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

共 1298 条记录，以下是31-40 订阅

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2023 8th international conference on Mechanical engineering and robotics Research, ICMERR 2023

2023 8th International Conference on Mechanical Engineering ...

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8th international conference on Mechanical engineering and robotics Research, ICMERR 2023

ISBN: (纸本)9798350330519

The proceedings contain 16 papers. The topics discussed include: training a neural network to observe the assembly mode of parallel robots;control of variable-size wheeled mobile robots;prototype of an intelligent textile based robot skin to expand potential applications for cobot’s;classification of human postures;overview of synthetic data generation for computer vision in industry;advancements and innovations in U-Net for enhanced medical image segmentation: a review;optimum design of bottom stirring impeller for cosmetics manufacturing;a study of vibration signal feature extraction for bone drilling layer classification;exploring the potential of a vertical takeoff and landing flying car;descent vehicle movement control by the angular position change method payload;and development of an innovative air-ground carrier platform for multi-mode drones.

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Research on Industrial Small Sample Object Data Enhancement Strategy for Robot vision 7

Research on Industrial Small Sample Object Data Enhancement ...

引用

7th international conference on robotics, control and Automation engineering, RCAE 2024

作者： Li, Xuna Wan, Guoyang Jiang, Ming Tao, Xiuwen Wang, Qianqian He, Qin Anhui Polytechnic University School of Electrical Engineering WuHu China

ISBN: (纸本)9798350355642

For the problems of insufficient number of samples and lack of sample diversity faced by industrial scenes in robot vision applications, this paper proposes an improved data enhancement strategy for industrial small-sample objects. The method combines a stochastic algorithm with a deep learning-based image generation technique to generate a large number of realistic real-life style target images from a small number of template images, thus effectively improving the recognition ability of target objects in complex backgrounds. In addition, virtual reality technology is introduced to generate virtual artifact images with similar styles to those captured by actual cameras using a virtual engine, further enriching the diversity and coverage of the dataset. This technique also employs an advanced algorithm that incorporates a convolutional network and a self-attention module in a cyclic adversarial generative network (CycleGan) to achieve efficient generation and style migration of industrial object images. This data enhancement strategy enriches the training data of the robot vision system and significantly improves the robot's stable detection ability and data quality of targets in complex environments. © 2024 IEEE.

关键词： Robot vision

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Point Cloud Derived from Binocular vision Based on P-KLT Algorithm 9

Point Cloud Derived from Binocular Vision Based on P-KLT Alg...

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9th international conference on Electrical engineering, control and robotics, EECR 2023

作者： Mo, Yumei Guo, Qingda Quan, Yanming Guangdong Technology College Zhaoqing China South China University of Technology Guangzhou Institute of Industrial Intelligence Guangzhou China South China University of Technology Guangzhou China

ISBN: (纸本)9781665491204

The method of sparse point cloud derived from binocular vision based on P-KLT (Pyramid-Kanade-Lucas Tomasi) algorithm is proposed. Firstly, this paper introduces the basic mathematical model of binocular vision and feature point PKLT tracking model. Secondly, the process and steps of sparse point cloud derived from binocular vision are introduced;this paper expounds target area segmentation of the left camera image and the Harris feature point detection. Finally, P-KLT algorithm is used to track the feature points of the left gray image in the right gray image and the matching points are used to solve the 3D point cloud data. In simulation experiments Zhang' s calibration is used to obtain the parameters of binocular vision, the P-KLT algorithm is used to track Harris feature point of left gray image and the target of 3D point clouds are obtained to verify the object size and height. Simulation results show that the method can efficiently obtain the target of sparse three-dimensional (3D) point cloud and the feasibility of the method is validated. © 2023 IEEE.

关键词： Binocular vision

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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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MarineX: Design and Implementation of Unmanned Surface Vessel for vision Guided Navigation 21

MarineX: Design and Implementation of Unmanned Surface Vesse...

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21st international conference on Advanced robotics (ICAR)

作者： Din, Muhayy Ud Humais, Ahmed Akram, Waseem Alblooshi, Mohamed Saoud, Lyes Saad Alblooshi, Abdelrahman Seneviratne, Lakmal Hussain, Irfan Khalifa Univ Ctr Autonomous Robot Syst KUCARS Abu Dhabi U Arab Emirates

ISBN: (纸本)9798350342291

Marine robots, particularly Unmanned Surface Vessels (USVs), have gained considerable attention for their diverse applications in maritime tasks, including search and rescue, environmental monitoring, and maritime security. This paper presents the design and implementation of a USV named marineX. The hardware components of marineX are meticulously developed to ensure robustness, efficiency, and adaptability to varying environmental conditions. Furthermore, the integration of a vision-based object tracking algorithm empowers marineX to autonomously track and monitor specific objects on the water surface. The control system utilizes PID control, enabling precise navigation of marineX while maintaining a desired course and distance to the target object. To assess the performance of marineX, comprehensive testing is conducted, encompassing simulation, trials in the marine pool, and real-world tests in the open sea. The successful outcomes of these tests demonstrate the USV's capabilities in achieving real-time object tracking, showcasing its potential for various applications in maritime operations.

关键词： Autonomous navigation marine robotics autonomous systems.

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Hand Gesture Recognition-Based control System Using Computer vision for Transporter Robot

Hand Gesture Recognition-Based Control System Using Computer...

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2023 international conference on Radar, Antenna, Microwave, Electronics, and Telecommunications, ICRAMET 2023

作者： Rianu, Bagas Martinus Kitagawa, Akio Dirgantara, Wahyu Subairi University of Merdeka Malang Department of Electrical Engineering Malang Indonesia Kanazawa University Department of Electrical Engineering and Computer Science Kanazawa Japan

ISBN: (纸本)9798350343892

robotics is a discipline that has experienced rapid development in recent decades. One of the main focuses in robotics development is the advancement of control systems. The primary objective is to create control systems that are more natural, easily understood, easily operated by humans, thereby enabling more intuitive interactions between humans and robots. To achieve this goal, research based on hand gesture recognition has been conducted by adapting computer vision methods as the control system for controlling a transporter robot. In this paper, computer vision methods are employed using the image processing framework called MediaPipe. MediaPipe is used to recognize and comprehend hand gesture movements, as well as display hand landmarks. The research utilizes two hand landmark functions: 'Finger Up' for the right hand to control the wheel movement, and 'Range Gesture' for the left hand to control the arm and gripper of the transporter robot. The testing results indicate that 'Finger Up' function for the right hand successfully controls robot transporter's wheels with a success rate of 100%. Meanwhile, 'Range Gesture' function for the left hand effectively controls the transporter robot's arm and gripper with a success rate of 97%, with a total average error of 3%. © 2023 IEEE.

关键词： Computer control systems

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Machine vision and Artificial Intelligence in robotics for Smart Factory

Machine Vision and Artificial Intelligence in Robotics for S...

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2023 IEEE international conference on Emerging Trends in engineering, Sciences and Technology, ICES and T 2023

作者： Kanth, Rajeev Heikkonen, Jukka Savonia University of Applied Sciences Kuopio Finland University of Turku Turku Finland

ISBN: (纸本)9781665455602

In this work, we have illustrated how machine vision and artificial intelligence could enhance operations in a smart factory along with a robot via several use cases. The first use case is to teach the robots with a leap motion sensor. In Industry 4.0 environment, the intelligent factories decide the subsequent actions based on the sensor data. The leap motion sensor can control a robot's movements and is beneficial, particularly in smart factory environments. We have demonstrated how a leap motion sensor could control picking and dropping off cylindrical objects. The second use case is to control a robot using a mouse and how the robot could play chess. For this use case, we taught the robot to grip a chess object and put it in the designated place. Similarly, we also use a machine vision camera for detecting several entities, such as wingnuts and bolts. The TensorFlow algorithm has been utilized for detecting different shapes and colors. © 2023 IEEE.

关键词： Mammals

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High-Value Fruit Biometric Identification via Triplet-Loss Technique 8

High-Value Fruit Biometric Identification via Triplet-Loss T...

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8th international conference on control and robotics engineering (ICCRE)

作者： Laimek, Plaifah Kongprawechnon, Waree Phatrapornnant, Teera Isshiki, Tsuyoshi Thammasat Univ Sch ICT Sirindhorn Int Inst Technol Bangkok Thailand Natl Elect & Comp Technol Ctr 112 Thailand Sci PkPhahon Yothinn RdKlong 1 Klongluang 12120 Pathum Thani Thailand Tokyo Inst Technol Ookayama Campus 2-12-1 OokayamaMeguro Ku Tokyo 1528550 Japan

ISBN: (纸本)9798350345650

High-valued products require an authentication method to provide the customers with a way to verify the product's genuineness. As the product's authenticity dramatically increases its value, the authentication method has to be reliable and secure. High-valued melon, a popular gift in Japan, is a suitable product for applying rind pattern identification, providing a new means of authenticity verification. As opposed to using only QR-code or RF-ID tags, implementing a rind pattern recognition can provide a more secure authentication method, improving customers' trust and further increasing the product value. A previous study on melon identification was done using a well-known method of fingerprint recognition known as minutiae feature extraction on the melon rind pattern. The study has shown accurate results in the controlled image acquisition environment. In this work, a method of melon identity matching is introduced by incorporating triplet loss function on a convolutional neural network, providing a system that can reliably match each melon image even with variating lighting, shadows, and angle.

关键词： Agriculture Authentication Fruits Melon Rind Pattern Identification Verification Computer vision Machine learning

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Design and Implementation of Automatic Palletizing System with vision Based Algorithms for Quality control 5

Design and Implementation of Automatic Palletizing System wi...

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5th international conference on robotics and Computer vision, ICRCV 2023

作者： Arce, Diego Pérez-Zuñiga, Gustavo Flores, Edelfre Cuellar, Francisco Jimenez, Victor Engineering Department Pontificia Universidad Católica del Perú Lima Peru Calificaciones e Innovaciones en Ingeniería Instituto Quimioterápico S.A. Lima Peru

ISBN: (纸本)9798350326369

In recent years, the pharmaceutical industry has been implementing automatic systems in their manufacturing and packaging processes. This has led to reduced risks, increased productivity, lower costs, and improved competitiveness. The adoption of Industry 4.0 principles and technologies has played a key role in automating processes and optimizing results. Therefore, this paper presents the design, implementation, and preliminary validation of an automatic palletizing and quality control system implemented in the solid drug production line. The proposed system incorporates two main technologies: intelligent computer vision and collaborative robotics, which are complemented with industrial-grade equipment and instruments. The computer vision system performs quality control on pallet boxes by detecting defects, tears, stains, and taping faults on each side of the boxes, as well as recognizing the text characters of the labels for subsequent processing. Additionally, a weight-based quality control is implemented to ensure that all boxes contain the exact number of solid drugs. Through preliminary validation tests, the viability of using this automated mechatronic system for the handling and quality control of boxes was demonstrated. The handling process was carried out using a robot, and it was determined that the robot can complete palletizing and quality control of a pallet of 21 boxes in a total of 11 minutes and 33 seconds. This innovative solution represents a successful application of Industry 4.0 principles and technologies in the pharmaceutical industry, enabling companies to further optimize their processes and remain competitive in the market. © 2023 IEEE.

关键词： Computer vision

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The dental implant robot system based on binocular vision 5

The dental implant robot system based on binocular vision

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5th international conference on robotics and Computer vision, ICRCV 2023

作者： Yan, Zhuangzhuang Tang, Qirong Wang, Fang Guo, Ruiqin Wu, Xianglong Liu, Qingyun School of Mechanical Engineering Anhui University of Technology Laboratory of Robotics and Multibody System Tongji University Maanshan China Laboratory of Robotics and Multibody System Tongji University Shanghai China The Affiliated Stomatology Hospital of Tongji University Tongji University Shanghai China School of Mechanical Engineering Anhui University of Technology Maanshan China

ISBN: (纸本)9798350326369

The dental implant surgery robot is currently one of the hot topics in dental implant technology research. This study proposed a dental implant robot system based on binocular vision navigation and presented the basic principles and methods of the localization system. By analyzing the surgical process and localization features of the dental implant robot, a tracking and localization strategy for the end effector of the robot was planned, and a method for obtaining pose parameters was provided. Finally, motion planning simulations were conducted in the Robot Operating System (ROS) simulation environment based on the target pose information provided by the binocular vision system. The simulation results verified the feasibility of the proposed dental implant robot system and provided a solid foundation for further practical research of the system. © 2023 IEEE.

关键词： Binocular vision

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