{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/110582"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/110582","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"CPU scheduling in robotics & AR/VR applications","abstract":"Robot and AR/VR systems have to take highly responsive real-time actions, driven by complex decisions involving a pipeline of sensing, perception, planning, and reaction tasks. Given constrained resources, this leads to a difficult scheduling problem. In practice – system designers manually tune params for their specific hardware and application, while real-time scheduling approaches assume static periodic schedules – both of which result in suboptimal application performance especially when both the environment and the hardware can change. In this work, we highlight the emerging need for automated resource optimization at runtime in sense-react systems. As a step towards this goal, we identify various unique challenges in this area, especially understanding the key scheduling requirements for such systems. We propose a preliminary framework and a novel scheduling policy that enables efficient and dynamic optimization of application-specific performance goals. In experiments with a prototype implemented in the ROS and ILLIXR platforms, we show that our approach improves application performance, for example, 15x better performance for a face tracking robot and 7x better collision avoidance for a navigation robot. We believe this work will lead to systems that are substantially easier to develop and fulfill their tasks measurably better.","abstract_html":"Robot and AR/VR systems have to take highly responsive real-time actions, driven by complex decisions involving a pipeline of sensing, perception, planning, and reaction tasks. Given constrained resources, this leads to a difficult scheduling problem. In practice – system designers manually tune params for their specific hardware and application, while real-time scheduling approaches assume static periodic schedules – both of which result in suboptimal application performance especially when both the environment and the hardware can change. In this work, we highlight the emerging need for automated resource optimization at runtime in sense-react systems. As a step towards this goal, we identify various unique challenges in this area, especially understanding the key scheduling requirements for such systems. We propose a preliminary framework and a novel scheduling policy that enables efficient and dynamic optimization of application-specific performance goals. In experiments with a prototype implemented in the ROS and ILLIXR platforms, we show that our approach improves application performance, for example, 15x better performance for a face tracking robot and 7x better collision avoidance for a navigation robot. We believe this work will lead to systems that are substantially easier to develop and fulfill their tasks measurably better.","abstract_has_math":false,"creators":["Aditi, -"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Godfrey, Philip Brighten","Mittal, Radhika"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2021,"date_issued":"2021-09-17T01:13:29Z","date_published":"2021-09-17T01:13:29Z","updated_at":"2026-07-22T22:24:52Z","subjects":["Robotics","Scheduling","Resource management","AR/VR"],"languages":["en"],"rights":["Copyright 2021 - Aditi"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/110582","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Godfrey, Philip Brighten","Mittal, Radhika"]},{"key":"dc:creator","label":"Author","values":["Aditi, -"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2021-09-17T01:13:29Z","2021-04-27","2021-05"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"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":["Robotics","Scheduling","Resource management","AR/VR"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2021 - Aditi"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/110582"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Robot and AR/VR systems have to take highly responsive real-time actions, driven by complex decisions involving a pipeline of sensing, perception, planning, and reaction tasks. 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In experiments with a prototype implemented in the ROS and ILLIXR platforms, we show that our approach improves application performance, for example, 15x better performance for a face tracking robot and 7x better collision avoidance for a navigation robot. We believe this work will lead to systems that are substantially easier to develop and fulfill their tasks measurably better.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2021-09-16 without embargo terms","The student, - Aditi, accepted the attached license on 2021-04-26 at 18:28.","The student, - Aditi, submitted this Thesis for approval on 2021-04-26 at 18:36.","This Thesis was approved for publication on 2021-04-27 at 15:00.","DSpace SAF Submission Ingestion Package generated from Vireo submission #16571 on 2021-09-16 at 16:48:27","Made available in DSpace on 2021-09-17T01:13:29Z (GMT). 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In practice – system designers manually tune params for their specific hardware and application, while real-time scheduling approaches assume static periodic schedules – both of which result in suboptimal application performance especially when both the environment and the hardware can change. In this work, we highlight the emerging need for automated resource optimization at runtime in sense-react systems. As a step towards this goal, we identify various unique challenges in this area, especially understanding the key scheduling requirements for such systems. We propose a preliminary framework and a novel scheduling policy that enables efficient and dynamic optimization of application-specific performance goals. In experiments with a prototype implemented in the ROS and ILLIXR platforms, we show that our approach improves application performance, for example, 15x better performance for a face tracking robot and 7x better collision avoidance for a navigation robot. We believe this work will lead to systems that are substantially easier to develop and fulfill their tasks measurably better.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2021-09-16 without embargo terms","The student, - Aditi, accepted the attached license on 2021-04-26 at 18:28.","The student, - Aditi, submitted this Thesis for approval on 2021-04-26 at 18:36.","This Thesis was approved for publication on 2021-04-27 at 15:00.","DSpace SAF Submission Ingestion Package generated from Vireo submission #16571 on 2021-09-16 at 16:48:27","Made available in DSpace on 2021-09-17T01:13:29Z (GMT). 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