Facial region division and robotic trajectory planning method for facial cleaning tasks

Aiming at the demand for automated facial cleaning for people with hand dysfunction, as well as the key challenges faced by facial cleaning robots, including accurate 3D geometric perception of the human face and trajectory planning for cleaning on complex curved surfaces, this paper proposes a complete technical scheme of trajectory generation for robotic arm facial cleaning operations. The scheme adopts a three-layer architecture of “data acquisition–facial region division–motion planning.” Firstly, 3D point cloud reconstruction of the human face is realized through robotic arm surrounding acquisition and ICP registration, and accurate matching between 2D facial key points and 3D point clouds is completed in combination with the MediaPipe Face Mesh model. Subsequently, optimized division of facial regions for cleaning tasks is accomplished based on facial anatomical divisions and multi-dimensional curvature features. Finally, a smooth cleaning trajectory fitting the facial surface is generated through normal fitting pose solving, dual-constraint reachable pose search, and adaptive spline interpolation. Experimental results demonstrate that the proposed facial partitioning method effectively segments the facial point cloud on demand. Compared to traditional anatomical facial partitioning, the number of designated cleaning regions is reduced from 16 to 9, significantly lowering the complexity of trajectory planning. Furthermore, the motion planning method ensures the reachability, configuration consistency, and kinematic smoothness of the cleaning trajectory, providing a robust technical reference for the research and development of nursing facial cleaning robots.