Abstract
The use of an evolutionary method for robot localization is explored. We use genetic programming to evolve an inverse function mapping sensor readings to robot locations. This inverse function is an internal model of the environment. The robot senses its environment using dense distance information which may be obtained from a laser range finder. Moments are calculated from the distance distribution. These moments are used as terminal symbols in the evolved function. Arithmetic, trigonometric functions and a conditional statement are used as primitive functions. Using this representation we evolved an inverse function to localize a robot in a simulated office environment. Finally, we analyze the accuracy of the resulting function.
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Ebner, M. (1999). Evolving an Environment Model for Robot Localization. In: Poli, R., Nordin, P., Langdon, W.B., Fogarty, T.C. (eds) Genetic Programming. EuroGP 1999. Lecture Notes in Computer Science, vol 1598. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48885-5_15
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DOI: https://doi.org/10.1007/3-540-48885-5_15
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