Robots have been successfully deployed in both traditional and novel manufacturing processes. However, they are still difficult to program by non-experts, which limits their accessibility to a wider range of potential users. Programming robots requires expertise in both robotics and the specific manufacturing process in which they are applied. Robot programs created offline often lack parameters that represent relevant manufacturing skills when executing a specific task. These skills encompass aspects like robot orientation and velocity. This paper introduces an intuitive robot programming system designed to capture manufacturing skills from task demonstrations performed by skilled workers. Demonstration data, including orientations and velocities of the working paths, are acquired using a magnetic tracking system fixed to the tools used by the worker. Positional data are extracted from CAD/CAM. Robot path poses are transformed into Cartesian space and validated in simulation, subsequently leading to the generation of robot programs. PathML, an AutomationML-based syntax, integrates robot and manufacturing data across the heterogeneous elements and stages of the manufacturing systems considered. Experiments conducted on the glass adhesive application and welding processes showcased the intuitive nature of the system, with path errors falling within the functional tolerance range.

翻译：机器人已成功应用于传统及新型制造工艺中。然而，非专业人员仍难以对其进行编程，这限制了其在更广泛潜在用户中的可及性。机器人编程需要同时掌握机器人学及其所应用的特定制造工艺的专业知识。离线创建的机器人程序在执行具体任务时，往往缺乏表征关键制造技能的参数。这些技能涵盖机器人姿态与运动速度等方面。本文提出一种直观的机器人编程系统，旨在从熟练工人的任务演示中获取制造技能。通过固定在工人所用工具上的磁追踪系统，采集包含工作路径姿态与速度的演示数据。位置数据则从CAD/CAM系统中提取。机器人路径位姿经笛卡尔空间转换并在仿真中验证，最终生成机器人程序。基于AutomationML的语法框架PathML，将机器人数据与制造数据集成到所构建制造系统的异构组件及多阶段流程中。在玻璃胶粘接与焊接工艺中开展的实验表明，该系统具有直观易用的特性，其路径误差均保持在功能公差范围内。