Abstract. In order to improve self-localization accuracy we are exploring ways of mutual localization in a team of autonomous robots. Detecting team mates visually usually leads to inaccurate bearings and only rough distance estimates. Also, visually identifying teammates is not possible. Therefore we are investigating methods of gaining relative position information acoustically in a team of robots. The technique introduced in this paper is a variant of code-multiplexed communication (CDMA, code division multiple access). In a CDMA system, several receivers and senders can communicate at the same time, using the same carrier frequency. Well-known examples of CDMA systems include wireless computer networks and the Global Positioning System, GPS. While these systems use electro-magnetic waves, we will try to adopt the CDMA principle towards using acoustic pattern recognition, enabling robots to calculate distances and bearings to each other. First, we explain the general idea of cross-correlation functions and appropriate signal pattern generation. We will further explain the importance of synchronized clocks and discuss the problems arising from clock drifts. Finally, we describe an implementation using the Aibo ERS-7 as platform and briefly state basic results, including measurement accuracy and a runtime estimate. We will briefly discuss acoustic localization in the specific scenario of a RoboCup soccer game.
On the way to the big goal-the game against the human world champion on a real soccer field-the configuration of the soccer fields in RoboCup has changed during the last years. There are two main modification trends: The fields get larger and the number of artificial landmarks around the fields decreases. The result is that a lot of the methods for self-localization developed during the last years do not work in the new scenarios without modifications. This holds especially for robots with a limited range of view as the probability for a robot to detect a landmark inside its viewing angle is significantly lower than on the old fields. On the other hand the robots have more space to play and do not collide as often as on the small fields. Thus the robots have a better idea of the courses they cover (odometry has higher reliability). This paper shows a method for self-localization that is based on bearings to horizontal landmarks and the knowledge about the robots movement between the observation of the features.
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