{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/26288"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/26288","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Human and automatic control interaction with a remotely piloted vehicle","abstract":"In this dissertation, we present results from a study on the performance of humans and automatic controllers in a general remote navigation task. The remote navigation task is defined as driving a vehicle with nonholonomic kinematic constraints around obstacles toward a goal. We conducted experiments with humans and automatic controllers; in these experiments, the number and type of obstacles as well as the feedback delay was varied. Humans showed significantly more robust performance compared to that of a receding horizon controller. Using the human data, we then train a new human-like receding horizon controller which provides goal convergence when there is no uncertainty. We show that paths produced by the trained human-like controller are similar to human paths and that the trained controller improves robustness compared to the original receding horizon controller. We also show the impact of feedback delay on five metrics of human operator performance and for each metric characterize the impact as linear or superlinear. Finally we propose a human-inspired strategy for the automatic controller to robustly handle feedback delay.","abstract_html":"In this dissertation, we present results from a study on the performance of humans and automatic controllers in a general remote navigation task. The remote navigation task is defined as driving a vehicle with nonholonomic kinematic constraints around obstacles toward a goal. We conducted experiments with humans and automatic controllers; in these experiments, the number and type of obstacles as well as the feedback delay was varied. Humans showed significantly more robust performance compared to that of a receding horizon controller. Using the human data, we then train a new human-like receding horizon controller which provides goal convergence when there is no uncertainty. We show that paths produced by the trained human-like controller are similar to human paths and that the trained controller improves robustness compared to the original receding horizon controller. We also show the impact of feedback delay on five metrics of human operator performance and for each metric characterize the impact as linear or superlinear. Finally we propose a human-inspired strategy for the automatic controller to robustly handle feedback delay.","abstract_has_math":false,"creators":["Burns, Chad R."],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Mechanical Engineering","degree_department":null,"school":null,"contributors":["Stipanović, Dušan M.","Wang, Ranxiao F.","Hovakimyan, Naira","Dullerud, Geir E."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2011,"date_issued":"2011-08-26T15:21:35Z","date_published":"2011-08-26T15:21:35Z","updated_at":"2026-07-22T22:25:26Z","subjects":["remote navigation","human obstacle avoidance","automatic obstacle avoidance","receding horizon control","learning human behavior","time delay"],"languages":["en"],"rights":["Copyright 2011 Chad Raymond Burns"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/26288","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Stipanović, Dušan M.","Wang, Ranxiao F.","Hovakimyan, Naira","Dullerud, Geir E."]},{"key":"dc:creator","label":"Author","values":["Burns, Chad R."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2011-08-26T15:21:35Z","2013-08-27T10:00:20Z","2011-08"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Mechanical Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["remote navigation","human obstacle avoidance","automatic obstacle avoidance","receding horizon control","learning human behavior","time delay"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2011 Chad Raymond Burns"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/26288"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["In this dissertation, we present results from a study on the performance of humans and automatic controllers in a general remote navigation task. 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