Improving robotic grasping system using deep learning approach

Traditional robots can only move according to a pre-planned trajectory which limits the range of applications that they could be engaged in. Despite their long history, the use of computer vision technology for grasp prediction and object detection is still an active research area. However, the gene...

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Main Author:	Mohannad K. H., Farag
Format:	Thesis
Language:	English
Published:	2020
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://umpir.ump.edu.my/id/eprint/34100/1/Improving%20robotic%20grasping%20system%20using%20deep.pdf
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spelling	my-ump-ir.341002022-05-11T08:55:57Z Improving robotic grasping system using deep learning approach 2020-08 Mohannad K. H., Farag TK Electrical engineering. Electronics Nuclear engineering Traditional robots can only move according to a pre-planned trajectory which limits the range of applications that they could be engaged in. Despite their long history, the use of computer vision technology for grasp prediction and object detection is still an active research area. However, the generating of a full grasp configuration of a target object is the main challenge to plan a successful robotic operation of the physical robotic grasp. Integrating computer vision technology with tactile sensing feedback has given rise to a new capability of robots that can accomplish various robotic tasks. However, the recently conducted studies had used tactile sensing with grasp detection models to improve prediction accuracy, not physical grasp success. Thus, the problem of detecting the slip event of the grasped objects that have different weights is addressed in this research. This research aimed to develop a Deep Learning grasp detection model and a slip detection algorithm and integrating them into one innovative robotic grasping system. By proposing a four-step data augmentation technique, the achieved grasping accuracy was 98.2 % exceeding the best-reported results by almost 0.5 % where 625 new instances were generated per original image with different grasp labels. Besides, using the twostage- transfer-learning technique improved the obtained results in the second stage by 0.3 % compared to the first stage results. For the physical robot grasp, the proposed sevendimensional grasp representations method allows the autonomous prediction of the grasp size and depth. The developed model achieved 74.8 milliseconds as prediction time, which makes it possible to use the model in real-time robotic applications. By observing the real-time feedback of a force sensing resistor sensor, the proposed slip detection algorithm indicated a quick response within 86 milliseconds. These results allowed the system to maintain holding the target objects by an immediate increase of the grasping force. The integration of the Deep Learning and slip detection models has shown a significant improvement of 18.4% in the results of the experimental grasps conducted on a SCARA robot. Besides, the utilized Zerocross-Canny edge detector has improved the robot positioning error by 0.27 mm compared to the related studies. The achieved results introduced an innovative robotic grasping system with a Grasp-NoDrop-Place scheme. 2020-08 Thesis http://umpir.ump.edu.my/id/eprint/34100/ http://umpir.ump.edu.my/id/eprint/34100/1/Improving%20robotic%20grasping%20system%20using%20deep.pdf pdf en public phd doctoral Universiti Malaysia Pahang Faculty of Electrical & Electronics Engineering
institution	Universiti Malaysia Pahang Al-Sultan Abdullah
collection	UMPSA Institutional Repository
language	English
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Mohannad K. H., Farag Improving robotic grasping system using deep learning approach
description	Traditional robots can only move according to a pre-planned trajectory which limits the range of applications that they could be engaged in. Despite their long history, the use of computer vision technology for grasp prediction and object detection is still an active research area. However, the generating of a full grasp configuration of a target object is the main challenge to plan a successful robotic operation of the physical robotic grasp. Integrating computer vision technology with tactile sensing feedback has given rise to a new capability of robots that can accomplish various robotic tasks. However, the recently conducted studies had used tactile sensing with grasp detection models to improve prediction accuracy, not physical grasp success. Thus, the problem of detecting the slip event of the grasped objects that have different weights is addressed in this research. This research aimed to develop a Deep Learning grasp detection model and a slip detection algorithm and integrating them into one innovative robotic grasping system. By proposing a four-step data augmentation technique, the achieved grasping accuracy was 98.2 % exceeding the best-reported results by almost 0.5 % where 625 new instances were generated per original image with different grasp labels. Besides, using the twostage- transfer-learning technique improved the obtained results in the second stage by 0.3 % compared to the first stage results. For the physical robot grasp, the proposed sevendimensional grasp representations method allows the autonomous prediction of the grasp size and depth. The developed model achieved 74.8 milliseconds as prediction time, which makes it possible to use the model in real-time robotic applications. By observing the real-time feedback of a force sensing resistor sensor, the proposed slip detection algorithm indicated a quick response within 86 milliseconds. These results allowed the system to maintain holding the target objects by an immediate increase of the grasping force. The integration of the Deep Learning and slip detection models has shown a significant improvement of 18.4% in the results of the experimental grasps conducted on a SCARA robot. Besides, the utilized Zerocross-Canny edge detector has improved the robot positioning error by 0.27 mm compared to the related studies. The achieved results introduced an innovative robotic grasping system with a Grasp-NoDrop-Place scheme.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Mohannad K. H., Farag
author_facet	Mohannad K. H., Farag
author_sort	Mohannad K. H., Farag
title	Improving robotic grasping system using deep learning approach
title_short	Improving robotic grasping system using deep learning approach
title_full	Improving robotic grasping system using deep learning approach
title_fullStr	Improving robotic grasping system using deep learning approach
title_full_unstemmed	Improving robotic grasping system using deep learning approach
title_sort	improving robotic grasping system using deep learning approach
granting_institution	Universiti Malaysia Pahang
granting_department	Faculty of Electrical & Electronics Engineering
publishDate	2020
url	http://umpir.ump.edu.my/id/eprint/34100/1/Improving%20robotic%20grasping%20system%20using%20deep.pdf
_version_	1783732176597024768

Improving robotic grasping system using deep learning approach

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