First complete draft

documentation/appendix/colorpicker.tex

@@ -1,18 +1,18 @@
-\section{Color calibration}
+\section{Color Calibration}
 
 All our detection algorithms require color calibration, and when the lighting
-conditions on the field change, colors might have to be newly calibrated. For
-us this meant that a tool was necessary, that could simplify this process as
-far as possible. For this reason, we implemented a small OpenCV-based program,
-that we called \verb|Colorpicker|. This program can access various video
-sources, as well as use still images for calibration. The main interface
-contains the sliders for adjusting the HSV interval, as well as the video area,
+conditions on the field change, colors might have to be recalibrated. For us
+this meant that a tool was necessary, that could simplify this process as far
+as possible. For this reason, we implemented a small OpenCV-based program, that
+we called \verb|Colorpicker|. This program can access various video sources, as
+well as use still images for calibration. The main interface contains the
+sliders for adjusting the HSV interval, as well as the video area,
 demonstrating the resulting binary mask. The colors can be calibrated for three
 targets: ball, goal and field; and the quality of detection, depending on the
-chosen target is demonstrated. When the program is closed, the calibration
-values are automatically saved to the settings file \verb|nao_defaults.json|.
-The interface of the Colorpicker is demonstrated in the figure \ref{p figure
-  colorpicker}.
+chosen target is demonstrated in the tool's video area. When the program is
+closed, the calibration values are automatically saved to the settings file
+\verb|nao_defaults.json|. The interface of the Colorpicker is demonstrated in
+the figure \ref{p figure colorpicker}.
 
 \begin{figure}[ht]
   \includegraphics[width=\textwidth]{\fig colorpicker}

documentation/appendix/details.tex

@@ -1,6 +1,6 @@
-\chapter{Implementation details}
+\chapter{Implementation Details}
 
-\section{Code organization}
+\section{Code Organization}
 
 Our code is organized as a standard Python package. The following command can
 be used to make the robot run the whole goal scoring sequence:
@@ -32,7 +32,7 @@ The main logic of our implementation can be found in the following files:
     or video files.
 
   \item \verb|movements.py| implements convenience movements-related function,
-    such as walking and kick.
+    such as walking and also the kick.
 
   \item \verb|nao_defaults.json| stores all project-global settings, such as
     the IP-address of the robot, or color calibration results.

documentation/appendix/pov.tex

@@ -16,4 +16,6 @@ the playback speed of the videos needed to be adjusted afterwards using video
 editing programs. Furthermore, due to computational resource limitations of the
 Nao, the frames could have been captured only in low resolution. However, the
 quality of the resulting videos was sufficient for successful debugging and
-also for the presentation.
+also for the presentation. Some of the illustrations for this report, such as
+the figure \ref{p figure direct-approach} for example, were created with the
+help of those videos.

documentation/appendix/tts.tex

@@ -1,4 +1,4 @@
-\section{Text to speech}
+\section{Text to Speech}
 
 During the implementation of our solution for the objective stated in \ref{sec
   problem statement} we included suitable functions to get a feedback about
@@ -12,7 +12,7 @@ the program by running it in a separate thread. We also ensured, that the robot
 does not repeat the same sentence over and over again, if he remains in the
 same state.
 
-\section{Goal confirmation}
+\section{Goal Confirmation}
 
 It makes sense to let the robot check, if he has actually scored a goal after
 he performed a goal kick. We therefore implemented a simple goal confirmation