remained unadjusted.
Fitting the function\index{fit function} was done with \acs{GNU} R\index{GNU R}
-through a wrapper script which is explained in section~\ref{sec:impl:eval}. In
-this experiment, a linear fit of the form $o = a*v+b*i+c$ was used, with $o$
-being the measured value, $v$ the input velocity, $i$ the target distance or
-angle, and $a,b,c \in \mathbb{R}$. The fitted values \todo{how? least
-square?} for $a, b, c$ were then used in the algorithm to calculate the adapted
-target distance or angle.
+through the wrapper script \prog{graph.sh} which is explained in
+section~\ref{sec:impl:eval}. In this experiment, a 2-dimensional linear fit for
+the measured value was determined by the method of least squares, with target
+value (angle or distance) and velocity as input parameters. The fit function was
+then used in the algorithm to calculate the adapted target distance or angle.
\section{Setup}
The hardware setup was exactly the same as in Experiment 1. However, in this
-experiment the application \cmd{mean\_correction\_test} was used to measure
+experiment the application \prog{mean\_correction\_test} was used to measure
data. It did exactly the same as the application from Experiment 1, except that
-it adapted the target distance resp. target angle according to the algorithm
-described above.
+it adapted the target value according to the method described above.
\section{Results}
\begin{figure}[p!]
projects / bachelor-thesis / written-stuff.git / blobdiff