X-Git-Url: http://git.rohieb.name/bachelor-thesis/written-stuff.git/blobdiff_plain/600380af57fe95132b89fbf21db1ef26258f955b..cbbf1119f4b08f1a8d8eef497380c589778c5068:/Ausarbeitung/experiment2.tex diff --git a/Ausarbeitung/experiment2.tex b/Ausarbeitung/experiment2.tex index ba75ff5..807b9f5 100644 --- a/Ausarbeitung/experiment2.tex +++ b/Ausarbeitung/experiment2.tex @@ -1,5 +1,6 @@ \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 could originate from imprecise measurement of the Roomba's internal sensors or @@ -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