Soft robotics approach to autonomous plastering

Abstract

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.