Development of robotics gripper with variable stiffness joints via numerical modeling and machine learning

Soft grippers in automation, especially bionic finger grippers, hold promise for precise manipulation. However, their complexity makes predicting finger joint bending angles challenging. This dissertation focuses on bionic finger grippers, aiming to improve angle prediction accuracy in light of thes...

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Main Author: Li, Zhengchen
Other Authors: Yeong Wai Yee
Format: Thesis-Master by Coursework
Language:English
Published: Nanyang Technological University 2023
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Online Access:https://hdl.handle.net/10356/171245
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Institution: Nanyang Technological University
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spelling sg-ntu-dr.10356-1712452023-10-21T16:51:38Z Development of robotics gripper with variable stiffness joints via numerical modeling and machine learning Li, Zhengchen Yeong Wai Yee School of Mechanical and Aerospace Engineering WYYeong@ntu.edu.sg Engineering::Mechanical engineering Soft grippers in automation, especially bionic finger grippers, hold promise for precise manipulation. However, their complexity makes predicting finger joint bending angles challenging. This dissertation focuses on bionic finger grippers, aiming to improve angle prediction accuracy in light of these challenges. The choice of a gripper design with three segments and two knuckles was deliberate, as it emulates the complex structure of bionic finger grippers, making it an ideal testbed for investigating the intricate challenges associated with predicting finger joint bending angles. This design mirrors real-world applications and offers valuable insights into addressing the complexities of soft grippers in automation. This dissertation investigates soft grippers in automation, specifically bionic finger grippers, and the need for an accurate angle prediction model to achieve precise control of finger joint bending angles. The gripper designed in this study uses polymer 3D printing with TPU and PLA materials and is composed of three segments with two knuckles. ANSYS is used to simulate the bending shape of the gripper when pulled to measure bending angles for different material properties. A regression model is built using deep learning techniques to predict temperature, pull distance, and knuckle angle, with the K-neighbour regression model yielding the best results. The model is validated with an average error of no more than 15%. Master of Science (Mechanical Engineering) 2023-10-18T01:29:49Z 2023-10-18T01:29:49Z 2023 Thesis-Master by Coursework Li, Z. (2023). Development of robotics gripper with variable stiffness joints via numerical modeling and machine learning. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/171245 https://hdl.handle.net/10356/171245 en application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Mechanical engineering
spellingShingle Engineering::Mechanical engineering
Li, Zhengchen
Development of robotics gripper with variable stiffness joints via numerical modeling and machine learning
description Soft grippers in automation, especially bionic finger grippers, hold promise for precise manipulation. However, their complexity makes predicting finger joint bending angles challenging. This dissertation focuses on bionic finger grippers, aiming to improve angle prediction accuracy in light of these challenges. The choice of a gripper design with three segments and two knuckles was deliberate, as it emulates the complex structure of bionic finger grippers, making it an ideal testbed for investigating the intricate challenges associated with predicting finger joint bending angles. This design mirrors real-world applications and offers valuable insights into addressing the complexities of soft grippers in automation. This dissertation investigates soft grippers in automation, specifically bionic finger grippers, and the need for an accurate angle prediction model to achieve precise control of finger joint bending angles. The gripper designed in this study uses polymer 3D printing with TPU and PLA materials and is composed of three segments with two knuckles. ANSYS is used to simulate the bending shape of the gripper when pulled to measure bending angles for different material properties. A regression model is built using deep learning techniques to predict temperature, pull distance, and knuckle angle, with the K-neighbour regression model yielding the best results. The model is validated with an average error of no more than 15%.
author2 Yeong Wai Yee
author_facet Yeong Wai Yee
Li, Zhengchen
format Thesis-Master by Coursework
author Li, Zhengchen
author_sort Li, Zhengchen
title Development of robotics gripper with variable stiffness joints via numerical modeling and machine learning
title_short Development of robotics gripper with variable stiffness joints via numerical modeling and machine learning
title_full Development of robotics gripper with variable stiffness joints via numerical modeling and machine learning
title_fullStr Development of robotics gripper with variable stiffness joints via numerical modeling and machine learning
title_full_unstemmed Development of robotics gripper with variable stiffness joints via numerical modeling and machine learning
title_sort development of robotics gripper with variable stiffness joints via numerical modeling and machine learning
publisher Nanyang Technological University
publishDate 2023
url https://hdl.handle.net/10356/171245
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