Cascaded Fuzzy Reward Mechanisms in Deep Reinforcement Learning for Comprehensive Path Planning in Textile Robotic Systems

With the rapid advancement of industrial automation and artificial intelligence technologies, particularly in the textile industry, robotic technology is increasingly challenged with intelligent path planning and executing high-precision tasks. This study focuses on the automatic path planning and y...
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Autor*in:	Di Zhao [verfasserIn] Zhenyu Ding [verfasserIn] Wenjie Li [verfasserIn] Sen Zhao [verfasserIn] Yuhong Du [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	fuzzy reward end-to-end network trajectory planning forward kinematics deep reinforcement learning

Übergeordnetes Werk:	In: Applied Sciences - MDPI AG, 2012, 14(2024), 2, p 851
Übergeordnetes Werk:	volume:14 ; year:2024 ; number:2, p 851

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/app14020851

Katalog-ID:	DOAJ096198931

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authorswithroles_txt_mv	Di Zhao @@aut@@ Zhenyu Ding @@aut@@ Wenjie Li @@aut@@ Sen Zhao @@aut@@ Yuhong Du @@aut@@
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callnumber-first	T - Technology
author	Di Zhao
spellingShingle	Di Zhao misc TA1-2040 misc QH301-705.5 misc QC1-999 misc QD1-999 misc fuzzy reward misc end-to-end network misc trajectory planning misc forward kinematics misc deep reinforcement learning misc Technology misc T misc Engineering (General). Civil engineering (General) misc Biology (General) misc Physics misc Chemistry Cascaded Fuzzy Reward Mechanisms in Deep Reinforcement Learning for Comprehensive Path Planning in Textile Robotic Systems
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topic_title	TA1-2040 QH301-705.5 QC1-999 QD1-999 Cascaded Fuzzy Reward Mechanisms in Deep Reinforcement Learning for Comprehensive Path Planning in Textile Robotic Systems fuzzy reward end-to-end network trajectory planning forward kinematics deep reinforcement learning
topic	misc TA1-2040 misc QH301-705.5 misc QC1-999 misc QD1-999 misc fuzzy reward misc end-to-end network misc trajectory planning misc forward kinematics misc deep reinforcement learning misc Technology misc T misc Engineering (General). Civil engineering (General) misc Biology (General) misc Physics misc Chemistry
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title	Cascaded Fuzzy Reward Mechanisms in Deep Reinforcement Learning for Comprehensive Path Planning in Textile Robotic Systems
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title_sort	cascaded fuzzy reward mechanisms in deep reinforcement learning for comprehensive path planning in textile robotic systems
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title_auth	Cascaded Fuzzy Reward Mechanisms in Deep Reinforcement Learning for Comprehensive Path Planning in Textile Robotic Systems
abstract	With the rapid advancement of industrial automation and artificial intelligence technologies, particularly in the textile industry, robotic technology is increasingly challenged with intelligent path planning and executing high-precision tasks. This study focuses on the automatic path planning and yarn-spool-assembly tasks of textile robotic arms, proposing an end-to-end planning and control model that integrates deep reinforcement learning. The innovation of this paper lies in the introduction of a cascaded fuzzy reward system, which is integrated into the end-to-end model to enhance learning efficiency and reduce ineffective exploration, thereby accelerating the convergence of the model. A series of experiments conducted in a simulated environment demonstrate the model’s exceptional performance in yarn-spool-assembly tasks. Compared to traditional reinforcement learning methods, our model shows potential advantages in improving task success rates and reducing collision rates. The cascaded fuzzy reward system, a core component of our end-to-end deep reinforcement learning model, offers a novel and more robust solution for the automated path planning of robotic arms. In summary, the method proposed in this study provides a new perspective and potential applications for industrial automation, especially in the operation of robotic arms in complex and uncertain environments.
abstractGer	With the rapid advancement of industrial automation and artificial intelligence technologies, particularly in the textile industry, robotic technology is increasingly challenged with intelligent path planning and executing high-precision tasks. This study focuses on the automatic path planning and yarn-spool-assembly tasks of textile robotic arms, proposing an end-to-end planning and control model that integrates deep reinforcement learning. The innovation of this paper lies in the introduction of a cascaded fuzzy reward system, which is integrated into the end-to-end model to enhance learning efficiency and reduce ineffective exploration, thereby accelerating the convergence of the model. A series of experiments conducted in a simulated environment demonstrate the model’s exceptional performance in yarn-spool-assembly tasks. Compared to traditional reinforcement learning methods, our model shows potential advantages in improving task success rates and reducing collision rates. The cascaded fuzzy reward system, a core component of our end-to-end deep reinforcement learning model, offers a novel and more robust solution for the automated path planning of robotic arms. In summary, the method proposed in this study provides a new perspective and potential applications for industrial automation, especially in the operation of robotic arms in complex and uncertain environments.
abstract_unstemmed	With the rapid advancement of industrial automation and artificial intelligence technologies, particularly in the textile industry, robotic technology is increasingly challenged with intelligent path planning and executing high-precision tasks. This study focuses on the automatic path planning and yarn-spool-assembly tasks of textile robotic arms, proposing an end-to-end planning and control model that integrates deep reinforcement learning. The innovation of this paper lies in the introduction of a cascaded fuzzy reward system, which is integrated into the end-to-end model to enhance learning efficiency and reduce ineffective exploration, thereby accelerating the convergence of the model. A series of experiments conducted in a simulated environment demonstrate the model’s exceptional performance in yarn-spool-assembly tasks. Compared to traditional reinforcement learning methods, our model shows potential advantages in improving task success rates and reducing collision rates. The cascaded fuzzy reward system, a core component of our end-to-end deep reinforcement learning model, offers a novel and more robust solution for the automated path planning of robotic arms. In summary, the method proposed in this study provides a new perspective and potential applications for industrial automation, especially in the operation of robotic arms in complex and uncertain environments.
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title_short	Cascaded Fuzzy Reward Mechanisms in Deep Reinforcement Learning for Comprehensive Path Planning in Textile Robotic Systems
url	https://doi.org/10.3390/app14020851 https://doaj.org/article/c1617326640041e6a1deb1ca87584faa https://www.mdpi.com/2076-3417/14/2/851 https://doaj.org/toc/2076-3417
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Cascaded Fuzzy Reward Mechanisms in Deep Reinforcement Learning for Comprehensive Path Planning in Textile Robotic Systems

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