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Multi-Objective Optimal Trajectory Planning for Robotic Arms Using Deep Reinforcement Learning
This study investigated the trajectory-planning problem of a six-axis robotic arm based on deep reinforcement learning. Taking into account several characteristics of robot motion, a multi-objective optimization approach is proposed, which was based on the motivations of deep reinforcement learning...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10346668/ https://www.ncbi.nlm.nih.gov/pubmed/37447823 http://dx.doi.org/10.3390/s23135974 |
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author | Zhang, Shaobo Xia, Qinxiang Chen, Mingxing Cheng, Sizhu |
author_facet | Zhang, Shaobo Xia, Qinxiang Chen, Mingxing Cheng, Sizhu |
author_sort | Zhang, Shaobo |
collection | PubMed |
description | This study investigated the trajectory-planning problem of a six-axis robotic arm based on deep reinforcement learning. Taking into account several characteristics of robot motion, a multi-objective optimization approach is proposed, which was based on the motivations of deep reinforcement learning and optimal planning. The optimal trajectory was considered with respect to multiple objectives, aiming to minimize factors such as accuracy, energy consumption, and smoothness. The multiple objectives were integrated into the reinforcement learning environment to achieve the desired trajectory. Based on forward and inverse kinematics, the joint angles and Cartesian coordinates were used as the input parameters, while the joint angle estimation served as the output. To enable the environment to rapidly find more-efficient solutions, the decaying episode mechanism was employed throughout the training process. The distribution of the trajectory points was improved in terms of uniformity and smoothness, which greatly contributed to the optimization of the robotic arm’s trajectory. The proposed method demonstrated its effectiveness in comparison with the RRT algorithm, as evidenced by the simulations and physical experiments. |
format | Online Article Text |
id | pubmed-10346668 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-103466682023-07-15 Multi-Objective Optimal Trajectory Planning for Robotic Arms Using Deep Reinforcement Learning Zhang, Shaobo Xia, Qinxiang Chen, Mingxing Cheng, Sizhu Sensors (Basel) Article This study investigated the trajectory-planning problem of a six-axis robotic arm based on deep reinforcement learning. Taking into account several characteristics of robot motion, a multi-objective optimization approach is proposed, which was based on the motivations of deep reinforcement learning and optimal planning. The optimal trajectory was considered with respect to multiple objectives, aiming to minimize factors such as accuracy, energy consumption, and smoothness. The multiple objectives were integrated into the reinforcement learning environment to achieve the desired trajectory. Based on forward and inverse kinematics, the joint angles and Cartesian coordinates were used as the input parameters, while the joint angle estimation served as the output. To enable the environment to rapidly find more-efficient solutions, the decaying episode mechanism was employed throughout the training process. The distribution of the trajectory points was improved in terms of uniformity and smoothness, which greatly contributed to the optimization of the robotic arm’s trajectory. The proposed method demonstrated its effectiveness in comparison with the RRT algorithm, as evidenced by the simulations and physical experiments. MDPI 2023-06-27 /pmc/articles/PMC10346668/ /pubmed/37447823 http://dx.doi.org/10.3390/s23135974 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Zhang, Shaobo Xia, Qinxiang Chen, Mingxing Cheng, Sizhu Multi-Objective Optimal Trajectory Planning for Robotic Arms Using Deep Reinforcement Learning |
title | Multi-Objective Optimal Trajectory Planning for Robotic Arms Using Deep Reinforcement Learning |
title_full | Multi-Objective Optimal Trajectory Planning for Robotic Arms Using Deep Reinforcement Learning |
title_fullStr | Multi-Objective Optimal Trajectory Planning for Robotic Arms Using Deep Reinforcement Learning |
title_full_unstemmed | Multi-Objective Optimal Trajectory Planning for Robotic Arms Using Deep Reinforcement Learning |
title_short | Multi-Objective Optimal Trajectory Planning for Robotic Arms Using Deep Reinforcement Learning |
title_sort | multi-objective optimal trajectory planning for robotic arms using deep reinforcement learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10346668/ https://www.ncbi.nlm.nih.gov/pubmed/37447823 http://dx.doi.org/10.3390/s23135974 |
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