Domain Knowledge-Enhanced Welding Robot Path Planning Algorithm

Welding Robot Path Planning (WRPP) is a core technology for welding automation that seeks an optimal path that satisfies the requirements of the welding process in complex obstacle environments. However, existing algorithms generally lack the integration of knowledge in the welding domain and exhibit insufficient adaptability to complex working conditions. To address these issues, this study proposes a Domain Knowledge-Enhanced Welding Robot Path Planning Algorithm (DKE-WRPP) equipped with a pluggable knowledge fusion frame-work, which is validated on three representative path planning algorithms: Artificial Bee Colony (ABC), Genetic Algorithm (GA), and Optimal Rapidly-exploring Random Tree (RRT*). Specifically, we first employ Large Language Models (LLMs) to extract domain knowledge such as welding processes and safety distances, and generate standardized knowledge vectors via semantic encoding using a pre-trained language model. Then, a Knowledge Enhancement Module (KEM) is constructed to deeply fuse knowledge features and path geometric features through an attention mechanism, and adaptively update the cost functions of the three baseline algorithms, realizing low-intrusive coupling between domain knowledge and planning algorithms. Finally, experiments in a 300×300 grid environment demonstrate that, compared with traditional algorithms, the knowledge-enhanced algorithms reduce the convergence iterations by more than 33%on average and significantly improve path smoothness. The results fully verify the effectiveness of domain knowledge enhancement and the universality of the pluggable framework, providing an efficient and stable solution for welding robot path planning in complex working conditions.

@article{Liu2026,
  title     = {Domain Knowledge-Enhanced Welding Robot Path Planning Algorithm},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {5},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Ming Liu and Peng Shao},
  doi       = {10.14569/IJACSA.2026.0170582},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170582}
}