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Journal of Intelligent and Robotic Systems 19
(3):271-298, July 1997. © Kluwer Academic Publishers
Sensor Fusion and Planning with Perception–Action
NetworkSukhan Lee Jet Propulsion Laboratory, California
Institute of Technology, Pasadena, California 91109, U.S.A. Depts. of
EE-Systems and CS, University of Southern California, Los Angeles, CA
90089-0781, U.S.A.
Abstract Robot intelligence
requires a real-time connection between sensing and action. A new
computation principle of robotics that efficiently implements such a
connection is utmost important for the new generation of robotics. In this
paper, a perception–action network is presented as a means of efficiently
integrating sensing, knowledge, and action for sensor fusion and planning.
The network consists of a number of heterogeneous computational units,
representing feature transformation and decision-making for action, which
are interconnected as a dynamic system. New input stimuli to the network
invoke the evolution of network states to a new equilibrium, through which
a real-time integration of sensing, knowledge, and action can be
accomplished. The network provides a formal, yet general and efficient,
method of achieving sensor fusion and planning. This is because the
uncertainties of signals, propagated in the network, can be controlled by
modifying sensing parameters and robot actions. Algorithms for sensor
planning based on the proposed network are established and applied to
robot self-localization. Simulation and experimental results are
shown.
Keywords sensor fusion, planning, dynamic system,
robotic system, uncertainty
ISSN 0921-0296
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