Joshua Davies

Graduation Semester and Year

2008

Language

English

Document Type

Thesis

Degree Name

Master of Science in Computer Science

Department

Computer Science and Engineering

First Advisor

Farhad Kamangar

Abstract

Mobile robotics is an interesting and challenging emerging field in computer science.To properly achieve true autonomy, a robot must be able to determine where it is in relationshipto a global frame of reference and it must be able to do so with a minimum of interaction fromhuman operators and impose as few constraints on its surroundings as possible. The uncertainnature of mobility and perception requires that advanced probabilistic inference techniques beapplied to minimize error. This work examines the applicability of radio-frequency signals to the mobile robotlocalization problem to localize quickly over a wide area. Particle filtering techniques areemployed to adjust for anticipated errors in both the motion model and the perception model.Radio hardware designed to capture time of flight and received signal-strength indicators (RSSI)is used to infer relative distances and triangulate the most likely position of a mobile node,taking into account a priori knowledge about past poses.

Disciplines

Computer Sciences | Physical Sciences and Mathematics

Comments

Degree granted by The University of Texas at Arlington

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