Development and Analysis of an Origami-Based Elastomeric Actuator and Soft Gripper Control with Machine Learning and EMG Sensors
This study investigates the characteristics of a novel origami-based, elastomeric actuator and a soft gripper, which are controlled by hand gestures that are recognized through machine learning algorithms. The lightweight paper–elastomer structure employed in this research exhibits distinct actuatio...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-03-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/24/6/1751 |
| _version_ | 1797239385911984128 |
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| author | Meixin Wang Wonhyong Lee Liqi Shu Yong Sin Kim Chung Hyuk Park |
| author_facet | Meixin Wang Wonhyong Lee Liqi Shu Yong Sin Kim Chung Hyuk Park |
| author_sort | Meixin Wang |
| collection | DOAJ |
| description | This study investigates the characteristics of a novel origami-based, elastomeric actuator and a soft gripper, which are controlled by hand gestures that are recognized through machine learning algorithms. The lightweight paper–elastomer structure employed in this research exhibits distinct actuation features in four key areas: (1) It requires approximately 20% less pressure for the same bending amplitude compared to pneumatic network actuators (Pneu-Net) of equivalent weight, and even less pressure compared to other actuators with non-linear bending behavior; (2) The control of the device is examined by validating the relationship between pressure and the bending angle, as well as the interaction force and pressure at a fixed bending angle; (3) A soft robotic gripper comprising three actuators is designed. Enveloping and pinch grasping experiments are conducted on various shapes, which demonstrate the gripper’s potential in handling a wide range of objects for numerous applications; and (4) A gesture recognition algorithm is developed to control the gripper using electromyogram (EMG) signals from the user’s muscles. |
| first_indexed | 2024-04-24T17:50:42Z |
| format | Article |
| id | doaj.art-8be4683218564d3cbc791b041fadde46 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-24T17:50:42Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-8be4683218564d3cbc791b041fadde462024-03-27T14:03:40ZengMDPI AGSensors1424-82202024-03-01246175110.3390/s24061751Development and Analysis of an Origami-Based Elastomeric Actuator and Soft Gripper Control with Machine Learning and EMG SensorsMeixin Wang0Wonhyong Lee1Liqi Shu2Yong Sin Kim3Chung Hyuk Park4Department of Biomedical Engineering, School of Engineering and Applied Science, George Washington University, Washington, DC 20052, USASchool of Computer Science and Electrical Engineering, Handong Global University, Pohang 37554, Republic of KoreaDepartment of Neurology, Brown University, Providence, RI 02903, USASchool of Electrical Engineering, Korea University, Seoul 02841, Republic of KoreaDepartment of Biomedical Engineering, School of Engineering and Applied Science, George Washington University, Washington, DC 20052, USAThis study investigates the characteristics of a novel origami-based, elastomeric actuator and a soft gripper, which are controlled by hand gestures that are recognized through machine learning algorithms. The lightweight paper–elastomer structure employed in this research exhibits distinct actuation features in four key areas: (1) It requires approximately 20% less pressure for the same bending amplitude compared to pneumatic network actuators (Pneu-Net) of equivalent weight, and even less pressure compared to other actuators with non-linear bending behavior; (2) The control of the device is examined by validating the relationship between pressure and the bending angle, as well as the interaction force and pressure at a fixed bending angle; (3) A soft robotic gripper comprising three actuators is designed. Enveloping and pinch grasping experiments are conducted on various shapes, which demonstrate the gripper’s potential in handling a wide range of objects for numerous applications; and (4) A gesture recognition algorithm is developed to control the gripper using electromyogram (EMG) signals from the user’s muscles.https://www.mdpi.com/1424-8220/24/6/1751soft manipulationartificial neural networkshighly deformable robotssoft robotorigami-basedgesture recognition |
| spellingShingle | Meixin Wang Wonhyong Lee Liqi Shu Yong Sin Kim Chung Hyuk Park Development and Analysis of an Origami-Based Elastomeric Actuator and Soft Gripper Control with Machine Learning and EMG Sensors Sensors soft manipulation artificial neural networks highly deformable robots soft robot origami-based gesture recognition |
| title | Development and Analysis of an Origami-Based Elastomeric Actuator and Soft Gripper Control with Machine Learning and EMG Sensors |
| title_full | Development and Analysis of an Origami-Based Elastomeric Actuator and Soft Gripper Control with Machine Learning and EMG Sensors |
| title_fullStr | Development and Analysis of an Origami-Based Elastomeric Actuator and Soft Gripper Control with Machine Learning and EMG Sensors |
| title_full_unstemmed | Development and Analysis of an Origami-Based Elastomeric Actuator and Soft Gripper Control with Machine Learning and EMG Sensors |
| title_short | Development and Analysis of an Origami-Based Elastomeric Actuator and Soft Gripper Control with Machine Learning and EMG Sensors |
| title_sort | development and analysis of an origami based elastomeric actuator and soft gripper control with machine learning and emg sensors |
| topic | soft manipulation artificial neural networks highly deformable robots soft robot origami-based gesture recognition |
| url | https://www.mdpi.com/1424-8220/24/6/1751 |
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