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Autonomous Referencing Procedure for Mobile Robots

IP.com Disclosure Number: IPCOM000122180D
Original Publication Date: 1991-Nov-01
Included in the Prior Art Database: 2005-Apr-04
Document File: 2 page(s) / 65K

Publishing Venue

IBM

Related People

Malik, R: AUTHOR [+2]

Abstract

A method of self-location for mobile robots in known environments is given. The known environment is decomposed into disjoint sub-regions, and each sub-region is assigned an a-priori probability of containing the robot. The a-priori probabilities are based on the geometric shape descriptions of each sub-region. Sensor readings are used to modify these probabilities, and a single region is chosen as having the highest probability of containing the robot. Once the most likely region is chosen, the location problem is posed as a standard estimation problem with noisy data, and an actual position is calculated.

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Autonomous Referencing Procedure for Mobile Robots

      A method of self-location for mobile robots in known
environments is given.  The known environment is decomposed into
disjoint sub-regions, and each sub-region is assigned an a-priori
probability of containing the robot.  The a-priori probabilities are
based on the geometric shape descriptions of each sub-region.  Sensor
readings are used to modify these probabilities, and a single region
is chosen as having the highest probability of containing the robot.
Once the most likely region is chosen, the location problem is posed
as a standard estimation problem with noisy data, and an actual
position is calculated.

      The input to the system consists of three items:  data from
sensors, environment description, and prior location densities (see
Fig.  1).  The data can come from any type of sensors, such as
ultrasonic ranging, vision, or proximity. This input is generally not
raw sensor data, but pre-processed data which has gone through some
specific "feature extractor," such as a perpendicular distance
detector, or a corner detector.  The environment description needs to
be supplied only once for each different environment that the mobile
robot will operate in.  This information normally consists of
locations for walls and obstacles in the form of a two-dimensional
representation. The prior location density is a two-dimensional
probability density function which supplies the a-priori information
availab...