Many industries extensively use flexible materials. Effective approaches for handling flexible objects with a robot manipulator must address residual vibrations. Existing solutions rely on complex models, use additional instrumentation for sensing the vibrations, or do not exploit the repetitive nature of most industrial tasks. This paper develops an iterative learning control approach that jointly learns model parameters and residual dynamics using only the interoceptive sensors of the robot. The learned model is subsequently utilized to design optimal (PTP) trajectories that accounts for residual vibration, nonlinear kinematics of the manipulator and joint limits. We experimentally show that the proposed approach reduces the residual vibrations by an order of magnitude compared with optimal vibration suppression using the analytical model and threefold compared with the available state-of-the-art method. These results demonstrate that effective handling of a flexible object does not require neither complex models nor additional instrumentation.
翻译:许多行业广泛使用灵活材料。用机器人操纵器处理灵活物体的有效办法必须解决残余振动问题。现有解决办法依靠复杂的模型,使用额外的仪器来感测振动,或不利用大多数工业任务的重复性。本文件开发了一种迭代学习控制方法,仅使用机器人的感应传感器,共同学习模型参数和残余动态。随后,利用所学的模型设计最佳(PTP)轨迹,其中考虑到操纵器的残余振动、非线性运动体和联合界限。我们实验性地表明,拟议办法将残余振动减少数量级,而使用分析模型和与现有最新方法相比,将优化震动抑制减少3倍。这些结果表明,对灵活物体的有效处理不需要复杂的模型或额外的仪器。