Learning Robotic Milling Strategies Based on Passive Variable Operational Space Interaction Control

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. Note to Practitioners—This work is motivated by challenges in emerging fields such as recycling of electric vehicles, where products such as batteries adopt a range of designs with varying physical geometry and materials. More generally, this applies when considering robotic disassembly of any unknown component where semi-destructive operations such as cutting are required. Product-to-product variation introduces challenges when planning cutting processes required to disassemble a component, as contemporary planning approaches typically require advance knowledge of the material properties, shape and desired path to select tool speed, feed and depth of cut. In this paper, we show a mathematical model of milling force embedded in a simulation environment can be used as a relatively inexpensive approach to simulate a broad spectrum of cutting processes the robot may encounter. This allows the robot to learn from experience a strategy that can select these key parameters of a milling task online without user assistance. We develop a framework for controlling a robot using this strategy that allows the stiffness of the robot arm to be modulated over time to best satisfy metrics of productivity (e.g. required cutting time), while maintaining safe interaction of the robot with its environment (e.g. by avoiding force limits), similarly to how a human operator can vary muscular tension to accomplish different tasks. We posit that the proposed method can substitute a trial-and-error strategy of selecting process parameters for disassembly of novel products, or integrated with existing planning approaches to adjust the parameters of milling tasks online.