自主抹灰的软机器人方法

Marsela Polic, Bruno Maric, M. Orsag
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引用次数: 0

摘要

本文提出了一种工业软机器人应用于复杂形状表面的自主抹灰,采用协同工业机械手。该系统的核心是基于深度学习的软体建模,即柔性抹灰刀具的变形估计。估计依赖于视觉反馈和深度卷积神经网络(CNN)。在学习阶段开发了迁移学习方法和专门设计的数据集生成程序。抹灰刀的估计变形然后用于控制相对于处理表面的刀倾斜,作为抹灰过程中的基本控制变量之一。实验验证了所开发的系统,包括基于CNN的变形估计,以及它在刀倾角控制中的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Soft robotics approach to autonomous plastering
This paper presents an industrial soft robotics application for the autonomous plastering of complex shaped surfaces, using a collaborative industrial manipulator. In the core of the proposed system is the deep learning based soft body modeling, i.e. deformation estimation of the flexible plastering knife tool. The estimation relies on visual feedback and a deep convolution neural network (CNN). The transfer learning approach and specially designed dataset generation procedures were developed in the learning phase. The estimated deformation of the plastering knife is then used to control the knife inclination with respect to the treated surface, as one of the essential control variables in the plastering procedure. The developed system is experimentally validated, including both the CNN based deformation estimation, as well as its performance in the knife inclination control.
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