Adaptive equidistant robot path planning on complex freeform surface based on 3D point cloud and least-squares conformal mapping

In complex freeform surface machining, such as aircraft skin grinding and engine blade processing, equidistant robot path planning is challenged by low efficiency and strong curvature influence on path uniformity. To address this, an adaptive equidistant path planning method based on 3D point cloud data and least-squares conformal mapping (LSCM) is proposed. The point cloud of the workpiece is first reconstructed into triangular meshes using greedy triangulation, and then the 3D path planning problem is reduced to a 2D plane through LSCM. Equidistant paths are generated on the plane and inversely mapped to 3D space via the area coordinate method, ensuring consistent spacing along complex curved surfaces. A complete robot path planning system integrating online and offline programming was developed and validated on an ABB industrial robot with a designed 3D-printed freeform surface. Experimental results show that the proposed method improves path isometry and normal accuracy, effectively enhancing machining efficiency and surface quality.