Development of gesture control of robotic arm/robotic structures for assistive systems

The development and use of collaborative robots have been on the rise in recent years, enabling humans to perform tasks with precision and ability that would otherwise be humanly impossible. Many of such robotic applications and studies conducted were to simplify or automate work processes and reduc...

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Main Author: Tan, Yi Ming
Other Authors: Tay, Wee Peng
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2021
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Online Access:https://hdl.handle.net/10356/150122
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1501222023-07-07T18:12:50Z Development of gesture control of robotic arm/robotic structures for assistive systems Tan, Yi Ming Tay, Wee Peng School of Electrical and Electronic Engineering Government Technology Agency wptay@ntu.edu.sg Engineering::Electrical and electronic engineering::Computer hardware, software and systems The development and use of collaborative robots have been on the rise in recent years, enabling humans to perform tasks with precision and ability that would otherwise be humanly impossible. Many of such robotic applications and studies conducted were to simplify or automate work processes and reduce the workload of general, able-bodied users. Little research and work were done to investigate the possibilities of new interfaces which allow users with physical limitations to interact and control robotic systems. This project proposes a method for interaction making use of a Leap Motion Controller, an LSTM machine learning model, and a Niryo Ned robotic arm. The Leap Motion Controller, an optical hand tracking module was used to read and return data of hands and fingers detected. An LSTM model was trained to classify and recognize a set of 9 different gestures and the classified gestures are then used to control a robotic arm. The final trained LSTM model was able to achieve an F-score of 92% when evaluated against a test set of 9 gestures. After, it was successfully integrated and implemented with a Niryo Ned robotic arm which was tasked to perform 8 actions. The development of this system would be beneficial especially in the field of assistive technology, giving individuals with physical limitations and disabilities ways to overcome their limitations, enabling them to live independently in this world that is largely catered to the able-bodied majority. Bachelor of Engineering (Electrical and Electronic Engineering) 2021-06-12T07:39:34Z 2021-06-12T07:39:34Z 2021 Final Year Project (FYP) Tan, Y. M. (2021). Development of gesture control of robotic arm/robotic structures for assistive systems. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/150122 https://hdl.handle.net/10356/150122 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::Electrical and electronic engineering::Computer hardware, software and systems
spellingShingle Engineering::Electrical and electronic engineering::Computer hardware, software and systems
Tan, Yi Ming
Development of gesture control of robotic arm/robotic structures for assistive systems
description The development and use of collaborative robots have been on the rise in recent years, enabling humans to perform tasks with precision and ability that would otherwise be humanly impossible. Many of such robotic applications and studies conducted were to simplify or automate work processes and reduce the workload of general, able-bodied users. Little research and work were done to investigate the possibilities of new interfaces which allow users with physical limitations to interact and control robotic systems. This project proposes a method for interaction making use of a Leap Motion Controller, an LSTM machine learning model, and a Niryo Ned robotic arm. The Leap Motion Controller, an optical hand tracking module was used to read and return data of hands and fingers detected. An LSTM model was trained to classify and recognize a set of 9 different gestures and the classified gestures are then used to control a robotic arm. The final trained LSTM model was able to achieve an F-score of 92% when evaluated against a test set of 9 gestures. After, it was successfully integrated and implemented with a Niryo Ned robotic arm which was tasked to perform 8 actions. The development of this system would be beneficial especially in the field of assistive technology, giving individuals with physical limitations and disabilities ways to overcome their limitations, enabling them to live independently in this world that is largely catered to the able-bodied majority.
author2 Tay, Wee Peng
author_facet Tay, Wee Peng
Tan, Yi Ming
format Final Year Project
author Tan, Yi Ming
author_sort Tan, Yi Ming
title Development of gesture control of robotic arm/robotic structures for assistive systems
title_short Development of gesture control of robotic arm/robotic structures for assistive systems
title_full Development of gesture control of robotic arm/robotic structures for assistive systems
title_fullStr Development of gesture control of robotic arm/robotic structures for assistive systems
title_full_unstemmed Development of gesture control of robotic arm/robotic structures for assistive systems
title_sort development of gesture control of robotic arm/robotic structures for assistive systems
publisher Nanyang Technological University
publishDate 2021
url https://hdl.handle.net/10356/150122
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