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：本文针对产品拆解过程中的机器人切割问题展开研究，旨在实现材料分离与回收。与制造过程中的铣削加工不同，废弃物处理应用面临显著的材料参数（如硬度）多样性与不确定性挑战。为解决该问题，我们提出一种融合交互控制思想的基于学习的方法，使机器人能够在任务执行过程中自适应调整进给速率、切削深度和机械柔顺性等关键参数。研究表明，嵌入仿真环境的切削力学数学模型可用于快速训练系统，无需从物理切割试验中获取大量数据。基于四种具有不同结构与力学特性的案例材料，我们在真实机器人平台上验证了该仿真方法的有效性。实验证明，所提方法能将过程力与路径偏差最小化至与离线最优规划方法相当的水平，同时完成切割任务的平均时间控制在最优值的25%以内，但代价是单次材料去除量有所降低。与同类研究相比，本方法的核心优势在于无需预先获取材料特性知识。