{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/18313"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/18313","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Navigation in an unfamiliar environment using signal intensity","abstract":"This thesis introduces a sensor-based planning algorithm that uses less sensing information than any others within the family of bug algorithms. The robot is unable to access precise information regarding position coordinates, angular coordinates, time, or odometry, but is nevertheless able to navigate itself to a goal among unknown piecewise-analytic obstacles in the plane. The only sensor providing real values is an intensity sensor, which measures the signal strength emanating from the goal. The signal intensity function may or may not be symmetric; the main requirement is that the level sets are concentric images of simple closed curves, i.e. topological circles. Convergence analysis and distance bounds are established for the presented approach. The algorithm is then experimentally verified using a differential drive robot and an infrared beacon.","abstract_html":"This thesis introduces a sensor-based planning algorithm that uses less sensing information than any others within the family of bug algorithms. The robot is unable to access precise information regarding position coordinates, angular coordinates, time, or odometry, but is nevertheless able to navigate itself to a goal among unknown piecewise-analytic obstacles in the plane. The only sensor providing real values is an intensity sensor, which measures the signal strength emanating from the goal. The signal intensity function may or may not be symmetric; the main requirement is that the level sets are concentric images of simple closed curves, i.e. topological circles. Convergence analysis and distance bounds are established for the presented approach. The algorithm is then experimentally verified using a differential drive robot and an infrared beacon.","abstract_has_math":false,"creators":["Taylor, Kamilah J."],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["LaValle, Steven M."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2011,"date_issued":"2011-01-14T22:45:54Z","date_published":"2011-01-14T22:45:54Z","updated_at":"2026-07-22T22:25:11Z","subjects":["robotics","planning algorithms","bug algorithms","infrared beacon"],"languages":["en"],"rights":["Copyright 2010 Kamilah J. 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