Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Position estimation techniques for an autonomous mobile robot: a review
Handbook of pattern recognition & computer vision
Genetic programming II: automatic discovery of reusable programs
Genetic programming II: automatic discovery of reusable programs
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
Map learning and high-speed navigation in RHINO
Artificial intelligence and mobile robots
Image Map Correspondence for Mobile Robot Self-Location Using Computer Graphics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Behaviors for Environmental Modeling by Genetic Algorithm
Proceedings of the First European Workshop on Evolutionary Robotics
Autonome Mobile Systeme 1997, 13. Fachgespräch
Estimating the absolute position of a mobile robot using position probability grids
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Constructing maps for mobile robot navigation based on ultrasonic range data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Spatial learning for navigation in dynamic environments
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Cascaded Evolutionary Estimator for Robot Localization
International Journal of Applied Evolutionary Computation
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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.