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Proceedings Paper

Ultrasonic robot localization using Dempster-Shafer theory
Author(s): Kenneth F. Hughes; Robin R. Murphy
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Paper Abstract

In this paper we present a method for ultrasonic robot localization without a priori world models utilizing the ideas of distinctive places and open space attraction. This method was incorporated into a move-to-station behavior, which was demonstrated on the Georgia Tech mobile robot. The key aspect of our approach was to use Dempster-Shafer theory to overcome the problem of the uncertainty in the range measurements returned by the sensors. The state of the world was modeled as a two element frame of discernment (Theta) : empty and occupied. The world itself was represented as a grid, with the belief in whether a grid element was empty or occupied was set to total ignorance (don't know) at the beginning of the robot behavior. A belief model of the range readings was used to compute the belief of points in the environment being empty, occupied, or unknown. Belief from repeated measurements updated the world map according to Dempster's rule of combination. The current belief in the empty space was used to construct a weighted centroid of the empty space (or station) after each move of the robot. By moving toward this center of mass and continually adding to the beliefs of the points in the environment the robot iteratively moved to the center of the open space. Experiments demonstrated that the robot was able to localize itself with a repeatability of 1.5 feet in a 33 foot square room, regardless of the starting position within the open space. This method is contrasted with a technique which did not explicitly model the belief in the range readings; that technique was unable to consistently converge on the center of the room within ten moves.

Paper Details

Date Published: 16 December 1992
PDF: 10 pages
Proc. SPIE 1766, Neural and Stochastic Methods in Image and Signal Processing, (16 December 1992); doi: 10.1117/12.130812
Show Author Affiliations
Kenneth F. Hughes, Univ. of South Florida (United States)
Robin R. Murphy, Georgia Institute of Technology (United States)


Published in SPIE Proceedings Vol. 1766:
Neural and Stochastic Methods in Image and Signal Processing
Su-Shing Chen, Editor(s)

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