Feature-based localization in sonar-equipped autonomous mobile robots through hough transform and unsupervised learning network
By:  Jonathan Scott Glennon
Publisher: Naval Postgraduate School; Available from National Technical Information Service
Published: 1998
Place of publication: Monterey, Calif; Springfield, Va
Language: eng
Number of pages: 94
Description: As we approach the new millennium, robots are playing an increasingly important role in our everyday lives.      Robotics has evolved in industrial and military applications, and unmanned space exploration promises the      continued development of ever-more-complex robots. Over the past few decades, research has focused on      the development of autonomous mobile robots - robots that can move about without human supervision. This      brings with it several problems, however, specifically the problem of localization. How can the robot      determine its own position and orientation relative to the environment around it? Various methods of      localization in mobile robots have been explored. Most of these methods, however, assume some a priori      knowledge of the environment, or that the robot will have access to navigation beacons or Global Positioning      Satellites. In this thesis, the foundations for feature-based localization are explored. An algorithm involving the      Rough transform of range data and a neural network is developed, which enables the robot to find an      unspecified number of wall-like features in its vicinity and determine the range and orientation of these walls      relative to itself. Computation times are shown to be quite reasonable, and the algorithm is applied in both      simulated and real-world indoor environments.
Notes: "June 1998."

Thesis advisor(s): Xiaoping Yun.

Thesis (M.S. in Electrical Engineering) Naval Postgraduate School, June 1998.

Includes bibliographical references (p. 89-91).

Approved for public release; distribution unlimited.

Also available online.

Mode of access: World Wide Web.

System requirements: Adobe acrobat reader.

US Marine Corps (USMC) author.

dk/dk cc:9116 9/15/98.