Deep Fusion of Kinematic Features and Task-Aware Partition Planning for Mold Surface Robotic Polishing

Robotic polishing in CAD-free industrial settings relies on point-cloud data, yet noise and non-uniform sampling often compromise kinematic feasibility and finishing quality. This paper proposes an adaptive motion planning approach with explicit kinematic constraints. A downsampling–clustering–mapping-back strategy is first employed for rapid workpiece extraction. Subsequently, an improved supervoxel representation and attributed adjacency graph (AAG) are developed, utilizing a multi-objective energy formulation to partition sub-regions that satisfy geometric consistency and kinematic reachability. To handle point-cloud noise, a lightweight neural network predicts scanning directions and step-distance coefficients, followed by thick-slice serpentine path generation. Finally, closed-loop verification ensures safety through inverse-kinematics and safety-margin checks. Experimental results demonstrate consistent sub-micron finishing quality, with Ra ≈ 0.6 μm on complex mold surfaces. Moreover, the proposed pipeline achieves a 7.5× preprocessing speedup, completing workpiece extraction in 1.14 s for a 237,640-point scan, and improves kinematic feasibility to 100% IK success while reducing the mean TCP normal deviation by ~76% compared with a PCA-based baseline.