[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122748-en":3,"doc-seo-122748-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122748,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",6,"Technology","Developing an Object Detection and Gripping Mechanism Algorithm using Machine Learning","Localizing and recognizing objects are critical for indoor manipulation tasks. The paper presents a machine-vision and machine-learning algorithm that performs object detection and robotic gripping using a Kinect v1 camera-depth setup. YOLO-based computer vision localizes objects in 3D by estimating spatial coordinates from the sensor’s point cloud, attached to a robotic arm base for compact stationary deployment. Reported results achieve about 5 mm localization error and over 70% confidence in correct detection, enabling sorting and grasping in industrial, agricultural, kitchen, and mobile-robot indoor services.","JASAE  \nJournal of Applied Science and Advanced Engineering  \nVol. 1 No. 2 (2023)  \ne-ISSN: 2985-7252  \n[https://jasae.org/](https://jasae.org/)  \nDeveloping an Object Detection and Gripping Mechanism Algorithm using Machine Learning  \nHazem Mohammad1*, Sameer Kishore2, and Judhi Prasetyo3  \n1,2,3Middlesex University, Dubai, United Arab Emirates  \n*email: [hh777@live.mdx.ac.uk](hh777@live.mdx.ac.uk), [Hazem7mohammad@gmail.com](Hazem7mohammad@gmail.com)  \nARTICLE HISTORY  \nReceived: 24 August 2023  \nRevised: 15 September 2023  \nAccepted: 20 September 2023 KEYWORDS  \nObject detection Localizing YOLO Gripping Robot  \n© Hazem Mohammad et al.  \nABSTRACT– Localizing and recognition of objects are critical problems for indoor manipulation tasks. This paper describes an algorithm based on computer vision and machine learning that does object detection and gripping tasks. Detection of objects is carried out using a combination of a camera and depth sensor using a Kinect v1 depth sensor. Moreover, machine learning algorithms (YOLO) are used for computer vision. The project presents a method that allows the Kinect sensor to detect objects' 3D location. At the same time, it is attached to any robotic arm base, allowing fora more versatile and compact solution to be used in stationary places using industrial robot arms or mobile robots. The results show an error of locating an object to be 5 mm. and more than 70% confidence in detecting objects correctly. There are many possibilities in which this project can be used, such as in industrial fields, to sort, load, and unload different kinds of objects based on their type, size, and shape. In agriculture fields, to collect or sort different kinds of fruits, in kitchens and cafes where sorting objects like cups, bottles, and cans can occur. Also, this project can be added to mobile robots to do indoor human services or collect trash from different places.  \nThis is an Open Access article distributed under the terms of the Creative Commons CC-BY-4.0 License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.  \nIntroduction  \nA. Background  \nSome early scholars used manual grasping systems for object grasping, having the robot go to a predefined location to pick an object and then to another predefined location to place the object; these methods are straightforward, simple, and suitable for fixed scene operation tasks. However, external factors can easily affect these systems or methods because they do not visually detect the object's color, size, or shape, and they only follow preprogrammed coordinates.  \nMachine vision has changed the game for industrial robots. The demand for intelligent autonomous robots has increased, and robots that can be used in complex scenes that are not easily affected by external factors are very much needed.  \nFor object-grabbing robots, there are three steps to get the task done: (A) detecting the object, (B) estimating the coordinates of the object, and (C) grabbing the object. The ability to manipulate and grasp a desired object for picking or placing is a challenging problem, and it requires an accurate calculation of the object's position relative to the arm and an accurate kinematics model to achieve the task precisely.  \nThe problem for visually guided robots is that a camera detects the objects' location, shape, and dimensions, as shown in Figure 1. In Figure 1, the size and the distance of the object are entirely dependent on the area calculations of the objects being viewed from the top as a 2D image.  \nFigure 1. Top view of workspace showing object's 2D location  \nThe position of the camera sensor is crucial in such robots. Having the camera attached to a fixed top view of the workspace area separated from the robotic arm, like in Figure (2), can limit the flexibility as well as the accuracy of the robot because of the need for having a specific fixed setup or workspace for the robot","cbCaijqRKnTA7DgR","https://ap.wps.com/l/cbCaijqRKnTA7DgR","pdf",1020053,1,8,"English","en",105,"# Introduction\n## Background","[{\"question\":\"What is the main objective of the proposed work?\",\"answer\":\"To develop an algorithm that uses computer vision and machine learning to detect objects and support gripping by estimating each object’s 3D location for manipulation tasks.\"},{\"question\":\"How does the system perform object detection and localization?\",\"answer\":\"It combines a camera with a Kinect v1 depth sensor to produce point clouds, then applies YOLO for computer vision to detect objects and estimate their 3D coordinates.\"},{\"question\":\"What performance results are reported for locating objects?\",\"answer\":\"The results indicate an object localization error of about 5 mm or more and more than 70% confidence in detecting objects correctly.\"}]","Developing an Object Detection and Gripping Mechanism Algorithm using Machine Learning | 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is the main objective of the proposed work?","Question",{"text":75,"@type":76},"To develop an algorithm that uses computer vision and machine learning to detect objects and support gripping by estimating each object’s 3D location for manipulation tasks.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the system perform object detection and localization?",{"text":80,"@type":76},"It combines a camera with a Kinect v1 depth sensor to produce point clouds, then applies YOLO for computer vision to detect objects and estimate their 3D coordinates.",{"name":82,"@type":73,"acceptedAnswer":83},"What performance results are reported for locating objects?",{"text":84,"@type":76},"The results indicate an object localization error of about 5 mm or more and more than 70% confidence in detecting objects 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