Path Optimization for Robotic Arm Additive Manufacturing via Geometric Curvature-Adaptive Sampling

To address the significant accuracy loss in high-curvature regions caused by conventional uniform sampling in the preprocessing stage, this paper proposes a geometric curvature-based adaptive sampling method for trajectory planning of multi-degree-of-freedom robotic arms in additive manufacturing. The strategy dynamically adjusts the sampling density according to the path curvature, preserving more points in high-curvature regions to maintain trajectory accuracy while reducing redundant points in low-curvature regions to decrease computational load. A local error evaluation method is proposed to assess the path error of the trajectory planning results for the sampled point sets. Experimental results within the TOPPRA framework demonstrate that, compared to uniform sampling, the proposed approach reduces errors in high-curvature regions by 21.68%–30.06% without affect the errors in low-curvature regions. This improve ment enhances the overall trajectory quality while maintaining the same point set size.