{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/85078"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/85078","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Efficient Hierarchical Global Motion Planning for Autonomous Vehicles","abstract":"Motion planning has become an increasingly important task as autonomy in mechanical systems has gained in popularity. Such systems must be able to plan new trajectories and controls reliably and rapidly in response to inputs. The challenges in creating such planners are decidedly non-trivial, including issues such as algorithm convergence, its correspondence to system controllability, optimality of solution, computational complexity, and dynamic environments. In response to these challenges, a hierarchical algorithm will be introduced that provides (a) decreased computational complexity though symmetry and a hybrid systems representation of the dynamics, (b) utilization of local controllability through a local planning algorithm, (c) minimization of a cost functional, (d) a randomized planner for obstacle avoidance, and (e) convergence guarantees. Applications include autonomous vehicles, sensor-based planning, and multiple vehicle coordination in ground-based, underwater, atmospheric, and orbital environments.","abstract_html":"Motion planning has become an increasingly important task as autonomy in mechanical systems has gained in popularity. Such systems must be able to plan new trajectories and controls reliably and rapidly in response to inputs. The challenges in creating such planners are decidedly non-trivial, including issues such as algorithm convergence, its correspondence to system controllability, optimality of solution, computational complexity, and dynamic environments. In response to these challenges, a hierarchical algorithm will be introduced that provides (a) decreased computational complexity though symmetry and a hybrid systems representation of the dynamics, (b) utilization of local controllability through a local planning algorithm, (c) minimization of a cost functional, (d) a randomized planner for obstacle avoidance, and (e) convergence guarantees. Applications include autonomous vehicles, sensor-based planning, and multiple vehicle coordination in ground-based, underwater, atmospheric, and orbital environments.","abstract_has_math":false,"creators":["Cerven, William Todd"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Aerospace Engineering","degree_department":null,"school":null,"contributors":["Coverstone, Victoria L.","Francesco Bullo"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015-09-25T22:34:20Z","date_published":"2015-09-25T22:34:20Z","updated_at":"2026-07-22T22:26:24Z","subjects":["Engineering, Mechanical"],"languages":["eng"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["(MiAaPQ)AAI3101812"],"render_values":[{"text":"(MiAaPQ)AAI3101812","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/2142/85078","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Coverstone, Victoria L.","Francesco Bullo"]},{"key":"dc:creator","label":"Author","values":["Cerven, William Todd"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2015-09-25T22:34:20Z","10000-01-01","2003"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Aerospace 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":["Engineering, Mechanical"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/85078","(MiAaPQ)AAI3101812"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Motion planning has become an increasingly important task as autonomy in mechanical systems has gained in popularity. Such systems must be able to plan new trajectories and controls reliably and rapidly in response to inputs. The challenges in creating such planners are decidedly non-trivial, including issues such as algorithm convergence, its correspondence to system controllability, optimality of solution, computational complexity, and dynamic environments. In response to these challenges, a hierarchical algorithm will be introduced that provides (a) decreased computational complexity though symmetry and a hybrid systems representation of the dynamics, (b) utilization of local controllability through a local planning algorithm, (c) minimization of a cost functional, (d) a randomized planner for obstacle avoidance, and (e) convergence guarantees. Applications include autonomous vehicles, sensor-based planning, and multiple vehicle coordination in ground-based, underwater, atmospheric, and orbital environments.","Made available in DSpace on 2015-09-25T22:34:20Z (GMT). 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Such systems must be able to plan new trajectories and controls reliably and rapidly in response to inputs. The challenges in creating such planners are decidedly non-trivial, including issues such as algorithm convergence, its correspondence to system controllability, optimality of solution, computational complexity, and dynamic environments. In response to these challenges, a hierarchical algorithm will be introduced that provides (a) decreased computational complexity though symmetry and a hybrid systems representation of the dynamics, (b) utilization of local controllability through a local planning algorithm, (c) minimization of a cost functional, (d) a randomized planner for obstacle avoidance, and (e) convergence guarantees. Applications include autonomous vehicles, sensor-based planning, and multiple vehicle coordination in ground-based, underwater, atmospheric, and orbital environments.","Made available in DSpace on 2015-09-25T22:34:20Z (GMT). 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