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• W4322579551 abstract "This paper uses deep learning to define the grasping points of unknown objects and proposes an approach to implement for grasping and possible correct picking and placing of objects to offer optimal performance and results in generic and cluttered environments. The suggested method detects and recognizes objects, classifies them under their appropriate class and defines the position of the object in the work space, and draws a bounding box around the detected object, and marks the optimal grasping points. Existing grasping methods and algorithms were studied, a deep learning model for object detection was developed, an algorithm that returns the position and orientation of the object was developed, and the selected method and algorithm are tested and validated. A method for classifying and detecting the position and orientation of unknown objects that are located randomly, based on image processing and deep learning methods is suggested. The reliability and validity of the effectiveness of the proposed method is proved by experimental tests. The contribution of this work is to develop a method that allows robots to perform precise autonomous selection and placement tasks for specific types of objects that require task-oriented grasping." @default.
• W4322579551 created "2023-02-28" @default.
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• W4322579551 date "2023-01-01" @default.
• W4322579551 modified "2023-09-30" @default.
• W4322579551 title "Exploiting deep learning methods for object recognition and grasping tasks" @default.
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• W4322579551 doi "https://doi.org/10.1063/5.0112973" @default.
• W4322579551 hasPublicationYear "2023" @default.
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