Robotic Surface Finishing with a Region-Based Approach Incorporating Dynamic Motion Constraints

Abstract

This work presents a task-oriented framework for optimizing robotic surface finishing to improve efficiency and ensure feasibility under realistic kinematic and geometric constraints. The approach combines surface subdivision, optimal placement of the workpiece, and region-based toolpath planning to adapt machining strategies to local surface characteristics. A novel time evaluation criterion is introduced that improves our previous kinematic approach by incorporating dynamic aspects. This advancement enables a more realistic estimation of machining time, providing a more reliable basis for optimization and path planning. The framework determines both the optimal position of the workpiece and the subdivision of its surface into regions systematically, enabling machining directions and speeds to be adapted to the geometry of each region. The methodology was validated on several semi-complex surfaces through simulation and experimental trials with collaborative robotic manipulators. The results demonstrate that improved region-based optimization leads to machining time reductions of 9–26% compared to conventional single-direction machining strategies. The most significant improvements were achieved for larger, more complex geometries and denser machining paths, confirming the method’s industrial relevance. These findings establish the framework as a practical solution for reducing cycle time in specific robotic surface finishing tasks.