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Kalman Filtering approach for Localisation in RobotSoccer

Author(s):

S. Stüdli
Conference/Journal:

Discrete and Computational Geometry, Diploma/Master Thesis, SS 10 (10056)
Abstract:

Autonomous robots can be used in a wide range of applications, for example as a worker in a factory or for rescue missions in dangerous situations. To fulfil the different tasks the robot has to know its own position. This problem is well known as the self localisation problem and there are numerous algorithms for solving this problem. The most common approaches are the particle filter [1], which is based on Bayesian filtering, and the Kalman filter [2]. In this thesis the self localisation problem is investigated on a RoboCup soccer player. RoboCup is an international competition, in which teams from all around the world compete with each other. The RoboCup competition is divided in different leagues, where one of them is the RoboCup Soccer - Standard Platform League. In this league robots play in a soccer match against each other, where all the teams are obliged to use the same hardware. At the moment the hardware used is a humanoid robot, called Nao, produced by Aldebaran Robotics. The aim of this thesis is to improve the existing localisation, an unscented Kalman filter, as described in [3], [4], [5]. To do so the filter is combined with approaches called Covariance Intersection [6] and Cox’s algorithm [7]. The algorithms developed are first tested in a Matlab simulation. In a second step experimental data are used offline. In the last stage experimental tests are performed where the robot is programmed to walk to a specific position on the field. As soon as the robot thinks it is at the correct position, the error to the real position is measured. Based on these tests we show the advantages and disadvantages

Year:

2011
Type of Publication:

(12)Diploma/Master Thesis
Supervisor:

S. Summers

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% Autogenerated BibTeX entry
@PhdThesis { Xxx:2011:IFA_4098
}
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