Learning robotic milling strategies based on passive variable operational space interaction control

TL;DR

This paper presents a learning-based robotic milling strategy that adapts to material variability during disassembly, using simulation-trained interaction control to optimize cutting parameters without prior material knowledge.

Contribution

It introduces a simulation-based learning approach for passive variable interaction control in robotic milling, enabling adaptation to unknown materials during disassembly tasks.

Findings

Minimizes process force and deviations similar to offline methods

Achieves cutting times within 25% of optimal

Requires no prior material knowledge

Abstract

This paper addresses the problem of robotic cutting during disassembly of products for materials separation and recycling. Waste handling applications differ from milling in manufacturing processes, as they engender considerable variety and uncertainty in the parameters (e.g. hardness) of materials which the robot must cut. To address this challenge, we propose a learning-based approach incorporating elements of interaction control, in which the robot can adapt key parameters, such as feed rate, depth of cut, and mechanical compliance during task execution. We show how a mathematical model of cutting mechanics, embedded in a simulation environment, can be used to rapidly train the system without needing large amounts of data from physical cutting trials. The simulation approach was validated on a real robot setup based on four case study materials with varying structural and mechanical properties. We demonstrate the proposed method minimises process force and path deviations to a level similar to offline optimal planning methods, while the average time to complete a cutting task is within 25% of the optimum, at the expense of reduced volume of material removed per pass. A key advantage of our approach over similar works is that no prior knowledge about the material is required.