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\begin{document}

\begin{titlepage}
        \setcounter{page}{1}
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        \headsep 1.5cm
        \vskip -4cm
        \centerline{\Huge\bf Technische Universit\"at Braunschweig}
        \vskip 3.4cm
        \begin{center}
            \begin{minipage}[t][7cm][c]{13.5cm}
                \begin{center}
                    {\large{Teamprojekt}\par}
                    \vskip 0.5cm
		    {\LARGE\bf Koordinierte Ballsuche mit }
                    \vskip 0.25cm
                    {\LARGE\bf mobilen Robotern}
                    \vskip 0.5cm
                    {\Large\bf Martin Mikolas, Markus Reschke, Johannes
		    Starosta}
                    \vskip 0.25cm


                    {\large\bf Betreuer:
                    \begin{tabular}[t]{c}{René Iser}\end{tabular}}
                    \vskip 0.75cm
                    {\large\bf{\today}\par}
                \end{center}
            \end{minipage}
        \end{center}
        \vskip 1.5cm
        \begin{figure}[h]
            \begin{center}
							\includegraphics{TU-Logo}
            \end{center}
        \end{figure}



        \vskip 1cm
        \centerline{\LARGE\bf Institut f\"ur Robotik und Prozessinformatik}
            \vskip 1cm
        \centerline{\LARGE\bf Prof.~Dr.~F.~Wahl}




    \end{titlepage}

    \declaration{den \today}

    \setcounter{footnote}{0}
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    \begin{abstract}Bei der vorliegenden Ausarbeitung handelt es sich
      um die Dokumentation unseres Teamprojektes zur ,,Koordinierten
      Suche mit mobilen Robotern''. Wir werden zunächst die
      Vorarbeiten vorstellen, auf denen wir aufbauen, bevor wir unser
      System näher vorstellen. Zum Schluss folgt noch eine kritische
      Bewertung unserer Ergebnisse.
    \end{abstract}

%    \raisebox{-12cm}{\hspace{5cm}\huge \textsc{To Loriot}}
 %   \newpage

%    \markright{Danksagungen}
%    \quote{ { \bf \Large  Danksagungen } \originalTeX
%    \\
%    \\
%    Unser Teamprojekt wäre nicht möglich gewesen, wenn wir nicht auf
%    wesentliche Vorarbeiten hätten zirück
%    I am very grateful to many people who helped me during my thesis in various
%    manners.
%    First, I wish to thank the entire staff of the Institute for Robotics and
%    Process Control for their helpful discussions and the truly great working atmosphere.
%    Particularly, I would like to thank Erich Kozlowski for his
%    helpful advice and tremendous encouragement during my student jobs at the Institute
%    and especially throughout this work.
%    Moreover, I am indebted to my family and my friends for their overwhelming
%    support throughout the last years.
%    Furthermore, I wish to thank Hans Meiser and Birte Karalus for their advice on
%    language issues.
%    Finally, I would like to thank the Institut für Mathematische
%    Stochastik, the Institute for Computational Mathematics, and the
%    Institute for Communications Technology for their kind provision
%    of literature.
%    \\ } { Walter Horstmann }


    \tableofcontents
    \listoffigures 
    \lstlistoflistings
    %\listoftables 
    \newpage 
%     \begin{minipage}{16cm} 
%         {\bf \Large Nomenclature} 
%         \\\\\\ 
 
%         {\bf \large Greek Letters\\\\} 
%         \begin{tabular}{ll} 
%                 $\ve{\alpha}$ & Vector denoting the angular acceleration of the sensor frame\\ 
%                 $\alpha_{i}$ & DH-parameter\\ 
%                 $\beta$ & Fudge factor employed in the APF and the DUKF\\ 
%                 $\ve{\chi}$ & Noise sample function\\ 
%                 $\Sigma$& $\Sigma$-points of the UKF to the true noise distribution\\ 
%                 $\delta$ & Fudge factor\\ 
%                 $\Delta$ & Variable denoting a difference\\ 
%                 $\Delta \gamma$ & Angle of rotation around $\ve{\omega}$\\ 
%                 $\Delta t$ & Interval between two sampling points\\ 
%                 $\eta$ & Scaling parameter of the UKF\\ 
%                 $\gamma$ & Influence factor of the Robbins-Monroe update scheme\\ 
%                 $\lambda$ & Forgetting factor in the RLS algorithm; scaling parameter of the DUKF\\ 
%                 $\omega$ & Vector denoting the angular velocity of the sensor frame\\ 
%                 $\omega_{i}$ & Joint angular velocity\\ 
%                 $^{i}\omega_{i}$ & Vector denoting the angular velocity vector of link $i$\\ 
%                 $\ma{\Omega}$ & Matrix parameterizing a sinusoidal 
%                 trajectory\\ 
%                 $\ve{\varphi}$ & Vector containing the inertial 
%                 parameters of a load\\ 
%                 $\ve{\varphi^{dyn}}$ & Inertial parameter vector containing 
%                 all ten inertial parameters\\ 
%                 $\ve{\varphi_{ext}}$ & Inertial parameter vector augmented by 
%                 the force/torque offsets\\ 
%                 $\ve{\varphi^{sta}}$ & Inertial parameter vector containing 
%                 the mass and the products of the mass and the COM coordinates\\ 
%                 $\rho$ & Fudge factor in the $MAD$ calculation\\ 
%                 $\varsigma$ & Factor employed in trajectory 
%                 optimization\\ 
%                 $\sigma$ & Variance of the prediction error; singular value\\ 
%                 $\ve\sigma$ & $\Sigma$-points that predict the 
%                 measurements\\ 
%                 $\ve\sigma$ & $\Sigma$-point that predict the 
%                 measurements\\ 
%                 $\ve\sigma$ & $\Sigma$-point describing the state 
%                 and its prediction\\ 
%                 $\tau$ & Threshold\\ 
%                 $\theta$ & DH-parameter; fudge factor\\ 
%                 $\ve{\upsilon_{k}^{i}}$ & Particle\\ 
%                 $\xi_{i,k}$ & Factor weighting the sine part of 
%                 the sinusoidal joint angle function of joint $i$\\ 
%                 $\zeta_{i,k}$ & Factor weighting the cosine part of 
%                 the sinusoidal joint angle function of joint $i$\\ 
%             \end{tabular} 
%                 \end{minipage} 
% \begin{minipage}{16cm} 
%                 {\bf \Large Nomenclature} 
%                 \\\\\\ 
%                 {\bf \large Roman Letters\\\\} 
%                 \begin{tabular}{ll} 
%                 $\ve{a}$ & Vector denoting the linear acceleration of the sensor frame\\ 
%                 $a_{i}$ & DH-parameter\\ 
%                 $\ma{B}$ & State transition matrix of the Kalman 
%                 filter\\ 
%                 $\ve{c}$ & Coordinates of the center of mass of the load w.r.t. the sensor frame\\ 
%                 $c(P)$ & Constraints function\\ 
%                 $\ma{C}$ & Information matrix\\ 
%                 $d_{i}$ & DH-parameter\\ 
%                 $diag(\ma{X})$ & diagonal matrix with the elements of 
%                 $\ma{X}$ on its main diagonal\\ 
%                 $e$ & Prediction error of the Kalman filter and 
%                 LS-based identification algorithms\\ 
%                 $|\ve{e_{rel}}|$ & absolute value of the relative 
%                 parameter error\\ 
%                 $E[x]$ & Expected value of $x$\\ 
%                 $\ma{E}$ & Error matrix of the identification variables 
%                 according to error model B\\ 
%                 $f$ & frequency\\ 
%                 $f_{exc,i}$ & base frequency of superposed sinusoidal 
%                 functions describing the rotation of joint $i$\\ 
%                 $f_{exc,min}$ & minimum base frequency of the manipulator joints\\ 
%                 $f_{s}$ & Sampling frequency\\ 
%                 $\ve{f}$ & Vector denoting the force exerted by the sensor on the load\\ 
%                 $\ve{f_{o}}$ & Force offset\\ 
%                 $\ma{F_{des}}$ & desired object frame\\ 
%                 $\ma{F_{est}}$ & estimated object frame\\ 
%                 $\ve{g}$ & Gravity vector w.r.t. to the sensor frame\\ 
%                 $g_{0}$ & gravitational constant \\ 
%                 $\ma{I}$ & Identity matrix\\ 
%                 $\ma{J}$ &\begin{math}\ma{J}=\left(\begin{array}{lll}j_{xx} & j_{xy} & j_{xz}\\j_{xy} & j_{yy} & j_{yz}\\j_{xz} & j_{yz} & j_{zz} 
%                 \\\end{array}\right)\end{math}  Inertia matrix expressed w.r.t. the sensor coordinate frame\\ 
%                 $\ma{J_{c}}$ & Inertia matrix expressed w.r.t. the center of mass 
%                         of the load\\ 
%                 $\ma{J_{kk}}$ & Moment of inertia around the $k$-axis\\ 
%                 $\ma{J_{kl}}$ & Product of inertia w.r.t. the 
%                     $k,l$-plane\\ 
%                 $m$ & Mass of the load\\ 
%                 $\ve{m}$ & Vector denoting the torque exerted by 
%                 the sensor on the load\\ 
%                 $\ve{m_{o}}$ & Torque offset\\ 
%                 $N$ & Variable denoting quantities\\ 
%                 $orth(\ma{X})$ & Orthonormal basis of matrix X\\ 
%                 $p(x)$ & probability that $x$ occurs\\ 
%                 $\ve{p\;^{load,i}_{s}}$ & Point $i$ of the load 
%                 bounding box expressed w.r.t. the sensor frame\\ 
%                 $\ma{P}$ & Error covariance matrix of the Kalman filter 
%                 and RLS-based methods\\ 
%                 $q_{i}$ & Joint angle of joint $i$\\ 
%                 $\dot{q}_{i}$ & Joint angular velocity of joint 
%                 $i$\\ 
%                 $\ddot{q}_{i}$ & Joint angular acceleration of joint 
%                 $i$\\ 
%                 $q_{i,0}$ & Initial joint angle of joint $i$\\ 
%                 $q_{ii}$ & Element of the process noise covariance 
%                 matrix\\ 
%                 $\ma{Q}$ & Process noise covariance matrix\\ 
%                 $\ve{r}$ & Vector pointing from the origin of the base 
%                       reference frame to the center of mass of the 
%                       load\\ 
%                 $^{j}_{i}\!R$ & rotation matrix relating the 
%                 frames $i$ and $j$\\ 
%                 $r_{ii}$ & Element of the measurement noise covariance 
%                 matrix\\ 
%                 $\ma{R}$ & Measurement noise covariance matrix\\ 
%                 $s$ & sample standard deviation\\ 
%                 $t$ & Point in time or period of time\\ 
%                 $t_{acq}$ & Acquisition time of measurements for sensor resets\\ 
%                 $t_{c}$ & control period of the position 
%                 controller\\ 
%                 $t_{max}$ & maximum identification time\\ 
%                 $t_{s}$ & Sampling period\\ 
%                 \end{tabular} 
%                 \end{minipage} 
% \begin{minipage}{16cm} 
%                 {\bf \Large Nomenclature} 
%                 \\\\\\ 
%                 {\bf \large Roman Letters\\\\} 
%                 \begin{tabular}{ll} 
%                 $t_{sta}$ & Period of time after which JR3 sensor 
%                 temperature has stabilized\\ 
%                 $\ma{^{i}_{j}T}$ & homogeneous transformation matrix 
%                 relating frame $i$ and $j$\\ 
%                 $\ma{U}$ & Left singular matrix\\ 
%                 $\ve{v}$ & Measurement noise vector of the Kalman 
%                 filter\\ 
%                 $\ma{V}$ & Right singular matrix\\ 
%                 $\ve{w}$ & Process noise vector of the Kalman 
%                 filter\\ 
%                 $\ma{W}$ & Instrumental variables matrix\\ 
%                 $\ve{x}$ & State of the Kalman filter, the DUKF, and the APF\\ 
%                 $\ve{y}$ & Measurement vector of the Kalman 
%                 filter\\ 
%                 \end{tabular} 
%                 \end{minipage} 
 
\setstretch{1.5}
\input{einleitung}%worum gehts? 
\input{hauptteil}%Wie haben wir die Aufgabe gelöst? 
                 %Softwarearchitektur!  
                 % - client: inklusive SonarParticelFilter, Balldetection 
                 % - server: inklusive Bahnplanung und ggf. weiterer 
                 % Algorithmen 
                 % 
\input{evaluation}%Beschreibung einiger Versuche (sowohl erfolgreich), 
                  %als auch der fails (Lokalisierung nur in Mitte des 
                  %Raumes zuverlässig, Balldetection gibt 
                  %falsePositives wenn weißes Licht zuviele rote 
                  %Partikel enthält) 
\input{zusammenfassung} % Zusammenfassung halt :) 
%\newpage
%\setstretch{1}
\nocite{Hart1968}
\nocite{Shinners2011}
\nocite{wiki:astern}
\nocite{p3dxmanual}
\nocite{tbr2011}
% \appendix
\bibliographystyle{plain}

\bibliography{literatur}
\addcontentsline{toc}{chapter}{Literaturverzeichnis}%Eintrag} 
% \begin{appendix} 
% dazu anhang mit installationsanleitung und ggf. weiteren Kram. 
%\include{buildsystem} 
%\end{appendix} 
%\nocite{*}
%\printbibliography
%literatur? Vielleicht die quelle des bahnplanungsalgorithmuses? 
%\begin{thebibliography}{99} 
%        \addcontentsline{toc}{chapter}{Bibliography} 
%        \bibitem[HORST06]{horst}\textsc{Horstmann, Werner}: 
%        \textsl{Das Fressverhalten der gemeinen Steinlaus bei Gegenlicht}. 
%        Springer, Berlin, March 2006. 
% 
% 
%\end{thebibliography} 
 
 
 
\end{document} 
 
 
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