Robotic Tack Welding Path and Trajectory Optimization Using an LF-IWOA
Robotic tack welding poses challenges in path optimization due to local optimum entrapment, limited adaptability, and high-dimensional complexity. To overcome these challenges, a Lévy flight-enhanced improved whale optimization algorithm (LF-IWOA) was developed. The algorithm combines elite oppositi...
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MDPI AG
2025-06-01
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| Series: | Actuators |
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| Online Access: | https://www.mdpi.com/2076-0825/14/6/287 |
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| author | Bingqi Jia Haihong Pan Lei Zhang Yifan Yang Huaxin Chen Lin Chen |
| author_facet | Bingqi Jia Haihong Pan Lei Zhang Yifan Yang Huaxin Chen Lin Chen |
| author_sort | Bingqi Jia |
| collection | DOAJ |
| description | Robotic tack welding poses challenges in path optimization due to local optimum entrapment, limited adaptability, and high-dimensional complexity. To overcome these challenges, a Lévy flight-enhanced improved whale optimization algorithm (LF-IWOA) was developed. The algorithm combines elite opposition-based learning (EOBL), differential evolution (DE), and Lévy flight (LF) to improve global exploration capability, increase population diversity, and improve convergence. Additionally, a dynamic trajectory optimization model is designed to consider joint-level constraints, including velocity, acceleration, and jerk. The performance of LF-IWOA was evaluated using two industrial workpieces with varying welding point distributions. Comparative experiments with metaheuristic algorithms, such as the genetic algorithm (GA), WOA and other recent nature-inspired methods, show that LF-IWOA consistently achieves shorter paths and faster convergence. For Workpiece 1, the algorithm reduces the welding path by up to 25.53% compared to the genetic algorithm, with an average reduction of 14.82% across benchmarks. For Workpiece 2, the optimized path is 18.41% shorter than the baseline. Moreover, the dynamic trajectory optimization strategy decreases execution time by 26.83% and reduces mechanical energy consumption by 15.40% while maintaining smooth and stable joint motion. Experimental results demonstrated the effectiveness and practical applicability of the LF-IWOA in robotic welding tasks. |
| format | Article |
| id | doaj-art-3a91e3a71f0e41fca132d767bdf77932 |
| institution | Kabale University |
| issn | 2076-0825 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Actuators |
| spelling | doaj-art-3a91e3a71f0e41fca132d767bdf779322025-08-20T03:30:28ZengMDPI AGActuators2076-08252025-06-0114628710.3390/act14060287Robotic Tack Welding Path and Trajectory Optimization Using an LF-IWOABingqi Jia0Haihong Pan1Lei Zhang2Yifan Yang3Huaxin Chen4Lin Chen5School of Mechanical Engineering, Guangxi University, Nanning 530004, ChinaSchool of Mechanical Engineering, Guangxi University, Nanning 530004, ChinaSchool of Mechanical Engineering, Guangxi University, Nanning 530004, ChinaSchool of Mechanical Engineering, Guangxi University, Nanning 530004, ChinaSchool of Advanced Manufacturing Engineering, Guangxi Science & Technology Normal University, Laibin 546100, ChinaSchool of Mechanical Engineering, Guangxi University, Nanning 530004, ChinaRobotic tack welding poses challenges in path optimization due to local optimum entrapment, limited adaptability, and high-dimensional complexity. To overcome these challenges, a Lévy flight-enhanced improved whale optimization algorithm (LF-IWOA) was developed. The algorithm combines elite opposition-based learning (EOBL), differential evolution (DE), and Lévy flight (LF) to improve global exploration capability, increase population diversity, and improve convergence. Additionally, a dynamic trajectory optimization model is designed to consider joint-level constraints, including velocity, acceleration, and jerk. The performance of LF-IWOA was evaluated using two industrial workpieces with varying welding point distributions. Comparative experiments with metaheuristic algorithms, such as the genetic algorithm (GA), WOA and other recent nature-inspired methods, show that LF-IWOA consistently achieves shorter paths and faster convergence. For Workpiece 1, the algorithm reduces the welding path by up to 25.53% compared to the genetic algorithm, with an average reduction of 14.82% across benchmarks. For Workpiece 2, the optimized path is 18.41% shorter than the baseline. Moreover, the dynamic trajectory optimization strategy decreases execution time by 26.83% and reduces mechanical energy consumption by 15.40% while maintaining smooth and stable joint motion. Experimental results demonstrated the effectiveness and practical applicability of the LF-IWOA in robotic welding tasks.https://www.mdpi.com/2076-0825/14/6/287robotic tack weldingpath planningLF-IWOAtrajectory optimization |
| spellingShingle | Bingqi Jia Haihong Pan Lei Zhang Yifan Yang Huaxin Chen Lin Chen Robotic Tack Welding Path and Trajectory Optimization Using an LF-IWOA Actuators robotic tack welding path planning LF-IWOA trajectory optimization |
| title | Robotic Tack Welding Path and Trajectory Optimization Using an LF-IWOA |
| title_full | Robotic Tack Welding Path and Trajectory Optimization Using an LF-IWOA |
| title_fullStr | Robotic Tack Welding Path and Trajectory Optimization Using an LF-IWOA |
| title_full_unstemmed | Robotic Tack Welding Path and Trajectory Optimization Using an LF-IWOA |
| title_short | Robotic Tack Welding Path and Trajectory Optimization Using an LF-IWOA |
| title_sort | robotic tack welding path and trajectory optimization using an lf iwoa |
| topic | robotic tack welding path planning LF-IWOA trajectory optimization |
| url | https://www.mdpi.com/2076-0825/14/6/287 |
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