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Summary: 990 IEEE TRANSACTIONS ON ROBOTICS AND AUTOMATION, VOL. 15, NO. 6, DECEMBER 1999
Efficient Learning of Variable-Resolution Cognitive
Maps for Autonomous Indoor Navigation
Angelo Arleo, Jos´e del R. Mill´an, and Dario Floreano
Abstract--This paper presents an adaptive method that allows
mobile robots to learn cognitive maps of indoor environments
incrementally and on-line. Our approach models the environment
by means of a variable-resolution partitioning that discretizes the
world in perceptually homogeneous regions. The resulting model
incorporates both a compact geometrical representation of the
environment and a topological map of the spatial relationships
between its obstacle-free areas. The efficiency of the learning
process is based on the use of local memory-based techniques
for partitioning and of active learning techniques for selecting
the most appropriate region to be explored next. In addition, a
feed-forward neural network is used to interpret sensor readings.
We present experimental results obtained with two different
mobile robots, namely a Nomad 200 and a Khepera. The current
implementation of the method relies on the assumption that ob-
stacles are parallel or perpendicular to each other. This results in
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