Offline robot programming assisted by task demonstration: an AutomationML interoperable solution for glass adhesive application and welding
<|reference_start|>Offline robot programming assisted by task demonstration: an AutomationML interoperable solution for glass adhesive application and welding: 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.<|reference_end|>
@article{babcinschi2024offline,
    title={Offline robot programming assisted by task demonstration: an
  AutomationML interoperable solution for glass adhesive application and
  welding},
    author={M. Babcinschi, F. Cruz, N. Duarte, S. Santos, S. Alves, P. Neto},
    journal={arXiv preprint arXiv:2405.13141},
    year={2024},
    doi={10.1080/0951192X.2024.2358042},
    archivePrefix={arXiv},
    eprint={2405.13141},
    primaryClass={cs.RO}
}