Robotic Grinding Trajectory Planning and Correction Method for Large-Scale Complex-Shaped Products

Due to the inherent characteristics of large dimensions, complex curved surfaces, and densely distributed protrusions present in aerospace products, conventional offline programming and trajectory planning techniques for robots primarily prioritize the facilitation of uninterrupted grinding processes and the generation of points along trajectories on surfaces characterized by smoothness. However, these methods encounter challenges in identifying and proactively avoiding surface protrusions during the planning phase. The present paper puts forth a proposal for an automated robot trajectory planning method for grinding operations. This method is predicated on the integration of region partitioning and deformation correction. Specifically, this method first identifies protrusions based on curvature features and rule matching, followed by an analysis of the feasible workspace of a six-axis industrial robot equipped with an external axis. The product surface is discretized into multiple regional units according to the distribution characteristics of protrusions and the constraints of the feasible workspace. Subsequently, a parameter-optimized parallel sectioning method is employed to independently plan trajectories for each unit. The utilization of on-site measured point cloud data facilitates the analysis of contour deviations. In addition, grinding trajectories are dynamically corrected to meet high-precision process requirements. This approach effectively overcomes the challenge that trajectory planning for large-scale, complex-shaped products is easily affected by protrusions. According to the established methodology, the development of an offline programming software system for robotic automatic grinding was initiated. To this end, experiments were conducted on aircraft wall panels to plan and modify grinding trajectories using the proposed method. This process was undertaken to validate the effectiveness and engineering practicability of the proposed method.