Dual-Layer Ant Colony Optimization for Trajectory Direction Planning on Complex Surfaces

Abstract

To address the joint optimization problem of mesh partition and direction decision in complex surface spray trajectory planning, a partition and direction planning method using a dual-layer ant colony optimization (ACO) is proposed. The inner layer over-segments with a minimal dihedral angle threshold to ensure boundary closure, performs hierarchical merging using multi-feature distance, and then employs ACO to detect missed boundaries in smooth-transition regions. The outer layer pre-determines trajectory patterns based on partition geometric features, searches for the optimal direction combination via ACO, and feeds back the objective function to adjust the inner-layer merging parameters, achieving two-layer collaborative optimization. Experiments demonstrate that the method achieves favorable results on meshes from different sources. ACO boundary optimization adds 920 new partitions on the scanned mesh, reliably identifying smooth-transition boundaries. The direction deviation of 88.5% of partitions does not exceed 24 degrees. Ablation experiments verify the irreplaceability of each module.