Robotics multi-modal recognition system via computer-based vision

被引：0
|
作者：
Shahin, Mohammad [1 ]
Chen, F. Frank [1 ]
Hosseinzadeh, Ali [1 ]
Bouzary, Hamed [1 ]
Shahin, Awni [2 ]
机构：
[1] Mechanical Engineering Department, The University of Texas at San Antonio, San Antonio, United States
[2] Faculty of Education, Mu’tah University, Karak, Jordan
来源：
International Journal of Advanced Manufacturing Technology | 2024年
关键词：
This paper presents a multi-modal recognition system (MMR) which can eliminates the need for using Barcodes and Radio Frequency Identification Systems (RFIDs). Barcodes and RFID have limitations; for example, Barcodes require the scanner to have a direct line of sight with the code, and they are more susceptible to errors. Barcodes can also be hard to locate and can be affixed to oddly shaped products. While, RFID may overcome such problems, but it could be disturbed if the RFID is attached to a metallic background. The proposed MMR system can monitor items flowing one by one down on a conveyor belt to make sure they match their images, thus enabling robotics identification for items while picking them up, sorting them or turning them into a desirable orientation. Recognizing the business landscape evolves and competition from low-cost nations grows, new models must be created that provide a competitive edge by combining the Lean paradigm with Industry 4.0 technical advancements. This paper reports a contribution to this field by assessing the supporting function of MMR state-of-the-art algorithms in Lean manufacturing. In addition, this paper also aims to explore how MMR could be integrated into the Lean manufacturing settings to enable a competitive manufacturing process in a Lean 4.0 environment. A dataset with 21,000 vegetable images of 15 classes was used to present the recent development and application of image analysis and computer vision systems in object recognition, showing an overall detection F1-score of 85.08%. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024;
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