Each of these modules provides, to the supervisory module, a recommended heading, a recommended speed, and a quality factor, which is a fuzzy quantity used to rank the heading and speed recommendations against those of the other modules. The supervisory module uses the quality factors in arbitrating the possibly differing heading and speed recommendations to generate a single control recommendation for the robot to follow.

The fuzzy rule base of the fuzzy-logic system is generated in a process in which a human manually controls the vehicle on terrain similar to the terrain on which the robot is intended to operate. During this process, the choices made by the initially untrained or partially trained fuzzy-logic system are quantitatively compared with those of the human driver by means of a metric defined specifically for this purpose. Then the fuzzy logic and the quantities associated with it are adjusted, according to the metric, to make the fuzzy-logic system mimic the human’s choices more closely.

This work was done by Dean B. Edwards, John Canning, and Joel Alberts of the University of Idaho for the Naval Research Laboratory. For more information, download the Technical Support Package (free white paper) at www.defensetechbriefs.com/tsp under the Information Sciences category. NRL-0012

This Brief includes a Technical Support Package (TSP).

Autonomous Vehicles Would Learn by Mimicking Human Drivers (reference NRL-0012) is currently available for download from the TSP library.

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