Iterative offline trajectory correction based on dynamic model for compensating robot-dependent errors in robotic machining

被引:0
|
作者
Dambly, Valentin [1 ]
Olivier, Bryan [2 ]
Riviere-Lorphevre, Edouard [1 ]
Ducobu, Francois [1 ]
Verlinden, Olivier [2 ]
机构
[1] Univ Mons, Machine Design & Prod Engn Unit, Pl Parc 20, B-7000 Mons, Belgium
[2] Univ Mons, Theoret Mech Dynam & Vibrat Unit, Pl Parc 20, B-7000 Mons, Belgium
关键词
Robotic machining; Offline compensation; Modelling; Trajectory generation; OPTIMIZATION; MECHANICS;
D O I
10.1016/j.rcim.2025.102960
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
As manufacturing demands shift towards enhanced part geometries and materials, the need for flexibility in production has driven interest in robotic machining. This fast-growing technology offers advantages like cost-effectiveness, adaptability, and easy deployment, making it suitable for agile production lines. However, robotic machining encounters accuracy challenges due to inherent robot flexibility, causing deviations and vibrations. The positioning error along a robotic machining trajectory is composed of two contributions: the steadystate error and the transient. This research addresses these challenges through compensation methods based on a robotic cell equipped with a St & auml;ubli TX200 and its digital shadow. By proposing trajectory corrections based on the results from virtual machining simulator including the robot dynamical model, the study aims to compensate the static and dynamic deviations, responsible for steady-state and transient errors respectively. To achieve this, the trajectory is discretised in elementary sections, modelled with Hermite splines and connected by nodes that are iteratively repositioned in space based on the error estimated from the dynamics simulation and weighted along the tool path. Simulations and experiments are carried out in Aluminium 6082 to demonstrate the gain of iterative compensation algorithm. The error reduction encountered in simulation is successfully confirmed in experimental cases, within the repeatability tolerance of the robot, decreasing the steady-state error by 90% and about 60% in transient phases.
引用
收藏
页数:24
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