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.

翻译：本文研究了产品拆解过程中用于材料分离与回收的机器人切割问题。废弃物处理应用与制造过程中的铣削不同，因为其加工材料参数（例如硬度）存在显著多样性和不确定性。为应对这一挑战，我们提出了一种融合交互控制要素的基于学习的方法，使机器人能够在任务执行过程中自适应调整关键参数，如进给速度、切削深度及机械柔顺性。我们展示了如何利用嵌入仿真环境的切削力学数学模型，无需大量物理切削试验数据即可快速训练系统。基于四种具有不同结构与力学特性的案例材料，在真实机器人平台上验证了该仿真方法。实验表明，所提方法能将过程力和路径偏差最小化到与离线最优规划方法相近的水平，同时完成切割任务的平均时间在最优值的25%以内，但代价是单次材料去除量减少。与同类研究相比，本方法的关键优势在于无需预先了解材料特性。