{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/98384"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/98384","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Collision avoidance for multi-vehicle cooperative missions","abstract":"This thesis focuses on collision avoidance for multi-vehicle coordinated missions. Building upon an existing cooperative control framework, we propose collision-avoidance methods that rely on practicably available obstacle information and allow safe operation without compromising on the mission objectives. Several applications of multi-vehicle coordinated missions require the vehicles to satisfy relative temporal constraints, such as maintaining formation throughout the mission or reaching their respective destinations at the same time. With such applications in focus, two different methodologies for collision avoidance are explored. We first consider a speed-adjustment based approach that can be used to avoid moving obstacles. Using obstacle information which may be available in real world applications such as air traffic management and highway driving, the proposed algorithm allows collision avoidance without requiring any vehicle to deviate from its path or lose coordination with other vehicles. Next, trajectory replanning approach for obstacle avoidance is considered. Applicable to both static and moving obstacles, this method may require the vehicle to steer away from its originally intended path. The deviations in position, velocity and acceleration caused by the avoidance maneuver, however, are small and respect bounds that can be computed offline. These bounds can be used during the mission-planning phase to guarantee satisfaction of vehicle dynamic constraints and inter-vehicle safety distance even during collision avoidance maneuver. Through novel use of Bézier curves and surfaces for representing uncertain trajectories, these algorithms make use of partial information on obstacle trajectory and are computationally efficient.","abstract_html":"This thesis focuses on collision avoidance for multi-vehicle coordinated missions. Building upon an existing cooperative control framework, we propose collision-avoidance methods that rely on practicably available obstacle information and allow safe operation without compromising on the mission objectives. Several applications of multi-vehicle coordinated missions require the vehicles to satisfy relative temporal constraints, such as maintaining formation throughout the mission or reaching their respective destinations at the same time. With such applications in focus, two different methodologies for collision avoidance are explored. We first consider a speed-adjustment based approach that can be used to avoid moving obstacles. Using obstacle information which may be available in real world applications such as air traffic management and highway driving, the proposed algorithm allows collision avoidance without requiring any vehicle to deviate from its path or lose coordination with other vehicles. Next, trajectory replanning approach for obstacle avoidance is considered. Applicable to both static and moving obstacles, this method may require the vehicle to steer away from its originally intended path. The deviations in position, velocity and acceleration caused by the avoidance maneuver, however, are small and respect bounds that can be computed offline. These bounds can be used during the mission-planning phase to guarantee satisfaction of vehicle dynamic constraints and inter-vehicle safety distance even during collision avoidance maneuver. Through novel use of Bézier curves and surfaces for representing uncertain trajectories, these algorithms make use of partial information on obstacle trajectory and are computationally efficient.","abstract_has_math":false,"creators":["Mehdi, Syed Bilal"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Mechanical Engineering","degree_department":null,"school":null,"contributors":["Hovakimyan, Naira","Salapaka, Srinivasa","Stipanović, Dušan M.","Langbort, Cedric"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2017,"date_issued":"2017-09-29T17:56:47Z","date_published":"2017-09-29T17:56:47Z","updated_at":"2026-07-22T22:24:35Z","subjects":["Collision avoidanace","Multi-vehicle missions","Cooperative missions","Bézier curves","Bézier surfaces","Speed adjustment"],"languages":["en"],"rights":["Copyright 2017 Syed Bilal Mehdi"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/98384","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Hovakimyan, Naira","Salapaka, Srinivasa","Stipanović, Dušan M.","Langbort, Cedric"]},{"key":"dc:creator","label":"Author","values":["Mehdi, Syed Bilal"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2017-09-29T17:56:47Z","2017-07-14","2017-08"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"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":["Collision avoidanace","Multi-vehicle missions","Cooperative missions","Bézier curves","Bézier surfaces","Speed adjustment"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2017 Syed Bilal Mehdi"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/98384"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["This thesis focuses on collision avoidance for multi-vehicle coordinated missions. Building upon an existing cooperative control framework, we propose collision-avoidance methods that rely on practicably available obstacle information and allow safe operation without compromising on the mission objectives. Several applications of multi-vehicle coordinated missions require the vehicles to satisfy relative temporal constraints, such as maintaining formation throughout the mission or reaching their respective destinations at the same time. With such applications in focus, two different methodologies for collision avoidance are explored. We first consider a speed-adjustment based approach that can be used to avoid moving obstacles. Using obstacle information which may be available in real world applications such as air traffic management and highway driving, the proposed algorithm allows collision avoidance without requiring any vehicle to deviate from its path or lose coordination with other vehicles. Next, trajectory replanning approach for obstacle avoidance is considered. Applicable to both static and moving obstacles, this method may require the vehicle to steer away from its originally intended path. The deviations in position, velocity and acceleration caused by the avoidance maneuver, however, are small and respect bounds that can be computed offline. These bounds can be used during the mission-planning phase to guarantee satisfaction of vehicle dynamic constraints and inter-vehicle safety distance even during collision avoidance maneuver. Through novel use of Bézier curves and surfaces for representing uncertain trajectories, these algorithms make use of partial information on obstacle trajectory and are computationally efficient.","Submission original under an indefinite embargo labeled 'Open Access'. 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Building upon an existing cooperative control framework, we propose collision-avoidance methods that rely on practicably available obstacle information and allow safe operation without compromising on the mission objectives. Several applications of multi-vehicle coordinated missions require the vehicles to satisfy relative temporal constraints, such as maintaining formation throughout the mission or reaching their respective destinations at the same time. With such applications in focus, two different methodologies for collision avoidance are explored. We first consider a speed-adjustment based approach that can be used to avoid moving obstacles. Using obstacle information which may be available in real world applications such as air traffic management and highway driving, the proposed algorithm allows collision avoidance without requiring any vehicle to deviate from its path or lose coordination with other vehicles. Next, trajectory replanning approach for obstacle avoidance is considered. Applicable to both static and moving obstacles, this method may require the vehicle to steer away from its originally intended path. The deviations in position, velocity and acceleration caused by the avoidance maneuver, however, are small and respect bounds that can be computed offline. These bounds can be used during the mission-planning phase to guarantee satisfaction of vehicle dynamic constraints and inter-vehicle safety distance even during collision avoidance maneuver. Through novel use of Bézier curves and surfaces for representing uncertain trajectories, these algorithms make use of partial information on obstacle trajectory and are computationally efficient.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2017-09-29 without embargo terms","The student, Syed Mehdi, accepted the attached license on 2017-07-13 at 10:33.","The student, Syed Mehdi, submitted this Dissertation for approval on 2017-07-13 at 10:43.","This Dissertation was approved for publication on 2017-07-14 at 08:39.","DSpace SAF Submission Ingestion Package generated from Vireo submission #11443 on 2017-09-29 at 11:30:02","Made available in DSpace on 2017-09-29T17:56:47Z (GMT). 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