| Popis: |
This research studied the possibilities for utilizing different a priori types of measuring algorithms in order to improve accuracy and reliability of automated machine control in road construction. Generally in measurement science different a priori types of algorithms have widely been used to estimate beforehand the propagation of randomly appearing errors. While the exact mathematical solutions for derivative measurement quantities, as well as the random errors of fundamental quantities are known, the random errors of derivative quantities can always be evaluated using stochastic mathematics. In principle, this type of stochastic model can give us the magnitude of the highest attainable accuracy in practical measurement situations. In our research, new stochastic models to determine the random errors of robot tachymeter in kinematic measurement situations were developed and introduced. The tested tachymeter was TRIMBLE ATS. The needed random errors for the test solution were determined based on the technical specifications obtained from Trimble. In addition, two different kinematic calibrations for the use of robot tachymeters for road construction automation were developed and executed. |
