{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/105788"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/105788","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Bio-inspired vision-based evasion control: collision avoidance without distance measurement","abstract":"In the past decade, the appearance of multiple autonomous vehicle platforms - such as self-driving cars (SDC), unmanned aerial vehicles (UAVs), delivery robots and precision agriculture drones - have emerged with an accelerated pace. Their development has been driven by enormous research at the intersection of control technology and machine learning, enabling autonomous operations, and minimizing human intervention. While the minimization of human intervention has the objective of minimizing the impact of potential human errors, it comes at the price of rigorously formulating and solving challenging problems as collision avoidance that humans quite often do subconsciously and with ease. This dissertation introduces a framework for collision avoidance, where the measurement of the distance to objects and obstacles is not available. This limitation is common to all low-cost and small, ground, or flying vehicles that are not equipped with expensive cameras. The proposed solution takes inspiration from biological systems and the mechanisms that invertebrates and birds use to evade predators. Psychological evidence shows that animals are capable of evading eminent collisions without using depth information, relying instead on looming stimuli. In contrast, the field of robotics has solved collision avoidance among uncooperative vehicles by using depth (the relative distance) to the obstacles as feedback, measured e.g. by lidar, which can be very expensive. To bridge this gap, this works presents a different paradigm in the sensor measurements required for collision avoidance. Relying solely on information that can be directly acquired from a monocular camera this dissertation outlines three control strategies suitable for unicycle-like vehicles avoiding a single, unknown, dynamic uncooperative obstacle: (i) using a line-of-sight (LOS) only measurement, (ii) using a LOS measurement and time-to-collision, and (iii) a LOS, LOS rate and time-to-collision based algorithm. These quantities can readily be estimated from a monocular camera vision system on board the vehicle. Under reasonable assumptions theoretical guarantees are obtained that ensure collision avoidance with an uncooperative moving obstacle.","abstract_html":"In the past decade, the appearance of multiple autonomous vehicle platforms - such as self-driving cars (SDC), unmanned aerial vehicles (UAVs), delivery robots and precision agriculture drones - have emerged with an accelerated pace. Their development has been driven by enormous research at the intersection of control technology and machine learning, enabling autonomous operations, and minimizing human intervention. While the minimization of human intervention has the objective of minimizing the impact of potential human errors, it comes at the price of rigorously formulating and solving challenging problems as collision avoidance that humans quite often do subconsciously and with ease. This dissertation introduces a framework for collision avoidance, where the measurement of the distance to objects and obstacles is not available. This limitation is common to all low-cost and small, ground, or flying vehicles that are not equipped with expensive cameras. The proposed solution takes inspiration from biological systems and the mechanisms that invertebrates and birds use to evade predators. Psychological evidence shows that animals are capable of evading eminent collisions without using depth information, relying instead on looming stimuli. In contrast, the field of robotics has solved collision avoidance among uncooperative vehicles by using depth (the relative distance) to the obstacles as feedback, measured e.g. by lidar, which can be very expensive. To bridge this gap, this works presents a different paradigm in the sensor measurements required for collision avoidance. Relying solely on information that can be directly acquired from a monocular camera this dissertation outlines three control strategies suitable for unicycle-like vehicles avoiding a single, unknown, dynamic uncooperative obstacle: (i) using a line-of-sight (LOS) only measurement, (ii) using a LOS measurement and time-to-collision, and (iii) a LOS, LOS rate and time-to-collision based algorithm. These quantities can readily be estimated from a monocular camera vision system on board the vehicle. Under reasonable assumptions theoretical guarantees are obtained that ensure collision avoidance with an uncooperative moving obstacle.","abstract_has_math":false,"creators":["Marinho, Thiago"],"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","Voulgaris, Petros","Stipanovic, Dusan","Salapaka, Srinivasa"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-11-26T20:49:21Z","date_published":"2019-11-26T20:49:21Z","updated_at":"2026-07-22T22:24:45Z","subjects":["Collision Avoidance, Evasion Control, Autonomous Systems."],"languages":["en"],"rights":["Copyright 2019 Thiago Marinho"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/105788","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Hovakimyan, Naira","Voulgaris, Petros","Stipanovic, Dusan","Salapaka, Srinivasa"]},{"key":"dc:creator","label":"Author","values":["Marinho, Thiago"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2019-11-26T20:49:21Z","2019-07-08","2019-08"]},{"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 Avoidance, Evasion Control, Autonomous Systems."]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2019 Thiago Marinho"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/105788"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["In the past decade, the appearance of multiple autonomous vehicle platforms - such as self-driving cars (SDC), unmanned aerial vehicles (UAVs), delivery robots and precision agriculture drones - have emerged with an accelerated pace. Their development has been driven by enormous research at the intersection of control technology and machine learning, enabling autonomous operations, and minimizing human intervention. While the minimization of human intervention has the objective of minimizing the impact of potential human errors, it comes at the price of rigorously formulating and solving challenging problems as collision avoidance that humans quite often do subconsciously and with ease. This dissertation introduces a framework for collision avoidance, where the measurement of the distance to objects and obstacles is not available. This limitation is common to all low-cost and small, ground, or flying vehicles that are not equipped with expensive cameras. The proposed solution takes inspiration from biological systems and the mechanisms that invertebrates and birds use to evade predators. Psychological evidence shows that animals are capable of evading eminent collisions without using depth information, relying instead on looming stimuli. In contrast, the field of robotics has solved collision avoidance among uncooperative vehicles by using depth (the relative distance) to the obstacles as feedback, measured e.g. by lidar, which can be very expensive. To bridge this gap, this works presents a different paradigm in the sensor measurements required for collision avoidance. Relying solely on information that can be directly acquired from a monocular camera this dissertation outlines three control strategies suitable for unicycle-like vehicles avoiding a single, unknown, dynamic uncooperative obstacle: (i) using a line-of-sight (LOS) only measurement, (ii) using a LOS measurement and time-to-collision, and (iii) a LOS, LOS rate and time-to-collision based algorithm. These quantities can readily be estimated from a monocular camera vision system on board the vehicle. Under reasonable assumptions theoretical guarantees are obtained that ensure collision avoidance with an uncooperative moving obstacle.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2021-08-01","The student, Thiago Marinho, accepted the attached license on 2019-07-05 at 22:17.","The student, Thiago Marinho, submitted this Dissertation for approval on 2019-07-05 at 22:19.","This Dissertation was approved for publication on 2019-07-08 at 10:36.","DSpace SAF Submission Ingestion Package generated from Vireo submission #14179 on 2019-11-26 at 13:04:32","Made available in DSpace on 2019-11-26T20:49:21Z (GMT). No. of bitstreams: 2 MARINHO-DISSERTATION-2019.pdf: 21521380 bytes, checksum: 9d9b8cfe3fb5ca324b17660739a0b5c9 (MD5) LICENSE.txt: 4211 bytes, checksum: 2d7802c10a355cc11e7a14ef0289cf47 (MD5) Previous issue date: 2019-07-08","Embargo set by: Seth Robbins for item 112933 Lift date: 2021-11-26T20:49:41Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","Open Restriction set for Item 112933 on 2021-04-28T13:53:53Z with date null by madinag@illinois.edu.","Open Restriction set for Item 112933 on 2021-04-28T13:53:59Z with date null by madinag@illinois.edu.","Open"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Bio-inspired vision-based evasion control: collision avoidance without distance measurement"]}]}],"canonical_facts":{"dc:contributor":["Hovakimyan, Naira","Voulgaris, Petros","Stipanovic, Dusan","Salapaka, Srinivasa"],"dc:creator":["Marinho, Thiago"],"dc:date":["2019-11-26T20:49:21Z","2019-07-08","2019-08"],"dc:description":["In the past decade, the appearance of multiple autonomous vehicle platforms - such as self-driving cars (SDC), unmanned aerial vehicles (UAVs), delivery robots and precision agriculture drones - have emerged with an accelerated pace. Their development has been driven by enormous research at the intersection of control technology and machine learning, enabling autonomous operations, and minimizing human intervention. While the minimization of human intervention has the objective of minimizing the impact of potential human errors, it comes at the price of rigorously formulating and solving challenging problems as collision avoidance that humans quite often do subconsciously and with ease. This dissertation introduces a framework for collision avoidance, where the measurement of the distance to objects and obstacles is not available. This limitation is common to all low-cost and small, ground, or flying vehicles that are not equipped with expensive cameras. The proposed solution takes inspiration from biological systems and the mechanisms that invertebrates and birds use to evade predators. Psychological evidence shows that animals are capable of evading eminent collisions without using depth information, relying instead on looming stimuli. In contrast, the field of robotics has solved collision avoidance among uncooperative vehicles by using depth (the relative distance) to the obstacles as feedback, measured e.g. by lidar, which can be very expensive. To bridge this gap, this works presents a different paradigm in the sensor measurements required for collision avoidance. Relying solely on information that can be directly acquired from a monocular camera this dissertation outlines three control strategies suitable for unicycle-like vehicles avoiding a single, unknown, dynamic uncooperative obstacle: (i) using a line-of-sight (LOS) only measurement, (ii) using a LOS measurement and time-to-collision, and (iii) a LOS, LOS rate and time-to-collision based algorithm. These quantities can readily be estimated from a monocular camera vision system on board the vehicle. Under reasonable assumptions theoretical guarantees are obtained that ensure collision avoidance with an uncooperative moving obstacle.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2021-08-01","The student, Thiago Marinho, accepted the attached license on 2019-07-05 at 22:17.","The student, Thiago Marinho, submitted this Dissertation for approval on 2019-07-05 at 22:19.","This Dissertation was approved for publication on 2019-07-08 at 10:36.","DSpace SAF Submission Ingestion Package generated from Vireo submission #14179 on 2019-11-26 at 13:04:32","Made available in DSpace on 2019-11-26T20:49:21Z (GMT). No. of bitstreams: 2 MARINHO-DISSERTATION-2019.pdf: 21521380 bytes, checksum: 9d9b8cfe3fb5ca324b17660739a0b5c9 (MD5) LICENSE.txt: 4211 bytes, checksum: 2d7802c10a355cc11e7a14ef0289cf47 (MD5) Previous issue date: 2019-07-08","Embargo set by: Seth Robbins for item 112933 Lift date: 2021-11-26T20:49:41Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","Open Restriction set for Item 112933 on 2021-04-28T13:53:53Z with date null by madinag@illinois.edu.","Open Restriction set for Item 112933 on 2021-04-28T13:53:59Z with date null by madinag@illinois.edu.","Open"],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/105788"],"dc:language":["en"],"dc:rights":["Copyright 2019 Thiago Marinho"],"dc:subject":["Collision Avoidance, Evasion Control, Autonomous Systems."],"dc:title":["Bio-inspired vision-based evasion control: collision avoidance without distance measurement"],"dc:type":["text"],"thesis:degree_discipline":["Mechanical Engineering"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:45Z"}