Time-Impact Optimal Trajectory Planning for Wafer-Handling Robotic Arms Based on the Improved Snake Optimization Algorithm
To enhance the working efficiency of a wafer-handling robotic arm and simultaneously alleviate the impact and vibration during the motion process, a trajectory planning approach based on an improved snake optimization (ISO) algorithm is proposed. The following improvements have been made to the snak...
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| Published in | Sensors (Basel, Switzerland) Vol. 25; no. 6; p. 1872 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
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18.03.2025
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| ISSN | 1424-8220 1424-8220 |
| DOI | 10.3390/s25061872 |
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| Abstract | To enhance the working efficiency of a wafer-handling robotic arm and simultaneously alleviate the impact and vibration during the motion process, a trajectory planning approach based on an improved snake optimization (ISO) algorithm is proposed. The following improvements have been made to the snake optimization (SO) algorithm: the introduction of a Chaotic Tent Map for initializing the swarm, the use of randomly perturbed dynamic learning factors to replace fixed values, the application of a cosine annealing learning rate for self-adaptively updating individual positions, and the incorporation of Bayesian optimization for parameterization and fine-tuning of the system’s selection process. Furthermore, the ISO algorithm is applied in the Cartesian space of the robotic arm to effectively address the trajectory planning challenge of the single-segment start–stop S-shaped speed curve with arc transitions. The simulation results indicate that the improved S-shaped speed curve has increased by 24.1% compared with the original plan, and the mean and variance rankings of ISO algorithm have, respectively, improved by 60.8% and 63.4%, compared with the SO algorithm. Meanwhile, this study has successfully achieved the Pareto optimal solution with time and impact as the targets based on the established MATLAB experimental simulation platform. |
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| AbstractList | To enhance the working efficiency of a wafer-handling robotic arm and simultaneously alleviate the impact and vibration during the motion process, a trajectory planning approach based on an improved snake optimization (ISO) algorithm is proposed. The following improvements have been made to the snake optimization (SO) algorithm: the introduction of a Chaotic Tent Map for initializing the swarm, the use of randomly perturbed dynamic learning factors to replace fixed values, the application of a cosine annealing learning rate for self-adaptively updating individual positions, and the incorporation of Bayesian optimization for parameterization and fine-tuning of the system’s selection process. Furthermore, the ISO algorithm is applied in the Cartesian space of the robotic arm to effectively address the trajectory planning challenge of the single-segment start–stop S-shaped speed curve with arc transitions. The simulation results indicate that the improved S-shaped speed curve has increased by 24.1% compared with the original plan, and the mean and variance rankings of ISO algorithm have, respectively, improved by 60.8% and 63.4%, compared with the SO algorithm. Meanwhile, this study has successfully achieved the Pareto optimal solution with time and impact as the targets based on the established MATLAB experimental simulation platform. To enhance the working efficiency of a wafer-handling robotic arm and simultaneously alleviate the impact and vibration during the motion process, a trajectory planning approach based on an improved snake optimization (ISO) algorithm is proposed. The following improvements have been made to the snake optimization (SO) algorithm: the introduction of a Chaotic Tent Map for initializing the swarm, the use of randomly perturbed dynamic learning factors to replace fixed values, the application of a cosine annealing learning rate for self-adaptively updating individual positions, and the incorporation of Bayesian optimization for parameterization and fine-tuning of the system's selection process. Furthermore, the ISO algorithm is applied in the Cartesian space of the robotic arm to effectively address the trajectory planning challenge of the single-segment start-stop S-shaped speed curve with arc transitions. The simulation results indicate that the improved S-shaped speed curve has increased by 24.1% compared with the original plan, and the mean and variance rankings of ISO algorithm have, respectively, improved by 60.8% and 63.4%, compared with the SO algorithm. Meanwhile, this study has successfully achieved the Pareto optimal solution with time and impact as the targets based on the established MATLAB experimental simulation platform.To enhance the working efficiency of a wafer-handling robotic arm and simultaneously alleviate the impact and vibration during the motion process, a trajectory planning approach based on an improved snake optimization (ISO) algorithm is proposed. The following improvements have been made to the snake optimization (SO) algorithm: the introduction of a Chaotic Tent Map for initializing the swarm, the use of randomly perturbed dynamic learning factors to replace fixed values, the application of a cosine annealing learning rate for self-adaptively updating individual positions, and the incorporation of Bayesian optimization for parameterization and fine-tuning of the system's selection process. Furthermore, the ISO algorithm is applied in the Cartesian space of the robotic arm to effectively address the trajectory planning challenge of the single-segment start-stop S-shaped speed curve with arc transitions. The simulation results indicate that the improved S-shaped speed curve has increased by 24.1% compared with the original plan, and the mean and variance rankings of ISO algorithm have, respectively, improved by 60.8% and 63.4%, compared with the SO algorithm. Meanwhile, this study has successfully achieved the Pareto optimal solution with time and impact as the targets based on the established MATLAB experimental simulation platform. |
| Audience | Academic |
| Author | Ji, Yujie Yu, Jiale |
| AuthorAffiliation | School of Mechanical Engineering, Shenyang Ligong University, Shenyang 110159, China; yjl2801036037@163.com |
| AuthorAffiliation_xml | – name: School of Mechanical Engineering, Shenyang Ligong University, Shenyang 110159, China; yjl2801036037@163.com |
| Author_xml | – sequence: 1 givenname: Yujie surname: Ji fullname: Ji, Yujie – sequence: 2 givenname: Jiale orcidid: 0009-0001-2697-3658 surname: Yu fullname: Yu, Jiale |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/40293009$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.3390/pr10112195 10.3390/s22114071 10.3390/app14188234 10.1063/5.0058391 10.1007/s40430-022-03578-9 10.1016/j.ast.2020.106360 10.1007/s10489-024-06124-3 10.23919/CCC52363.2021.9549644 10.1109/LRA.2022.3165184 10.1007/s40430-024-04694-4 10.1049/cje.2021.00.373 10.3390/s23135974 10.3934/mbe.2024054 10.1109/CAC51589.2020.9327351 10.1109/ACCESS.2022.3220765 10.1017/S0263574724000481 10.1016/j.swevo.2019.03.004 10.3390/electronics12051135 10.1038/s41598-024-60402-1 10.1051/jnwpu/20234120428 10.1016/j.knosys.2022.108320 10.3390/math12111708 10.1109/ACCESS.2024.3375943 10.1109/LRA.2023.3256921 10.1177/09544062221088723 10.1109/CCDC58219.2023.10327662 10.1109/ITNEC.2019.8729547 10.1038/s41598-023-47001-2 10.3233/JIFS-202810 10.3934/mbe.2023150 10.1109/ICRA48506.2021.9561601 10.1108/IR-07-2023-0157 10.3390/app13127031 10.1002/rob.22354 |
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| SubjectTerms | Accuracy Algorithms Analysis Efficiency Energy consumption Genetic algorithms ISO algorithm Kinematics Mathematical optimization Methods Optimization algorithms Planning Robotics Robots S-shaped speed curve time-impact optimal trajectory planning |
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| Title | Time-Impact Optimal Trajectory Planning for Wafer-Handling Robotic Arms Based on the Improved Snake Optimization Algorithm |
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