Robotic Arm Trajectory Planning Method Using Deep Deterministic Policy Gradient With Hierarchical Memory Structure
Traditional robotic arm path planning methods are mainly carried out in the tool center point operation space, and frequently solve inverse kinematics problems, thus consuming a large number of computational resources. In contrast, the use of positive kinematics for planning in joint space not only...
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Format: | Article |
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IEEE
2023-01-01
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Series: | IEEE Access |
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Online Access: | https://ieeexplore.ieee.org/document/10348581/ |
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author | Di Zhao Zhenyu Ding Wenjie Li Sen Zhao Yuhong Du |
author_facet | Di Zhao Zhenyu Ding Wenjie Li Sen Zhao Yuhong Du |
author_sort | Di Zhao |
collection | DOAJ |
description | Traditional robotic arm path planning methods are mainly carried out in the tool center point operation space, and frequently solve inverse kinematics problems, thus consuming a large number of computational resources. In contrast, the use of positive kinematics for planning in joint space not only enhances efficiency, but also provides analytic solutions with higher accuracy. In order to better cover the environmental state space, this paper adopts the full-preserving experience preservation approach. In order to realize fast and efficient sampling of high reward value experience, this study constructs an innovative hierarchical memory structure and eliminates the overfitting phenomenon that may be caused by biased sampling through the Bias-Free strategy. Experimentally validated in the continuous path planning task of a textile robot arm, the proposed hierarchical memory deep deterministic gradient strategy method (HM-DDPG) demonstrates excellent performance and practicality in the textile robot arm path planning problem. This method offers an efficient and robust solution for handling complex tasks with temporal dependencies, paving the way for future innovations and applications in industrial fields such as textiles. |
first_indexed | 2024-03-08T19:36:29Z |
format | Article |
id | doaj.art-8e956913de214c90b48b276529b14598 |
institution | Directory Open Access Journal |
issn | 2169-3536 |
language | English |
last_indexed | 2024-03-08T19:36:29Z |
publishDate | 2023-01-01 |
publisher | IEEE |
record_format | Article |
series | IEEE Access |
spelling | doaj.art-8e956913de214c90b48b276529b145982023-12-26T00:10:16ZengIEEEIEEE Access2169-35362023-01-011114080114081410.1109/ACCESS.2023.334068410348581Robotic Arm Trajectory Planning Method Using Deep Deterministic Policy Gradient With Hierarchical Memory StructureDi Zhao0https://orcid.org/0009-0001-5969-2836Zhenyu Ding1Wenjie Li2https://orcid.org/0009-0008-6119-487XSen Zhao3Yuhong Du4https://orcid.org/0009-0007-6965-3105School of Mechanical Engineering, Tiangong University, Tianjin, ChinaSchool of Electronics and Information Engineering, Tiangong University, Tianjin, ChinaSchool of Mechanical Engineering, Tiangong University, Tianjin, ChinaSchool of Mechanical Engineering, Tiangong University, Tianjin, ChinaInnovation College, Tiangong University, Tianjin, ChinaTraditional robotic arm path planning methods are mainly carried out in the tool center point operation space, and frequently solve inverse kinematics problems, thus consuming a large number of computational resources. In contrast, the use of positive kinematics for planning in joint space not only enhances efficiency, but also provides analytic solutions with higher accuracy. In order to better cover the environmental state space, this paper adopts the full-preserving experience preservation approach. In order to realize fast and efficient sampling of high reward value experience, this study constructs an innovative hierarchical memory structure and eliminates the overfitting phenomenon that may be caused by biased sampling through the Bias-Free strategy. Experimentally validated in the continuous path planning task of a textile robot arm, the proposed hierarchical memory deep deterministic gradient strategy method (HM-DDPG) demonstrates excellent performance and practicality in the textile robot arm path planning problem. This method offers an efficient and robust solution for handling complex tasks with temporal dependencies, paving the way for future innovations and applications in industrial fields such as textiles.https://ieeexplore.ieee.org/document/10348581/Continuous control taskdeep deterministic policy gradientpath planningpositive kinematicshierarchical memory |
spellingShingle | Di Zhao Zhenyu Ding Wenjie Li Sen Zhao Yuhong Du Robotic Arm Trajectory Planning Method Using Deep Deterministic Policy Gradient With Hierarchical Memory Structure IEEE Access Continuous control task deep deterministic policy gradient path planning positive kinematics hierarchical memory |
title | Robotic Arm Trajectory Planning Method Using Deep Deterministic Policy Gradient With Hierarchical Memory Structure |
title_full | Robotic Arm Trajectory Planning Method Using Deep Deterministic Policy Gradient With Hierarchical Memory Structure |
title_fullStr | Robotic Arm Trajectory Planning Method Using Deep Deterministic Policy Gradient With Hierarchical Memory Structure |
title_full_unstemmed | Robotic Arm Trajectory Planning Method Using Deep Deterministic Policy Gradient With Hierarchical Memory Structure |
title_short | Robotic Arm Trajectory Planning Method Using Deep Deterministic Policy Gradient With Hierarchical Memory Structure |
title_sort | robotic arm trajectory planning method using deep deterministic policy gradient with hierarchical memory structure |
topic | Continuous control task deep deterministic policy gradient path planning positive kinematics hierarchical memory |
url | https://ieeexplore.ieee.org/document/10348581/ |
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