on stall. Additionally, this paper proposes a visually aided shopping (VAS) algorithm that allows robot minor seller to select and weigh products. It employs the KCF method to track the seller's hand movements and uses a classification camera to ensure that the products selected by the seller are consistent with the VI's expectations. Subsequently, it uses BP neural network to read the electronic scale indication and feed it back to VI. the experiments conducted in this study confirmed that the guide robot designed in this study is not only capable of navigating the farmer's market for VI, but also greatly improves mapping efficiency compared with general mapping method. Particularly, VAS successfully supervises the entire process of seller selecting and weighing products, preventing them from deliberately deceiving VI.

关键词： Navigation Tracking Robot vision systems Neural networks Layout Process control Cameras

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Smart visual sensors for real time weather and ground conditions recognition for agricultural robotics

Smart visual sensors for real time weather and ground condit...

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IEEE international conference on automation Science and Engineering (CASE)

作者： Diego Gragnaniello Antonio Greco Carlo Sansone Bruno Vento Department of Information and Electrical Engineering and Applied Mathematics (DIEM) University of Salerno Italy Department of Electrical Engineering and Information Technology (DIETI) University of Napoli Federico II Italy

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

the dynamic and unpredictable nature of environmental elements, such as weather and ground conditions, profoundly influences the efficiency of agricultural robots. Real-time environmental perception is an enabling technology for several agricultural automatic systems, facilitating precise scheduling of tasks like planting, harvesting, and irrigation. Drawing inspiration from successful applications of computer vision in crop health monitoring and AI-driven weather impact prediction, in this paper we propose a system for simultaneous recognition of weather and ground conditions based on computer vision techniques. the system can run in real-time onboard smart cameras, making it affordable at low cost. After providing a detailed description of its architecture and methodology, we conduct extensive experiments to demonstrate the effectiveness of the system in real-world conditions and its significance in advancing agricultural robotics, fostering sustainable farming practices by optimizing resource utilization, and mitigating environmental impact.

关键词： Visualization Smart cameras Robot sensing systems Real-time systems Resource management Sustainable development Intelligent sensors Remote control Meteorology Farming

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