Adaptive Grinding Planning of Robotic Arms with Self-optimization

In adaptive grinding tasks, robotic arms are required to autonomously accomplish uniform grinding of work-pieces with unknown sizes and arbitrary surface characteristics. This brings a great challenge to planning and control of robotic arms. In order to achieve adaptive grinding in terms of grinding path and grinding degree, an offline-online planning framework incorporating shortest grinding path offline generation and contact force online control is proposed in this paper. Under the case that workpiece size is unknown and the distribution of points to be ground is arbitrary, the shortest grinding path is generated adaptively by size estimation and Double-layer Ant Colony System(DACS) based optimal sorting algorithm. Grinding degree planning and contact force control are applied to adaptively adjust grinding degree. In order to more sufficiently utilize computational resources to minimize grinding path length, DACS is designed and applied in optimal sorting algorithm to make the latter gradually improve its optimization capability by self-optimization. Experiment results demonstrate the capability of the proposed planning framework to achieve adaptive grinding in terms of grinding path and grinding degree as well as the self-optimization capability of DACS-based optimal sorting algorithm.