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检索条件"任意字段=2023 International Conference on Robotics, Control and Vision Engineering, RCVE 2023"

共 1305 条记录，以下是241-250 订阅

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Aggregation Mechanisms in Federated Learning for Enhancing Robotic Visual Obstacle Avoidance 5

Aggregation Mechanisms in Federated Learning for Enhancing R...

引用

5th international conference on robotics, Intelligent control and Artificial Intelligence, RICAI 2023

作者： Zhang, Haizhou Cheng, Qianyu Chen, Xiong School of Information Science and Technology Fudan University Shanghai China City University of HongKong Department of Computer Science Hong Kong Hong Kong

ISBN: (纸本)9798350357950

In the realms of robotics and the autonomous driving field, deep learning has been a game-changer for applications such as perception, obstacle avoidance, SLAM, etc. The success of these deep learning algorithms, however, relies on large-scale data sets for training, raising substantial privacy concerns for users unwilling to share raw data. Addressing this issue, our study explores federated learning in robotics to leverage its inherent privacy-protection attributes. Federated learning, a privacy-conscious method within the machine learning paradigm, has become increasingly prominent in recent years. More importantly, an essential aspect of our work is the assessment of diverse aggregation mechanisms within the federated learning framework, aiming at enhancing robotic functionality. We gather data and implement a federated learning model using various aggregation algorithms in visual obstacle avoidance contexts. The results demonstrate considerable variances in the performance of these algorithms, indicating that certain federated learning aggregation methods can exceed the performance of traditional centralized learning models. This study affirms the feasibility and advantages of federated learning in robotic applications, publicly available on github. © 2023 IEEE.

关键词： aggregation algorithms autonomous robots federated learning privacy protection vision-based obstacle avoidance

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Obstacle detection system based on light markers grid deformation 3

Obstacle detection system based on light markers grid deform...

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3rd international conference on Optics, Computer Applications, and Materials Science, CMSD 2023

作者： Verkhovtsev, Daniil O. Popinako, Yaroslav V. Nikitenko, Mikhail S. Khudonogov, Danila Yu. Kizilov, Sergey A. Federal State Budgetary Scientific Institution the Federal Research Center of Coal Coal Chemistry of Siberian Branch Russian Academy of Science 18 Sovetsky ave Kemerovo Russia

ISBN: (纸本)9781510674486

The article considers autonomous vehicle control system using obstacle detection method based on machine vision using light markers on motion path. The data gaining method for optimization and calculating image is proposed. The specific feature of realizing the aim is light markers grid deformation consideration. The software realization of obstacle detection based on light markers grid geometric deformation is demonstrated. © 2024 SPIE. All rights reserved.

关键词： Computer vision

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Design of Garbage Intelligent Recognition and Automatic Classification Device 4

Design of Garbage Intelligent Recognition and Automatic Clas...

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4th international conference on Advances in Electrical engineering and Computer Applications, AEECA 2023

作者： Wu, Jiaqi Zhang, Yuhao Yi, Haibo Deng, Weiping Qu, Tao School of Artificial Intelligence Shenzhen Polytechnic Shenzhen China WeBank Shenzhen China Guangzhou Urban Blockchain Industry Association Guangzhou China

ISBN: (纸本)9798350308082

Artificial Intelligence (AI) has received increasing attention in the field of computer science. and it has been applied in robotics, economic and political decision-making, control systems, and simulation systems. In recent years, there have been efforts to accelerate the classification of household waste, establish a garbage classification and recycling system, and expedite the establishment of a garbage treatment infrastructure for sorting, placement, collection, transportation, and processing. The Raspberry Pi is a credit card-sized microcomputer with a Linux-based operating system. Given the need for a smaller recognition device with higher computational complexity, we choose the Raspberry Pi as our hardware platform for this project. Utilizing the Raspberry Pi's lightweight hardware and AI, intelligent garbage classification can achieve greater accuracy, speed, and efficiency. This article presents an intelligent garbage classification device designed using the Raspberry Pi 4B, which can be used in various scenarios due to its small footprint. The design method proposed in this paper can be widely applied to fields such as smart homes and smart cities. © 2023 IEEE.

关键词： Computer vision

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Using Computer vision to Improve SME Performance 32nd

Using Computer Vision to Improve SME Performance

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32nd international conference on Flexible Automation and Intelligent Manufacturing

作者： Lissassi, Kokou C. Dossou, Paul-Eric Sabourin, Christophe ICAM Site Grand Paris Sud F-77127 Lieusaint France Univ Gustave Eiffel F-77420 Champs Sur Marne France Univ Paris Est Creteil LISSI F-77567 Lieusaint France

ISBN: (纸本)9783031382406;9783031382413

The versatility of customer demand in addition to globalization involves companies to focus on how to improve their global performance. Industry 4.0 concepts are used by large companies for increasing their performance. Despite the success of these concepts, SMEs are reluctant to exploit them for their digital transformation. Nevertheless, human-machine collaboration by combining sustainability aspects with these new technologies corresponds to a solution for their performance improvement. This paper focuses on the use of computer vision and mobile robots for optimizing a production manufacturing through lean manufacturing methodology exploitation. Indeed, in a production line, products or raw materials transportation corresponds to a waste. A literature review based on organizational methods, industry 4.0 and vision including artificial intelligence tools allows to find concepts that will contribute to develop the solution for SMEs. This paper proposes a sustainable methodology, supported by an intelligent tool to reduce in SMEs, this motion waste and increase the operator's well-being at work. An illustration based on an electronics card SME is presented to validate the concepts and tool that have been elaborated.

关键词： Performance optimization Computer vision Artificial intelligence robotics Lean manufacturing

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Low-altitude Airspace Conflict Detection and Avoidance Algorithm Based on Computer vision and Multi-sensors

Low-altitude Airspace Conflict Detection and Avoidance Algor...

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2023 international conference on Power, Electrical engineering, Electronics and control, PEEEC 2023

作者： Song, Ji Sanya Aviation and Tourism College Hainan Sanya572000 China

ISBN: (纸本)9798350329124

The development and application bring a large quantity of unmanned aerial vehicle (UAV) flight activities, which directly affect the flight safety in complex airspace. UAV path planning algorithm is based on the task type to be completed, and then combined with the known planning space and terrain information, it plans an optimal path for UAV or UAV group that conforms to the real flying of UAV. Improving UAV's perception and avoidance ability and avoiding the collision danger between UAVs or between UAVs and manned aircraft is a core ability for UAV to enter non-isolated airspace. In order to prevent aircraft from colliding in low-altitude airspace, this paper analyzes the conflict prediction and avoidance methods between aircraft flying at the same altitude in low-altitude airspace, and puts forward the conflict detection and avoidance algorithm in low-altitude airspace based on binocular stereo vision and multi-sensors. In order to get more accurate position information faster, stereo vision and optical flow are combined. The experimental results show that this method can effectively improve the efficiency and accuracy of avoidance. © 2023 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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vision-based Defects Detection and Interactive Impedance control in Robotic Automated Layup Tasks

Vision-based Defects Detection and Interactive Impedance Con...

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2023 IEEE international conference on robotics and Biomimetics, ROBIO 2023

作者： Zheng, Haochen Zhai, Xueqian Gu, Shichao Liao, Zhaoyang Pan, Jia Xu, Zhihao Zhou, Xuefeng Mao, Shixin Wu, Hongmin Guangdong Key Laboratory of Modern Control Technology Institute of Intelligent Manufacturing Guangdong Academy of Sciences Guangzhou China Wuyi University Jiangmen China The University of Hong Kong Hong Kong Pazhou Lab Guangzhou China Intelligent Technology Co. Ltd Foshan China

ISBN: (纸本)9798350325706

This paper proposes a robotic automated layup framework consisting of three components: human-robot layup skill transfer, vision-based quality detection, and adaptive impedance controller. The framework aims to address challenges and limitations faced by manual Carbon Fiber Reinforced Polymer (CFRP) layup, such as high labor intensity, poor quality consistency, and limited scalability. In the proposed framework, firstly, on a horizontal plane, humans demonstrate the layup behavior by dragging the robot in gravity compensation mode. During the demonstration, the Cartesian position, orientation, and interaction forces of the robot's end-effector are recorded synchronously. Secondly, utilizing the Mask R-CNN instance segmentation model, defects such as bubbles and wrinkles on the CFRP surface are detected. Depth feature extraction and bounding boxes are employed to determine the position and orientation of the defects. Lastly, based on hand-eye calibration and coordinate system transformation, the robotic adaptive impedance controller repeats the layup behavior demonstrated at the defect pose points for automated defect repair. The feasibility and effectiveness of the proposed framework were validated in real-world scenarios, demonstrating its suitability for automated layup tasks. © 2023 IEEE.

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Deep Learning Methods for Ship Classification: From Visible to Infrared Images 5

Deep Learning Methods for Ship Classification: From Visible ...

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5th international conference on robotics, Intelligent control and Artificial Intelligence, RICAI 2023

作者： Liu, Tianci Qin, Hengjia Zhan, Zhuo Liu, Yunpeng Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China Chinese Academy of Sciences Key Laboratory of Opto-Electronic Information Processing Shenyang110016 China Key Laboratory of Image Understanding and Computer Vision Liaoning Province Shenyang110016 China The Third Military Representative Office of the Air Force Equipment Department in Shenyang Region Liaoning Province Shenyang110027 China

ISBN: (纸本)9798350357950

Deep learning methods have achieved excellent performances on visual tasks of target recognition and classification. The rapid development of autonomous seafaring vessels comes up with the requirement to recognize other maritime ships day and night. However, the recognition of ships based on the deep neural networks may not always access the results as expectation when the ships are under the nighttime environment. To this issue, we consider the ship recognition task under the deep learning framework with paired visible images and infrared images. In this article, we propose an end-to-end convolutional network based on visible images and infrared images of the autonomous seafaring vessels. To demonstrate the effectiveness of our model, we choose the V AIS dataset to test the performance of classifying the maritime ships. Experimental results show that the proposed network outperforms the state-of-the-art methods based on the V AIS database. © 2023 IEEE.

关键词： deep learning infrared images neural network Ship recognition

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Autonomous Patrol Technology and System on Leapfrog-Type Charging UAV - Automatic Charging control based on Machine vision 6

Autonomous Patrol Technology and System on Leapfrog-Type Cha...

引用

6th international conference on Energy, Electrical and Power engineering, CEEPE 2023

作者： Guan, Jiahua Lu, Jiahao Jian, Yaofeng Mai, Zitian Li, Zhihao Chen, Haojun Li, Donglun Zheng, Qiting Foshan Power Supply Bureau Guangdong Power Grid Co Ltd. Foshan China

ISBN: (纸本)9798350348279

Rotor-craft unmanned aerial vehicle (UAV) has been applied rapidly in the domestic electric power system. However, endurance time of the drone loaded with equipment is limited in 20 minutes. Therefore, this paper proposes a key technology of automatic leapfrog-type charging control based on machine vision. Leapfrog-type charging platform is composed of UAV platform, UAV automatic charging platform and ground control system. According to both flight control and intelligent control, with the aid of machine vision and image recognition technology on positioning and intelligent landing UAV with high precision, and effective docking of preset slots and charging interfaces between ground charging platform and UAV, the battery of UAV charges automatically reliably, so as to realize the take-off and landing and automatically recharging UAV. The experimental results show that the problem of high precision positioning in charging process of UAV is effectively solved by implementing the leapfrog charging platform, machine vision, image recognition and shutdown technology. © 2023 IEEE.

关键词： Computer vision

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Real-Time Weapons Detection System using Computer vision 3

Real-Time Weapons Detection System using Computer Vision

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3rd international conference on Smart Technologies, Communication and robotics, STCR 2023

作者： Nale, Pranav Gite, Shilpa Dharrao, Deepak Symbiosis Institute of Technology Department of Information Technology Maharashtra Pune412115 India Symbiosis Institute of Technology Department of Artificial Intelligence and Machine Learning Maharashtra Pune412115 India Symbiosis Institute of Technology Department of Computer Science and Engineering Maharashtra Pune412115 India

ISBN: (纸本)9798350370867

The growing use of Closed-Circuit Television (CCTV) systems in modern security applications has driven the need for automated surveillance through computer vision. The primary aim is to reduce human intervention while enhancing early threat detection and real-time security assessments. Although advanced surveillance technologies have facilitated monitoring, constant human oversight remains challenging. This has prompted a quest for models capable of identifying unlawful activities with minimal human involvement. Real-time weapon detection, despite advancements in deep learning algorithms and dedicated CCTV cameras, remains a formidable challenge, especially with varying angles and potential obstructions. Existing detection systems are often expensive and require specialized tools, necessitating a cost-effective and reliable alternative that minimizes false positives. This research focuses on creating a secure environment by utilizing real-time resources and deep-learning algorithms for identifying dangerous weapons. Without a predefined dataset for real-time detection, the researchers compiled one from diverse sources, including camera shots, internet images, movie data, YouTube CCTV recordings, and Roboflow Computer vision Datasets. The proposed weapon detection system employs a hybrid model of Detectron2 and YOLOv7, emphasizing precision and recall in object detection, particularly in challenging conditions like lowlight environments. This research contributes to developing an effective, reliable real-time weapon detection system tailored for diverse scenarios. © 2023 IEEE.

关键词： Object detection

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Noncontact Particle Manipulation on Water Surface with Ultrasonic Phased Array System and Microscopic vision

Noncontact Particle Manipulation on Water Surface with Ultra...

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IEEE international conference on robotics and Automation (ICRA)

作者： Zhang, Yexin Li, Jiaqi Jia, Yuyu Li, Teng Wang, Yang Jeong, David C. Su, Hu Liu, Song ShanghaiTech Univ Sch Informat Sci & Technol Shanghai 201210 Peoples R China Santa Clara Univ Dept Commun Santa Clara CA 95053 USA Chinese Acad Sci Inst Automat Beijing 100190 Peoples R China Shanghai Engn Res Ctr Intelligent Vis & Imaging Shanghai Peoples R China

ISBN: (纸本)9798350323658

Noncontact particle manipulation (NPM) shows great application potential than its conventional counterpart particularly in terms of non-invasiveness, and thus has significantly extended robotic manipulation capacity into bio-medical engineering, material science, etc. As NPM by means of electric, magnetic, and optical field has successfully demonstrated powerful strength in both academia and industry, NPM boosted by acoustic field, however, still faces staggering challenges. It is indeed in the very recent years that controllable dynamic airborne or waterborne acoustic field modulation technology emerged in academia. In this paper, we report our latest research regarding dexterous and dynamic noncontact micro-particle manipulation on water surface effected by acoustic field in terms of automated trapping, closed-loop positioning, and real-time motion planning, which can be applied to scenarios such as parallel 3D printing, cell assembly, etc. The main contribution of this work is we demonstrated the feasibility of objective-oriented and fully automated acoustic manipulation of micro-particle in precision scale based on robotic approach in 2D plane. Experiment results showed that the repetitive positioning accuracy can reach as high as 16 mu m, which is essentially the pixel scale factor.

关键词： Acoustic fields

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