zwischenstand, +makeindex"

[bachelor-thesis/written-stuff.git] / Ausarbeitung / experiment2.tex

diff --git a/Ausarbeitung/experiment2.tex b/Ausarbeitung/experiment2.tex
index 4dc3386..1dbe336 100644 (file)
--- a/Ausarbeitung/experiment2.tex
+++ b/Ausarbeitung/experiment2.tex
@@ -1,37 +1,54 @@
 \chapter{Experiment 2: Movement Behaviour with Mean Correction}
 \todo{}
 
-\section{Setup}
-Fit function from data in experiment 1; algorithm determined target value for
-distance from fit function and input distance and input velocity.
-
-approach: correct imprecise measurement
+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.
 
-Setup was the same as in experiment 1. Application on netbook was
-"`mean\_correction\_test"', same procedure as in first experiment.
+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.
 
-expectations \& were they fulfilled?
+\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
+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.
 
 \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
+tests}
 \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 tests}
 \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
+tests}
 \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 tests}
 \end{figure}
 
 results better than in experiment 1, very accurate for laminate floor, carpet