\chapter{Introduction}
-\todo{motivation, importance of dead reckoning, what was done?}
+The world nowadays depends more and more on robots. But not only stationary
+robots, like those used in assembly lines, improve our lives and make it
+possible to excel at industrial and exploratory challenges. Also, mobile robots
+could be an integral part of our world in the near future. Not only are they
+predestined for dangerous duties which would much certainly cost lives if
+humans had to do them, like detecting disarming mines in war zones, they are
+also much more specialized to their challenges.
-\chapter{Related work}
-Borenstein, Feng \cite{umbmark}: Benchmark for mobile robots
+However, in most cases, mobile robots become useless if they don't know anything
+about their environment. Especially their location is a basic to know,
+and particularly autonomous mobile robots benefit from the knowledge about their
+location. This knowledge gives them the ability to create a map of their
+environment, to find ways between certain points of interest, and to contact
+both human beings as well as other robots.
-Yamauchi \cite{yamauchi}: Dead-reckoning with Evidence Grids
+There are several methods to achieve knowledge about the location of mobile
+robots. At first, they can be classified as absolute or relative methods. For
+example, absolute positioning methods rely on a fixed, distributed
+infrastructure which allows the robot to determine its absolute location inside
+a specific region. For example, the \ac{GPS} only works because satellites orbit
+around the earth and constantly send a signal. There are also approaches using
+navigation beacons or by matching the observed environment to a previously
+known map.
-Chong, Kleeman \cite{chongkleeman}: High-precision odometry
+All of that positioning methods however depend on the infrastructure, which can
+be expensive to deploy and maintain. Also, most of the algorithms used are
+fairly complex and need highly specialized hardware or a certain computing
+power, and therefore cannot (yet) be used in real time. And sometimes, they are
+even more imprecise than relative approaches.~\cite{umbmark}
-\todo{}
\ No newline at end of file
+Relative positioning methods however only yield a position relative to a
+defined point, and mostly can be applied with cheap hardware. The best-known
+approach in this field is \definition{dead reckoning}, which computes the
+position relative to an initial starting point by considering the covered
+distance and course the robot has moved. In the case of mobile robots, the
+covered distance can be simply computed in real time from the revolution of its
+wheels, or by accelerometers the robot may be equipped with.
+
+The advantage of dead reckoning is that it is easy and fast to implement.
+However, there is also a drawback. The vehicle's current position is based on
+its previous position, and distance and course measurement is mostly imprecise.
+the error of the calculated position grows with time. In order to maintain a
+certain accuracy, countermeasures need to be taken to keep the error as small as
+possible.
+
+In this thesis, the popular Roomba manufactured by the iRobot Corporation is
+used as an instance for an mobile robot, since it is cheap, small, and can be
+simply controlled over a serial interface. The Roomba is accompanied with a
+netbook sitting on top of it, on which runs an application to represent the
+autonomous aspect. This application in turn uses the Wiselib library to control
+the Roomba.
+
+In the first experiment, the Roomba's movement behaviour will be analyzed. Then,
+two different easy approaches for minimizing the positioning error are presented
+and tested for their suitability.
+
+\section{Related Work}
+There are already some results on the problem. Yamauchi proposes to use dead
+reckoning with evidence grids to represent the robot's view of the
+world.~\cite{yamauchi} Evidence grids are subdivisions of the Cartesian space
+into cells, and each cell is assigned a probability value representing the
+estimated probability of occupancy by the robot. The robot now explores its
+environment, using its sensors to detect moving and fixed obstacles, and finally
+tries to find its home location again using dead reckoning. On its way back, it
+creates a second evidence grid. Finally, both grids are incorporated by an
+algorithm, which estimates the error in orientation and translation.
+Experimental results with a real robot showed a maximum translation error of 28\%
+and an orientation error of 25\%.
+
+Chong and Kleeman use high-precision odometry to increase the accuracy
+of position estimation by dead reckoning.~\cite{chongkleeman} They succeeded in
+building a odometry system that is as good as the best reported system, but can
+be fabricated at low-cost.
+
+Borenstein and Feng design a benchmark for mobile robots.~\cite{umbmark} They
+show how to measure systematic and non-systematic errors, and how to correct
+them.
projects / bachelor-thesis / written-stuff.git / blobdiff