Trajectory planning of redundant parallel mechanism considering motion accuracy based on reinforcement learning
The motion accuracy of the trajectory directly affects the reliability of parallel mechanisms in precision tasks such as micro-assembly. Thus, this paper investigates trajectory planning considering motion accuracy, and uses a (6+3)-degrees of freedom (DOF) kinematically redundant parallel mechanism...
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| Vydáno v: | Engineering applications of artificial intelligence Ročník 162; s. 112552 |
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| Jazyk: | angličtina |
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Elsevier Ltd
22.12.2025
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| ISSN: | 0952-1976 |
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| Abstract | The motion accuracy of the trajectory directly affects the reliability of parallel mechanisms in precision tasks such as micro-assembly. Thus, this paper investigates trajectory planning considering motion accuracy, and uses a (6+3)-degrees of freedom (DOF) kinematically redundant parallel mechanism (KRPM) as a case study. First, the kinematics of KRPM are analyzed, and an error model incorporating dimensional errors, driving input errors, and joint clearances is established. Then, based on the error model, the aggregate sensitivity index (ASI) and comprehensive error sensitivity (CES) are introduced to study the error properties of KRPM, along with a universal analysis process. Subsequently, reinforcement learning (RL) utilizing the twin delayed deep deterministic policy gradient (TD3) algorithm is employed to the trajectory planning of KRPM considering motion accuracy. Finally, numerical simulation is carried out based on three cases to verify the effectiveness of the proposed method, and experimental results further demonstrate its practical applicability.
•The error model of a (6+3)-DOF kinematically redundant parallel mechanism is established.•The trajectory planning problem considering motion accuracy is modeled as MDP and solved by the TD3 algorithm.•Numerical simulations are carried out based on three cases to verify the effectiveness of the proposed method.•Experimental results further verify the practical applicability of the proposed method. |
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| AbstractList | The motion accuracy of the trajectory directly affects the reliability of parallel mechanisms in precision tasks such as micro-assembly. Thus, this paper investigates trajectory planning considering motion accuracy, and uses a (6+3)-degrees of freedom (DOF) kinematically redundant parallel mechanism (KRPM) as a case study. First, the kinematics of KRPM are analyzed, and an error model incorporating dimensional errors, driving input errors, and joint clearances is established. Then, based on the error model, the aggregate sensitivity index (ASI) and comprehensive error sensitivity (CES) are introduced to study the error properties of KRPM, along with a universal analysis process. Subsequently, reinforcement learning (RL) utilizing the twin delayed deep deterministic policy gradient (TD3) algorithm is employed to the trajectory planning of KRPM considering motion accuracy. Finally, numerical simulation is carried out based on three cases to verify the effectiveness of the proposed method, and experimental results further demonstrate its practical applicability.
•The error model of a (6+3)-DOF kinematically redundant parallel mechanism is established.•The trajectory planning problem considering motion accuracy is modeled as MDP and solved by the TD3 algorithm.•Numerical simulations are carried out based on three cases to verify the effectiveness of the proposed method.•Experimental results further verify the practical applicability of the proposed method. |
| ArticleNumber | 112552 |
| Author | Zhang, Fen-hua Zhang, Xian-min Qiu, Zhi-cheng Zeng, Chen-dong |
| Author_xml | – sequence: 1 givenname: Chen-dong surname: Zeng fullname: Zeng, Chen-dong – sequence: 2 givenname: Zhi-cheng surname: Qiu fullname: Qiu, Zhi-cheng – sequence: 3 givenname: Fen-hua surname: Zhang fullname: Zhang, Fen-hua – sequence: 4 givenname: Xian-min surname: Zhang fullname: Zhang, Xian-min email: zhangxm@scut.edu.cn |
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| Keywords | Motion accuracy Trajectory planning Redundant parallel mechanism Reinforcement learning Twin delayed deep deterministic policy gradient algorithm |
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| SubjectTerms | Motion accuracy Redundant parallel mechanism Reinforcement learning Trajectory planning Twin delayed deep deterministic policy gradient algorithm |
| Title | Trajectory planning of redundant parallel mechanism considering motion accuracy based on reinforcement learning |
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