Neural Computation, Vol 9, 683-699, Copyright © 1997 by The MIT Press
A Mobile Robot that Learns its Place
Sageev Oore, Geoffrey E. Hinton and Gregory Dudek
We show how a neural network can be used to allow a mobile robot to derive
an accurate estimate of its location from noisy sonar sensors and noisy
motion information. The robot's model of its location is in the form of a
probability distribution across a grid of possible locations. This
distribution is updated using both the motion information and the
predictions of a neural network that maps locations into likelihood
distributions across possible sonar readings. By predicting sonar readings
from locations, rather than vice versa, the robot can handle the very
nongaussian noise in the sonar sensors. By using the constraint provided by
the noisy motion information, the robot can use previous readings to
improve its estimate of its current location. By treating the resulting
estimates as if they were correct, the robot can learn the relationship
between location and sonar readings without requiring an external
supervision signal that specifies the actual location of the robot. It can
learn to locate itself in a new environment with almost no supervision, and
it can maintain its location ability even when the environment is
nonstationary.
