X-Git-Url: http://git.rohieb.name/bachelor-thesis/written-stuff.git/blobdiff_plain/df75c351fb7b1deeb49fa444b1bef90e13c76290..34498d8190651a7dbf969da9b03d01b8cf085ed4:/Ausarbeitung/experiment2.tex

diff --git a/Ausarbeitung/experiment2.tex b/Ausarbeitung/experiment2.tex
index c575e4d..08816c0 100644
--- a/Ausarbeitung/experiment2.tex
+++ b/Ausarbeitung/experiment2.tex
@@ -1,35 +1,56 @@
 \chapter{Experiment 2: Movement Behaviour with Mean Correction}
-\todo
+\todo{}
+\label{sec:exp2}
 
-\section{Setup}
-Fit function from data in experiment 1; algorithm determined target value for
-distance from fit function and input distance and input velocity.
+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,
+find a function that fits the mean measured error depending of the
+target velocity and target distance or angle as well as possible, and then
+adapting either one of the target parameters so that the resulting movement
+would most likely be the desired target value. In this experiment however, only
+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 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.
 
-Setup was the same as in experiment 1. Application on netbook was
-"`mean\_correction\_test"', same procedure as in first experiment.
+\section{Setup}
+The hardware setup was exactly the same as in Experiment 1. However, in this
+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 value according to the method described above.
 
 \section{Results}
-\begin{figure}
+\begin{figure}[p!]
  \centering
  \includegraphics[width=\textwidth]{images/iz250flur_drive-mean_data.pdf}
- \caption{foo}
+ \caption{Behaviour with mean correction on laminated floor, straight drive
+movements}
 \end{figure}
-\begin{figure}
+\begin{figure}[p!]
  \centering
  \includegraphics[width=\textwidth]{images/iz250flur_turn-mean_data.pdf}
- \caption{foo}
+ \caption{Behaviour with mean correction on laminated floor, turn movements}
 \end{figure}
-\begin{figure}
+\begin{figure}[p!]
  \centering
  \includegraphics[width=\textwidth]{images/seminarraum_drive-mean_data.pdf}
- \caption{foo}
+ \caption{Behaviour with mean correction on carpet floor, straight drive
+movements}
 \end{figure}
-\begin{figure}
+\begin{figure}[p!]
  \centering
  \includegraphics[width=\textwidth]{images/seminarraum_drive-mean_data.pdf}
- \caption{foo}
+ \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.
+floor more spread but still kind of in the middle and less deviation from ideal
+value.
 \todo{statistical values, stddev?}
