Trajectory Planning of Shipbuilding Welding Manipulator Based on Improved Whale Optimization Algorithm

Time-optimal trajectory planning for shipboard welding robotic arms is a challenging problem due to strong kinematic constraints and the nonlinear coupling between trajectory parameters and execution time. Although various intelligent optimization algorithms have been combined with robotic arm trajectory planning in existing studies, most approaches primarily focus on algorithmic performance improvement and lack a clear formulation of time optimization within polynomial trajectory planning. To address this gap, this study proposes an Improved Whale Optimization Algorithm (IWOA) based on the traditional quintic polynomial trajectory planning method. In the proposed method, the trajectory execution time is explicitly formulated as the optimization objective under kinematic constraints, and the IWOA is designed to stably and efficiently search the time parameter space of the quintic polynomial trajectory. Specifically, chaotic sequence initialization is employed to enhance population distribution, an adaptive weight mechanism is introduced to balance global exploration and local exploitation, and a hybrid co-optimization strategy combining differential evolution and genetic operators is integrated to improve robustness and convergence stability. Simulation experiments are conducted to evaluate the effectiveness of the proposed algorithms. The results demonstrated that, while satisfying robotic arms kinematic constraints, the proposed method achieves an 18.3% reduction in operating time compared with the unoptimized trajectory. These results indicate that the proposed approach provides a systematic and effective solution for time-efficient trajectory planning of shipboard welding robotic arms.

@article{Liang2025,
  title     = {Trajectory Planning of Shipbuilding Welding Manipulator Based on Improved Whale Optimization Algorithm},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {12},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Caiping Liang and Hao Yuan and Chen Wang and Wenxu Niu and Yansong Zhang},
  doi       = {10.14569/IJACSA.2025.01612127},
  url       = {https://doi.org/10.14569/IJACSA.2025.01612127}
}