End-to-End Deep Learning by MCU Implementation: An Intelligent Gripper for Shape Identification
This paper introduces a real-time processing and classification of raw sensor data using a convolutional neural network (CNN). The established system is a microcontroller-unit (MCU) implementation of an intelligent gripper for shape identification of grasped objects. The pneumatic gripper has two em...
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Published in | Sensors (Basel, Switzerland) Vol. 21; no. 3; p. 891 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
Switzerland
MDPI
28.01.2021
MDPI AG |
Subjects | |
Online Access | Get full text |
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Summary: | This paper introduces a real-time processing and classification of raw sensor data using a convolutional neural network (CNN). The established system is a microcontroller-unit (MCU) implementation of an intelligent gripper for shape identification of grasped objects. The pneumatic gripper has two embedded accelerometers to sense acceleration (in the form of vibration signals) on the jaws for identification. The raw data is firstly transferred into images by short-time Fourier transform (STFT), and then the CNN algorithm is adopted to extract features for classifying objects. In addition, the hyperparameters of the CNN are optimized to ensure hardware implementation. Finally, the proposed artificial intelligent model is implemented on a MCU (Renesas RX65N) from raw data to classification. Experimental results and discussions are introduced to show the performance and effectiveness of our proposed approach. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Senior Member IEEE. |
ISSN: | 1424-8220 1424-8220 |
DOI: | 10.3390/s21030891 |