Aaj. Millan et D. Floreano, Efficient learning of variable-resolution cognitive maps for autonomous indoor navigation, IEEE ROBOT, 15(6), 1999, pp. 990-1000
This paper presents an adaptive method that allows mobile robots to learn c
ognitive maps of indoor environments incrementally and on-line. Our approac
h models the environment by means of a variable-resolution partitioning tha
t discretizes the world in perceptually homogeneous regions. The resulting
model incorporates both a compact geometrical representation of the environ
ment and a topological map of the spatial relationships between its obstacl
e-free areas. The efficiency of the learning process is based on the use of
local memory-based techniques for partitioning and of active learning tech
niques for selecting the most appropriate region to be explored nest. In ad
dition, 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 obstacles are parallel or perpendicular to e
ach other. This results in variable-resolution partitionings consisting of
simple rectangular partitions and reduces the complexity of treating the un
derlying geometrical properties.