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Artificial Intelligence-Based Optimal Grasping Control
A new tactile sensing module was proposed to sense the contact force and location of an object on a robot hand, which was attached on the robot finger. Three air pressure sensors are installed at the tip of the finger to detect the contacting force at the points. To obtain a nominal contact force at...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7664912/ https://www.ncbi.nlm.nih.gov/pubmed/33182402 http://dx.doi.org/10.3390/s20216390 |
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author | Kim, Dongeon Lee, Jonghak Chung, Wan-Young Lee, Jangmyung |
author_facet | Kim, Dongeon Lee, Jonghak Chung, Wan-Young Lee, Jangmyung |
author_sort | Kim, Dongeon |
collection | PubMed |
description | A new tactile sensing module was proposed to sense the contact force and location of an object on a robot hand, which was attached on the robot finger. Three air pressure sensors are installed at the tip of the finger to detect the contacting force at the points. To obtain a nominal contact force at the finger from data from the three air pressure sensors, a force estimation was developed based upon the learning of a deep neural network. The data from the three air pressure sensors were utilized as inputs to estimate the contact force at the finger. In the tactile module, the arrival time of the air pressure sensor data has been utilized to recognize the contact point of the robot finger against an object. Using the three air pressure sensors and arrival time, the finger location can be divided into 3 × 3 block locations. The resolution of the contact point recognition was improved to 6 × 4 block locations on the finger using an artificial neural network. The accuracy and effectiveness of the tactile module were verified using real grasping experiments. With this stable grasping, an optimal grasping force was estimated empirically with fuzzy rules for a given object. |
format | Online Article Text |
id | pubmed-7664912 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-76649122020-11-14 Artificial Intelligence-Based Optimal Grasping Control Kim, Dongeon Lee, Jonghak Chung, Wan-Young Lee, Jangmyung Sensors (Basel) Article A new tactile sensing module was proposed to sense the contact force and location of an object on a robot hand, which was attached on the robot finger. Three air pressure sensors are installed at the tip of the finger to detect the contacting force at the points. To obtain a nominal contact force at the finger from data from the three air pressure sensors, a force estimation was developed based upon the learning of a deep neural network. The data from the three air pressure sensors were utilized as inputs to estimate the contact force at the finger. In the tactile module, the arrival time of the air pressure sensor data has been utilized to recognize the contact point of the robot finger against an object. Using the three air pressure sensors and arrival time, the finger location can be divided into 3 × 3 block locations. The resolution of the contact point recognition was improved to 6 × 4 block locations on the finger using an artificial neural network. The accuracy and effectiveness of the tactile module were verified using real grasping experiments. With this stable grasping, an optimal grasping force was estimated empirically with fuzzy rules for a given object. MDPI 2020-11-09 /pmc/articles/PMC7664912/ /pubmed/33182402 http://dx.doi.org/10.3390/s20216390 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Kim, Dongeon Lee, Jonghak Chung, Wan-Young Lee, Jangmyung Artificial Intelligence-Based Optimal Grasping Control |
title | Artificial Intelligence-Based Optimal Grasping Control |
title_full | Artificial Intelligence-Based Optimal Grasping Control |
title_fullStr | Artificial Intelligence-Based Optimal Grasping Control |
title_full_unstemmed | Artificial Intelligence-Based Optimal Grasping Control |
title_short | Artificial Intelligence-Based Optimal Grasping Control |
title_sort | artificial intelligence-based optimal grasping control |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7664912/ https://www.ncbi.nlm.nih.gov/pubmed/33182402 http://dx.doi.org/10.3390/s20216390 |
work_keys_str_mv | AT kimdongeon artificialintelligencebasedoptimalgraspingcontrol AT leejonghak artificialintelligencebasedoptimalgraspingcontrol AT chungwanyoung artificialintelligencebasedoptimalgraspingcontrol AT leejangmyung artificialintelligencebasedoptimalgraspingcontrol |