Title:
Modeling Cross-Sensory and Sensorimotor Correlations to Detect and Localize Faults in Mobile Robots
Modeling Cross-Sensory and Sensorimotor Correlations to Detect and Localize Faults in Mobile Robots
Author(s)
Kira, Zsolt
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Abstract
We present a novel framework for learning crosssensory
and sensorimotor correlations in order to detect and
localize faults in mobile robots. Unlike traditional fault
detection and identification schemes, we do not use a priori
models of fault states or system dynamics. Instead, we utilize
additional information and possible source of redundancy that
mobile robots have available to them, namely a hierarchical
graph representing stages of sensory processing at multiple
levels of abstractions and their outputs. We learn statistical
models of correlations between elements in the hierarchy, in
addition to the control signals, and use this to detect and
identify changes in the capabilities of the robot. The framework
is instantiated using Self-Organizing Maps, a simple
unsupervised learning algorithm. Results indicate that the
system can detect sensory and motor faults in a mobile robot
and identify their cause, without using a priori models of the
robot or its fault states.
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Date Issued
2007
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Text
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Paper