Adaptive Grinding Planning of Robotic Arms With Minimal Cost

In adaptive grinding task, robotic arms are required to autonomously achieve uniform grinding of workpieces. This brings a great challenge to estimation and planning techniques. In this article, a three-layer adaptive grinding planning framework is proposed to adaptively accomplish uniform grinding task for tee tubes that have arbitrary size, spatial orientation, and surface characteristic with minimal cost, which is meant to achieve the following three goals simultaneously: 1) minimal grinding loss; 2) shortest grinding path; and 3) smallest computation number (when iteratively optimizing grinding path). In the planning framework proposed, grinding loss is minimized by layer 1 (grinding degree planning layer), computation number of iterative optimization as well as grinding path length are minimized by layer 2 (grinding order optimization layer), and desired grinding path planner and force/position switching controller are designed in grinding path generation layer to drive a robotic arm to adaptively accomplish various uniform grinding tasks for tee tubes. Compared to the state-of-the-art methods, experimental results demonstrate that quantitative performance advantages of our framework in terms of grinding loss, grinding path length, the computation number of iterative optimization, and uniform grinding effect are at least 19.64%, 4.21%, 20.97%, and 15.13%, respectively.