X-Git-Url: http://git.rohieb.name/bachelor-thesis/written-stuff.git/blobdiff_plain/656ed2ada1064d9874a45e1f55bba8cea4d01d6f..cbbf1119f4b08f1a8d8eef497380c589778c5068:/Ausarbeitung/experiment2.tex

diff --git a/Ausarbeitung/experiment2.tex b/Ausarbeitung/experiment2.tex
index 1dbe336..807b9f5 100644
--- a/Ausarbeitung/experiment2.tex
+++ b/Ausarbeitung/experiment2.tex
@@ -1,7 +1,8 @@
 \chapter{Experiment 2: Movement Behaviour with Mean Correction}
 \todo{}
+\label{sec:exp2}
 
-As presumed in section \ref{exp1:results}, errors in the Roomba's movements
+As presumed in Section \ref{exp1:results}, errors in the Roomba's movements
 could originate from imprecise measurement of the Roomba's internal sensors or
 in the Wiselib implementation. So a natural approach to correct this sort of
 errors would be to average the results for each data point from Experiment 1,
@@ -13,45 +14,43 @@ the target distance resp. the target angle was adjusted, while the velocity
 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!]
  \centering
  \includegraphics[width=\textwidth]{images/iz250flur_drive-mean_data.pdf}
  \caption{Behaviour with mean correction on laminated floor, straight drive
-tests}
+movements}
 \end{figure}
 \begin{figure}[p!]
  \centering
  \includegraphics[width=\textwidth]{images/iz250flur_turn-mean_data.pdf}
- \caption{Behaviour with mean correction on laminated floor, turn tests}
+ \caption{Behaviour with mean correction on laminated floor, turn movements}
 \end{figure}
 \begin{figure}[p!]
  \centering
  \includegraphics[width=\textwidth]{images/seminarraum_drive-mean_data.pdf}
  \caption{Behaviour with mean correction on carpet floor, straight drive
-tests}
+movements}
 \end{figure}
 \begin{figure}[p!]
  \centering
  \includegraphics[width=\textwidth]{images/seminarraum_drive-mean_data.pdf}
- \caption{Behaviour with mean correction on carpet floor, turn tests}
+ \caption{Behaviour with mean correction on carpet floor, turn movements}
 \end{figure}
 
 results better than in experiment 1, very accurate for laminate floor, carpet
 floor more spread but still kind of in the middle and less deviation from ideal
 value.
-\todo{statistical values, stddev?}
+\todo{!!!}
\ No newline at end of file