GPGPU acceleration of a novel calibration method for industrial robots

Abstract

Radial Basis Function (RBF) neural networks have strong engineering applications. The training of these networks however can be time consuming. In this paper, we examine the calibration of a MOTOMAN industrial robot using an RBF based network. Additionally we examine the acceleration of RBF network using general purpose graphical processing units (GPGPUs). On a data set of 1989 calibration points, we are able to achieve a speedup of over 300 times compared to a MATLAB simulation of the algorithm. Given that MATLAB's simulation required over a week of runtime, the GPGPU acceleration enables reasonable training time for datasets with more calibration points, thus providing better precision.