Statistical performance evaluation of the S-model arm signature identification technique

Abstract

The authors apply Monte Carlo simulation techniques to gain further insight into the relationship between manufacturing errors and the performance of a robot using either the design model or arm signature model for control. In conventional design-model robot control, manufacturing errors contribute most to robot positioning errors. The authors relate the statistical parameters which characterize the manufacturing error probability distribution functions. In arm signature-based robot control (S-model), the correct arm signature model eliminates kinematic errors due to manufacturing. In this case, robot performance is limited by sensor errors which contribute to inaccuracy of the identified arm signature model. The relationship between the statistical parameters which characterize a robot's positioning accuracy to the statistical parameters which characterize the sensor performance is presented. The authors analyze and quantify the requirements of an arm signature identification system in terms of the underlying sensor performance.