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vision-Based Object Sorting in Dynamic Environments using YOLO for RoboCup ARM Challenge 2023 7

Vision-Based Object Sorting in Dynamic Environments using YO...

引用

7th IEEE conference on Information and Communication Technology, CICT 2023

作者： Sahu, Yash Kumar Mittal, Radhika Patel, Deep Maiti, Chayan Sreekumar, M. Indian Institute of Information Technology Design and Manufacturing Centre for AI IoT and Robotics Department of Mechanical Engineering Kancheepuram Chennai India Indian Institute of Information Technology Design and Manufacturing Centre for AI IoT and Robotics Department of Computer Science Kancheepuram Chennai India

ISBN: (纸本)9798350305173

In the intersection of Artificial Intelligence (AI) and robotics, autonomous object manipulation has emerged as a pivotal research domain, finding applications in sectors as diverse as manufacturing and healthcare. While significant strides have been made in object identification, trajectory planning, and manipulation tasks, these advancements often fall short in real-world, dynamic settings, revealing a critical research gap in algorithmic adaptability and computational efficiency. this paper addresses the gap by applying YOLOv4, a state-of-the-art object detection AI algorithm, within the context of the RoboCup Autonomous robot Manipulation (ARM) Challenge - a competitive platform designed to simulate real-world complexities in autonomous systems. the research objectives are twofold: firstly, the performance of YOLOv4 in object detection and classification under dynamically changing conditions is rigorously evaluated, employing varied training strategies in both simulated and real-world scenarios. Secondly, the robustness and adaptability of the algorithm in object manipulation tasks are scrutinized and facilitated by the real-world dynamics inherent to the competition. Empirical results demonstrate the algorithm's high accuracy, efficiency, and robustness in dynamic environments, substantiating its broader applicability in the development of autonomous systems. © 2023 IEEE.

关键词： Object detection

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

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

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Power Electronics, computer Applications (ICPECA), IEEE International conference on

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

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.

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A preliminary system for recognizing boredom

A preliminary system for recognizing boredom

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ACM/IEEE International conference on Human-robot Interaction (HRI)

作者： Allison M. Jacobs Frederick W. P. Heckel Benjamin Fransen Alan R. Wagner J. Malcolm McCurry J. Gregory Trafton George Mason University Fairfax VA USA Naval Research Laboratory Washington DC DC USA University of North Carolina at Charlotte Charlotte NC USA Georgia Institute of Technology Atlanta GA USA

A 3D optical flow tracking system was used to track participants as they watched a series of boring videos. the video stream of the participants was rated for boredom events. Ratings and head position data were combin... 详细信息

关键词： Videos Head Educational institutions Magnetic heads Cameras Image motion analysis computer vision

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ImageCLEF 2013: the vision, the data and the open challenges

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4th International conference of the CLEF Initiative, CLEF 2013

作者： Caputo, Barbara Muller, Henning thomee, Bart Villegas, Mauricio Paredes, Roberto Zellhofer, David Goeau, Herve Joly, Alexis Bonnet, Pierre Martinez Gomez, Jesus Varea, Ismael Garcia Cazorla, Miguel Idiap Research Institue Martigny Switzerland University of Applied Sciences Western Switzerland in Sierre Switzerland Yahoo Research Barcelona Spain ITI/DSIC Universitat Politècnica de València Spain Brandenburg University of Technology Germany INRIA-IMEDIA Paris France INRIA-ZENITH Montpellier France CIRAD UMR AMAP Montpellier France University of Castilla-La Mancha Albacete Spain University of Alicante Alicante Spain

ISBN: (纸本)9783642408014

this paper presents an overview of the ImageCLEF 2013 lab. Since its first edition in 2003, ImageCLEF has become one of the key initiatives promoting the benchmark evaluation of algorithms for the cross-language annotation and retrieval of images in various domains, such as public and personal images, to data acquired by mobile robot platforms and botanic collections. Over the years, by providing new data collections and challenging tasks to the community of interest, the ImageCLEF lab has achieved an unique position in the multi lingual image annotation and retrieval research landscape. the 2013 edition consisted of three tasks: the photo annotation and retrieval task, the plant identification task and the robot vision task. Furthermore, the medical annotation task, that traditionally has been under the ImageCLEF umbrella and that this year celebrates its tenth anniversary, has been organized in conjunction with AMIA for the first time. the paper describes the tasks and the 2013 competition, giving an unifying perspective of the present activities of the lab while discussion the future challenges and opportunities. © 2013 Springer-Verlag.

关键词： Plants (botany)

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Research on precise positioning technology on the tower of high-voltage transmission line with live working robot

Research on precise positioning technology on the tower of h...

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International conference on computer Engineering and Applications (ICCEA)

作者： Li Huang Boyu Xu Wenbo Pan Qingqi Wu Yang Zou Guangdong Power Grid Co. Ltd. Meizhou Power Supply Bureau Guangdong China

the identification and positioning of obstacles on the operating lines of the live working robot is one of the key technologies to complete the operation task. In order to perform localization and ranging of obstacles on power lines, a binocular vision approach was used. In this paper, a binocular camera was used and camera calibration was performed first. the aberration correction and stereo correction of the binocular camera views were performed by the acquired camera internal reference matrix and the left and right camera positional matrices. then, the parallax map of the binocular image is obtained using the semi-global matching algorithm SGM (Semi-Global Matching), and the parallax map is smoothed using the weighted least squares filtering algorithm. Finally, the depth information of the obstacle is obtained by reprojecting the parallax map. the effectiveness and accuracy of the proposed localization and ranging method is verified by experiments on the obstacles of power lines.

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Warehouse Automation using Line Follower robot

Warehouse Automation using Line Follower Robot

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Global conference for Advancement in Technology (GCAT)

作者： Ayush Sharma Soumya Gopal Kulhalli Department of AI-ML BMSCE Bengaluru India Department of ECE BMSCE Bengaluru India

this paper emphasises how the warehouse environment can be automated with the help of robots, specifically, line following robots. the robots aim to minimise manual labor work and make the overall process fast and efficient. the proposed system ensures autonomous transportation of the materials and components within the warehouse. this is achieved using efficient computing techniques and path planning. the robot is given a predefined path between the intended source and destination, which is calculated using a path planning algorithm. the algorithm is applied to the pictorial map of the warehouse using computer vision techniques and the operator provides the source and destination. Using the on-board sensors, it navigates its way through the warehouse to deliver the goods. the Line Follower robot in focus here is primarily used in industrial setups where pick and place mechanisms are present.

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Study on vision Measurement for Levitation Gap of Magnetic Levitation Ball Based on Convolutional Neural Network

Study on Vision Measurement for Levitation Gap of Magnetic L...

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International conference on Image, vision and Computing (ICIVC)

作者： Jie Kong Yongzhi Jing Chenhao Zhang Jianhua Hao Cheng Qian Qianwen Gong Key Laboratory of Magnetic Suspension Technology and Maglev Vehicle Ministry of Education Chengdu China

ISBN: (纸本)9781728123264

With the development of deep learning, the Convolutional Neural Network (CNN) is widely used in object classification and pattern recognition. It has enabled computer to achieve better performance than humans in specialized computer vision tasks. this paper takes the magnetic levitation ball system as the research object. Aiming at the shortcomings of traditional methods for levitation gap measurement, a new method is proposed by combining machine vision and CNN image processing technology. the convolution neural network algorithm is used to build the gap measurement model, and the training set is used to train the model. the experimental results show that using convolution neural network image processing technology to realize the levitation gap measurement of magnetic levitation ball system has high distance measurement accuracy and good performance. the proposed CNN model provides correct gap data with the maximum error of 0.16mm for full scale and the average error of 0.07mm for full scale in the test set.

关键词： Magnetic levitation ball Convolutional neural network Image processing Gap measurement Image processing Magnetic levitation FULL-SCALE levitation maximum error robot vision Neural network Visual inspection Ball Device Component images computer vision

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Dual Arm Rubik’s Cube Solver robot

Dual Arm Rubik’s Cube Solver Robot

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Science and Technology Innovation (IAECST), International Academic Exchange conference on

作者： Chenglong Song Colorado State University Fort Collins Colorado

robotics is known as the "crown jewel of the manufacturing industry" and is an important symbol of a country's innovation capability and industrial competitiveness. At the same time, with the continuous improvement of people's material living standards, people's pursuit of spiritual life is also At the same time, with the continuous improvement of people's material living standard, people's pursuit of spiritual life has become more intense. In this thesis, we have chosen "the algorithm implementation of a two-armed Rubik's Cube solving robot" as the topic of choice, and have focused on the optimisation of the relevant algorithm and the development of the robot. this thesis is aimed at the frontiers of robotics and the needs of the service robot industry. this thesis is aimed at the frontiers of robotics and the needs of the service robotics industry. Since its invention in 1974, the Rubik's Cube has challenged users to solve a colorful puzzle in record time. the puzzle can be solved at a higher speed by building a robot. this paper deals with the development of a dual-arm Rubik's cube solver robot, which implements computer vision, artificial intelligence in motor drive design, and an intelligent control system based on a single chip. After running performance tests, it can solve the 3x3 Rubik's cube in any order within 7 seconds.

关键词： Technological innovation Service robots Machine vision Stability criteria Symbols Pneumatic systems Manipulators

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altURI-a thin Middleware for Simulated robot vision Applications

altURI-a Thin Middleware for Simulated Robot Vision Applicat...

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2011 4th IEEE International conference on computer Science and Information Technology(ICCSIT 2011)

作者： Mark N R Smith Marwan Shaker Tom Duckett Centre for Vision and Robotics Research University of Lincoln Lincoln UK

Fast software performance is often the focus when developing real-time vision-based control applications for robot *** this paper we have developed a thin,high performance middleware for USARSim and other simulators designed for real-time vision-based control *** includes a fast image server providing images in OpenCV,Matlab or web formats and a simple command/sensor *** interface has been tested in USARSim with an Unmanned Aerial Vehicle using two control applications;landing using a reinforcement learning algorithm and altitude control using elementary motion *** middleware has been found to be fast enough to control the flying robot as well as very easy to set up and use.

关键词： middleware robot vision USARSim

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Development and Application of robot Automatic Inspection System Technology Based on Machine Learning

Development and Application of Robot Automatic Inspection Sy...

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IEEE International conference on Automation, Electronics and Electrical Engineering (AUTEEE)

作者： Zhimin Zhang Wen Yang Xinfang Ding Hailin Wan Yiping Yang Methanol Production from Coal Branch of Gansu Huating Coal Electricty Co. LTD Pingliang Gansu China

ISBN: (纸本)9781665428408

With the development of artificial intelligence technology, computer technology and other related technologies, there are more and more researches on mobile robots. the robot has gone through three stages: from a simple teaching and reproducing robot to a low-level intelligent robot with vision, hearing, touch and certain adaptability, and then to a high-level intelligent robot with various advanced sensors and self-adaptive and self-learning functions. Intelligent voice inspection robot can be effectively used in petrochemical, oilfield, nuclear industry and other industries, especially in areas where people are inconvenient to reach, such as pipelines, tanks and towers. the robot is not taught what actions to take, but it evaluates the current state through a value function to select actions to maximize the cumulative reward in the long-term goal. this paper focuses on the design and implementation of intelligent voice inspection robot system, hoping to play a certain reference role for the further improvement and development of intelligent voice inspection robot system in China.

关键词： Industries Service robots Poles and towers Tactile sensors Speech recognition Inspection Security

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