Model-Free Path Planning for Complex Grooves on Spherical Workpieces Based on 3D Point Clouds

To address the precision and motion-smoothing challenges in path planning for spherical workpieces without Computer-Aided Design (CAD) models, this paper proposes a robust point-cloud-driven framework. Conventional Principal Component Analysis (PCA) alignment suffers from centroid shift errors due to asymmetric data loss from light-absorbing surface features. To solve this, a RANSAC-compensated hybrid PCA algorithm is developed to decouple position and orientation estimation, ensuring stable coordinate alignment despite incomplete data. Furthermore, to resolve the geometric collapse and kinematic jitter caused by traditional planar slicing in high-curvature polar regions, a spherical latitudinal equiangular conical slicing algorithm is introduced. By aligning the slicing planes with the sphere’s radial geometry, the method preserves topological accuracy while maintaining an optimal point density for smooth robotic execution. Experimental results on rubber ball groove processing demonstrate a repeat positioning accuracy of 0.09 mm and a feature coverage of 95.21%. This research provides a scientifically rigorous and computationally efficient solution for the automated processing of complex spherical surfaces.

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Publication Details

Journal
Applied Sciences
Published
2026-02-05
DOI
https://doi.org/10.3390/app16031598
Primary Topic
Robotic Path Planning Algorithms
Type
article
Field-Weighted Citation Impact
0.00
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article

Model-Free Path Planning for Complex Grooves on Spherical Workpieces Based on 3D Point Clouds

Ndifreke Offiong, Zhongsheng Zhai, Aoxing Yi, Xikang Xiao et al.
Applied Sciences
Robotic Path Planning Algorithms
article

Model-Free Path Planning for Complex Grooves on Spherical Workpieces Based on 3D Point Clouds

Ndifreke Offiong, Zhongsheng Zhai, Aoxing Yi, Xikang Xiao, Zhen Zeng
article en

Abstract

To address the precision and motion-smoothing challenges in path planning for spherical workpieces without Computer-Aided Design (CAD) models, this paper proposes a robust point-cloud-driven framework. Conventional Principal Component Analysis (PCA) alignment suffers from centroid shift errors due to asymmetric data loss from light-absorbing surface features. To solve this, a RANSAC-compensated hybrid PCA algorithm is developed to decouple position and orientation estimation, ensuring stable coordinate alignment despite incomplete data. Furthermore, to resolve the geometric collapse and kinematic jitter caused by traditional planar slicing in high-curvature polar regions, a spherical latitudinal equiangular conical slicing algorithm is introduced. By aligning the slicing planes with the sphere’s radial geometry, the method preserves topological accuracy while maintaining an optimal point density for smooth robotic execution. Experimental results on rubber ball groove processing demonstrate a repeat positioning accuracy of 0.09 mm and a feature coverage of 95.21%. This research provides a scientifically rigorous and computationally efficient solution for the automated processing of complex spherical surfaces.

Applied SciencesVol. 16(3)
Hubei University of Technology (CN)
Sustainable cities and communities
Openalex Percentile: Top 66%
Robotic Path Planning Algorithms
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