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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Bibliographic Details
Published inSensors (Basel, Switzerland) Vol. 25; no. 6; p. 1872
Main Authors Ji, Yujie, Yu, Jiale
Format Journal Article
LanguageEnglish
Published Switzerland MDPI AG 18.03.2025
MDPI
Subjects
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
time-impact optimal
ISO algorithm
S-shaped speed curve
trajectory planning
Online AccessGet full text
ISSN1424-8220
1424-8220
DOI10.3390/s25061872

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Summary: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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ISSN:1424-8220
1424-8220
DOI:10.3390/s25061872