Error Modeling for Automated Construction Equipment

This paper introduces the statistical error modeling approach for a computercontrolled large-scale manipulator (LSM). The LSM is sufficiently representative of several types of construction equipment to be able to serve as a general test bed. In the analysis, three factors which are measurable in real time: distance, hydraulic pressure, and payload, were varied to determine their influence on position errors in the LSM. It was shown that with an integrated multi-variable regression model, about 30% of the mean positioning error of the LSM can be reduced without the mid of fixed external reference systems.