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by Ian Sillitoe, Tapio Elomaa
http://www.cs.helsinki.fi/TR/O/O-1994-14.ps.gz
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Abstract:
This paper describes the use of the C4.5 decision tree learning algorithm in the design of a classifier for a new approach to the mapping of a mobile robot's local environment. The decision tree uses the features from the echoes of an ultrasonic array mounted on the robot to classify the contours of its local environment. The contours are classified into a finite number of two dimensional shapes to form a primitive map which is to be used for navigation. The nature of the problem, noise and the practical timing constraints, distinguishes it from those typically used in machine learning applications and highlights some of the advantages of decision tree learning in robotic applications. 1
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