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Research of volleyball robot based on YOLOv8 lightweight and binocular vision 4

Research of volleyball robot based on YOLOv8 lightweight and...

引用

4th IEEE International conference on Data Science and computer Application, ICDSCA 2024

作者： Zhao, Yiyao Wang, Jiahui Cui, Yanjiang Zhan, Haonan Yu, Qing Zhao, Juju Dalian Polytechnic University Dalian China

ISBN: (纸本)9798350368239

the study of volleyball robot capture mechanism is a challenging problem, and how to realize real-time and high-precision capture is the focus of current research. In order to solve these problems, this paper designs a capture system based on an improved YOLO model and binocular vision scheme for the study of volleyball robot capture mechanism. By improving faster as the backbone network and introducing the EMA attention mechanism, the YOLO model is realized to be lightweight, and the parameter computation is reduced by 26.5%, which ensures the real-time performance of the volleyball robot;meanwhile, the accuracy is improved by 1.1%. On this basis, the improved YOLO is combined with an improved binocular vision scheme based on the principle of solving triangles to obtain the accurate world coordinate system of the volleyball relative to the disk, ensuring that the robot can solve the world coordinates of the volleyball in real time. In practical applications, the distance error within 2 meters is always kept within 1 cm. through experimental verification, the design scheme in this paper can effectively improve the real-time and accuracy of the robot capture mechanism, and make an important contribution to the development of the robot capture mechanism. ©2024 IEEE.

关键词： Binocular vision

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Research and Simulation of Intelligent Grasping of robotic Arm Based on Machine vision Recognition 4

Research and Simulation of Intelligent Grasping of Robotic A...

引用

4th IEEE International conference on Power, Electronics and computer Applications, ICPECA 2024

作者： Dong, Jinshun Deng, Lixia Wan, Dapeng Liu, Haiying Jinan China

ISBN: (纸本)9798350359589

With the economic and social development of human beings, people have higher and higher requirements for the intelligence of machinery and equipment. With the vigorous development of robot vision technology, robots are becoming more and more widely used in various industries, and they also gradually have 'eyes'. this not only broadens the scope of its use, but also improves its intelligence. In this paper, the PUMA-560 manipulator was simulated to realize its visual recognition and grasping functions. It has been applied to the original industrial robots. For example, the six-axis manipulator used this time is to obtain relevant information about the target and the surrounding environment through computer vision technology, so as to accurately identify the action to be carried out, and respond according to the action to guide the robot to grasp. © 2024 IEEE.

关键词： Six-axis robotic arm Smart grabbing visual identity

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computer and vision Aided Mapping in Farmer's Market and Visually Aided Shopping Strategy for Visually Impaired Individuals 4

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

引用

4th International conference on computer, Control and robotics (ICCCR)

作者： Liu, Mei Chen, Yunhua Chen, Hanlin Liu, Xinyue Fang, Yuan Chen, Jinbo Shanghai Univ Sch Mechatron Engn & Automat Shanghai Key Lab Intelligent Mfg & Robot Shanghai 200444 Peoples R China

ISBN: (纸本)9798350373141;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： efficient mapping visually aided product classification deep learning supervise seller

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A Review of Machine vision Applications in Aerospace Manufacturing Quality Inspection 4

A Review of Machine Vision Applications in Aerospace Manufac...

引用

4th International conference on computer, Control and robotics (ICCCR)

作者： Deng, Li Liu, Gang Shanghai Univ Engn Sci Sch Mech & Automot Engn Shanghai Peoples R China

ISBN: (纸本)9798350373141;9798350373158

Machine vision is a technology that uses image processing and analysis techniques to acquire and understand image information, enabling the recognition, measurement, and detection of objects. Machine vision is widely used in aerospace manufacturing for automated production, quality inspection, and robot guidance. It can improve the efficiency and quality of aerospace manufacturing, reduce labor costs and risks, promote innovation and optimization, adapt to various inspection needs, and realize intelligent, automated, and digital manufacturing processes. this paper introduces the types of vision technologies, overviews the typical applications of machine vision for quality inspection in aerospace manufacturing, (including component surface inspection, drilling quality inspection, assembly quality inspection, and gluing quality inspection), analyzes the advantages, challenges, and development trends of machine vision for quality inspection in aerospace manufacturing, and looks forward to future research directions to support further research and application.

关键词： Machine vision Aerospace manufacturing Component surface inspection Drilling quality inspection Assembly quality inspection Gluing quality inspection

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Design and Deployment of computer vision based Smart Patrolling robot using UP Squared Board 4

Design and Deployment of Computer Vision based Smart Patroll...

引用

4th IEEE Global conference for Advancement in Technology, GCAT 2023

作者： Bale, John Babu Manoj Reddy, N. Naziya thabasum, N. Bhargavi, S. Nithya, M. Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350305258

Human patrolling and surveillance operations often involve inherent risks and inefficiencies. To address these challenges, this work discusses the design and development of a patrolling robot using the UP Squared board, a powerful maker board, harnessing the capabilities of the OpenCV computer vision library. the robot is designed to perform efficient patrolling and surveillance tasks while autonomously avoiding obstacles. Utilizing video processing techniques enabled by OpenCV, the robot accurately detects intruders and promptly alerts human operators. Additionally, obstacle avoidance sensors are incorporated to ensure the robot navigates safely and avoids potential hindrances. By combining robust surveillance, accurate intruder detection, and obstacle avoidance, this research contributes to developing a reliable and effective patrolling system. © 2023 IEEE.

关键词： computer vision Debian Packages GPIO intruder detection kernel MRAA patrolling surveillance UP Squared board

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Method for robot recognition and localization based on third-person perspective 4

Method for robot recognition and localization based on third...

引用

4th International conference on Machine Learning and computer Application, ICMLCA 2023

作者： Luo, Zongheng Lu, Jun Chengdu University of Information Technology Chengdu China

ISBN: (数字)9781510680265

ISBN: (纸本)9781510680258

To achieve the recognition and positioning functions of indoor mobile robots under limited computing power conditions, a method based on color recognition for robot recognition and positioning is proposed. the global image of the robot working in the field is collected by a camera outside the field, and the position of the robot is obtained through computer vision processing. then, the robot is controlled to move according to this information. Experiments conducted within a 2m × 1m area have shown that the maximum error during robot operation is 7.8cm/m, with an average error of 7.0cm/m. the maximum error of the steering angle is 16.6°, with an average error of 7.7°. © 2024 SPIE.

关键词： robot applications

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Research on robot Grasping Strategy Based on Deep Learning and K-Means Algorithm 4

Research on Robot Grasping Strategy Based on Deep Learning a...

引用

4th IEEE International conference on Power, Electronics and computer Applications, ICPECA 2024

作者： Wang, Ziyao Li, Kehan Zhou, Zhikun School of Robotics Hunan University Changsha China

ISBN: (纸本)9798350359589

In order to effectively improve the robot grasping accuracy and sorting efficiency of the valve body on the butterfly valve automated assembly line, a butterfly valve body grasping strategy based on machine vision and YOLOv3 deep learning algorithm is proposed. Based on the YOLOv3 algorithm, the visual inspection model of the valve body is generated. According to the depth image information and distance between the camera and the valve body is obtained by using the K-Means++ clustering algorithm, then the pose of robot grasping the valve body are estimated. Finally by using the hand-eye calibration method, it obtains the mapping matrix between the valve body coordinate system and the camera coordinate system. Experiments are carried out rapid self-identification detection, positioning and grasping for the butterfly valve body being arbitrarily placed, they prove that the strategies effectively avoided the influence of uncertain factors such as the position and posture of the butterfly valve body in the actual working environment, and the grasping accuracy is high. It provides a reference for the efficient and automated sorting of components in automated production lines. © 2024 IEEE.

关键词： butterfly valve body deep learning K-Means++ clustering algorithm pose estimation robot

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A Color Event-Based Camera Emulator for robot vision 4th

A Color Event-Based Camera Emulator for Robot Vision

引用

4th International conference on robotics, computer vision and Intelligent Systems (ROBOVIS)

作者： Bugueno-Cordova, Ignacio Campusano, Miguel Guaman-Rivera, Robert Verschae, Rodrigo Univ OHiggins Inst Engn Sci Rancagua Chile Univ Southern Denmark Odense Denmark

ISBN: (纸本)9783031590566;9783031590573

Event-based cameras are becoming increasingly popular due to their asynchronous spatial-temporal information, high temporal resolution, power efficiency, and high dynamic range advantages. Despite these benefits, the adoption of these sensors has been hindered, mainly due to their high cost. While prices are decreasing and commercial options exist, researchers and developers face barriers to addressing the potential of event-based vision, especially with more specialized models. Although accurate event-based simulators and emulators exist, their primary limitation lies in their inability to operate in real-time and in that they are designed only for grey-scale video streams. this limitation creates a gap between theoretical exploration and practical application, hindering the seamless integration of event-based systems into real-world applications, especially in robotics. Moreover, the importance of color information is well recognized for many tasks, and most existing event-based cameras do not handle color information, except for a few exceptions. To address this challenge, we propose a ROS-based color event camera emulator to aid in reducing the gap between the real-world applicability of event-based color cameras by presenting its software design and implementation. Finally, we present a preliminary evaluation to demonstrate its performance.

关键词： Event-Based camera Color vision Asynchronous sensors robot vision Emulator robot Operating System (ROS)

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Region Prediction for Efficient robot Localization on Large Maps 1

引用

4th International conference on robotics, computer vision and Intelligent Systems (ROBOVIS)

作者： Scucchia, Matteo Maltoni, Davide Univ Bologna DISI I-47521 Cesena Italy

ISBN: (数字)9783031590573

ISBN: (纸本)9783031590566;9783031590573

Recognizing already explored places (a.k.a. place recognition) is a fundamental task in Simultaneous Localization and Mapping (SLAM) to enable robot relocalization and loop closure detection. In topological SLAM the recognition takes place by comparing a signature (or feature vector) associated to the current node with the signatures of the nodes in the known map. However, as the number of nodes increases, matching the current node signature against all the existing ones becomes inefficient and thwarts real-time navigation. In this paper we propose a novel approach to pre-select a subset of map nodes for place recognition. the map nodes are clustered during exploration and each cluster is associated with a region. the region labels become the prediction targets of a deep neural network and, during navigation, only the nodes associated with the regions predicted with high probability are considered for matching. While the proposed technique can be integrated in different SLAM approaches, in this work we describe an effective integration with RTAB-Map (a popular framework for real-time topological SLAM) which allowed us to design and run several experiments to demonstrate its effectiveness.

关键词： SLAM Loop closure detection Localization Deep learning robotic vision

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robot vision and Deep Learning for Automated Planogram Compliance in Retail 1

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4th International conference on robotics, computer vision and Intelligent Systems (ROBOVIS)

作者： Merabet, Adel Latha, Abhishek V. Kuzhippallil, Francis A. Rahimipour, Mohammad Rhinelander, Jason Venkat, Ramesh St Marys Univ Div Engn Halifax NS B3N 3C3 Canada St Marys Univ Comp & Data Analyt Halifax NS B3N 3C3 Canada St Marys Univ Sobey Sch Business Halifax NS B3N 3C3 Canada

ISBN: (数字)9783031590573

ISBN: (纸本)9783031590566;9783031590573

In this paper, automated planogram compliance technique is proposed for retail applications. A mobile robot with camera vision capabilities provides the images of the products on shelves, which are processed to reconstruct an overall image of the shelves to be compared to the planogram. the image reconstruction includes image frames extraction from live video stream, images stitching and concatenation. Object detection, for the products, is achieved using a deep learning tool based on YOLOv5 model. Dataset, for algorithm training and testing, is built to identify the products based on their image identification, number, and location on the shelf. A small scale of shelves with products is built and different cases of products on shelves are tested in a laboratory environment. It was found that YOLOv5 algorithm detects various products on shelves with a precision of 0.98, recall of 0.99, F-measure of 0.98, and clarification loss of 0.006.

关键词： robot Image Deep learning Object detection Planogram Retail

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