{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/105105"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/105105","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Multi-step recovery strategy for humanoid robots using model predictive control","abstract":"Made available in DSpace on 2019-08-23T20:36:13Z (GMT). No. of bitstreams: 2 MATIJEVICH-THESIS-2019.pdf: 1689446 bytes, checksum: c41c3a94a10006aa2ead403c11aebf8d (MD5) LICENSE.txt: 4213 bytes, checksum: 1306b3614c66f8a5b95ee47dcfc8aa21 (MD5) Previous issue date: 2019-04-26","abstract_html":"Made available in DSpace on 2019-08-23T20:36:13Z (GMT). No. of bitstreams: 2 MATIJEVICH-THESIS-2019.pdf: 1689446 bytes, checksum: c41c3a94a10006aa2ead403c11aebf8d (MD5) LICENSE.txt: 4213 bytes, checksum: 1306b3614c66f8a5b95ee47dcfc8aa21 (MD5) Previous issue date: 2019-04-26","abstract_has_math":false,"creators":["Matijevich, Tyler John"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Mechanical Engineering","degree_department":null,"school":null,"contributors":["Park, Hae-Won"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-08-23T20:36:13Z","date_published":"2019-08-23T20:36:13Z","updated_at":"2026-07-22T22:24:44Z","subjects":["Push Recovery","Step Planning","Humanoid Robot","Model Predictive Control","Quadratic Programming"],"languages":["en"],"rights":["Copyright 2019 Tyler Matijevich"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/105105","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Park, Hae-Won"]},{"key":"dc:creator","label":"Author","values":["Matijevich, Tyler John"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2019-08-23T20:36:13Z","2021-08-24T09:15:10Z","2019-04-26","2019-05"]},{"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":["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":["Push Recovery","Step Planning","Humanoid Robot","Model Predictive Control","Quadratic Programming"]}]},{"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 Tyler Matijevich"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/105105"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Made available in DSpace on 2019-08-23T20:36:13Z (GMT). No. of bitstreams: 2 MATIJEVICH-THESIS-2019.pdf: 1689446 bytes, checksum: c41c3a94a10006aa2ead403c11aebf8d (MD5) LICENSE.txt: 4213 bytes, checksum: 1306b3614c66f8a5b95ee47dcfc8aa21 (MD5) Previous issue date: 2019-04-26","Embargo set by: Seth Robbins for item 112224 Lift date: 2021-08-23T20:36:18Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","U of I Only Restriction Lifted for Item 112224 on 2021-08-24T09:15:10Z.","Humanoid robots in any environment are likely to experience collisions with obstacles or imbalance while attempting to navigate or perform a task. While humans are quite capable of balancing when encountering moderate collisions and imbalance, humanoid robots have yet to master the same skill. Push recovery is a strategy to maintain an upright posture in a robot while subjected to disturbances or imbalance. Performing push recovery on a humanoid robot consists of planning the actuation of the joints, ground reaction forces, and footsteps. Controlling a robot's trajectory has many challenges such as incorporating nonlinear dynamic equations, achieving resilience to disturbances, and maintaining computational efficiency. This paper introduces a control framework that determines the necessary actuated joint forces and footsteps in order to balance. The proposed control framework is distinct from existing Capture Point/Capture Region solutions in that the controller simultaneously plans multiple future footsteps and all actuated joint forces. Model Predictive Control provides the necessary planning and optimization to anticipate the robot's future trajectory and accommodate imbalance. Combining the effort of all actuators and multiple footsteps into one recovery strategy has yet to be implemented in push recovery of humanoid robots. This control framework is demonstrated with a bipedal walking robot model and the performance is validated with simulation results.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2021-05-01","The student, Tyler Matijevich, accepted the attached license on 2019-04-25 at 17:39.","The student, Tyler Matijevich, submitted this Thesis for approval on 2019-04-25 at 17:46.","This Thesis was approved for publication on 2019-04-26 at 09:51.","DSpace SAF Submission Ingestion Package generated from Vireo submission #13930 on 2019-08-22 at 15:08:47"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Multi-step recovery strategy for humanoid robots using model predictive control"]}]}],"canonical_facts":{"dc:contributor":["Park, Hae-Won"],"dc:creator":["Matijevich, Tyler John"],"dc:date":["2019-08-23T20:36:13Z","2021-08-24T09:15:10Z","2019-04-26","2019-05"],"dc:description":["Made available in DSpace on 2019-08-23T20:36:13Z (GMT). No. of bitstreams: 2 MATIJEVICH-THESIS-2019.pdf: 1689446 bytes, checksum: c41c3a94a10006aa2ead403c11aebf8d (MD5) LICENSE.txt: 4213 bytes, checksum: 1306b3614c66f8a5b95ee47dcfc8aa21 (MD5) Previous issue date: 2019-04-26","Embargo set by: Seth Robbins for item 112224 Lift date: 2021-08-23T20:36:18Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","U of I Only Restriction Lifted for Item 112224 on 2021-08-24T09:15:10Z.","Humanoid robots in any environment are likely to experience collisions with obstacles or imbalance while attempting to navigate or perform a task. While humans are quite capable of balancing when encountering moderate collisions and imbalance, humanoid robots have yet to master the same skill. Push recovery is a strategy to maintain an upright posture in a robot while subjected to disturbances or imbalance. Performing push recovery on a humanoid robot consists of planning the actuation of the joints, ground reaction forces, and footsteps. Controlling a robot's trajectory has many challenges such as incorporating nonlinear dynamic equations, achieving resilience to disturbances, and maintaining computational efficiency. This paper introduces a control framework that determines the necessary actuated joint forces and footsteps in order to balance. The proposed control framework is distinct from existing Capture Point/Capture Region solutions in that the controller simultaneously plans multiple future footsteps and all actuated joint forces. Model Predictive Control provides the necessary planning and optimization to anticipate the robot's future trajectory and accommodate imbalance. Combining the effort of all actuators and multiple footsteps into one recovery strategy has yet to be implemented in push recovery of humanoid robots. This control framework is demonstrated with a bipedal walking robot model and the performance is validated with simulation results.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2021-05-01","The student, Tyler Matijevich, accepted the attached license on 2019-04-25 at 17:39.","The student, Tyler Matijevich, submitted this Thesis for approval on 2019-04-25 at 17:46.","This Thesis was approved for publication on 2019-04-26 at 09:51.","DSpace SAF Submission Ingestion Package generated from Vireo submission #13930 on 2019-08-22 at 15:08:47"],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/105105"],"dc:language":["en"],"dc:rights":["Copyright 2019 Tyler Matijevich"],"dc:subject":["Push Recovery","Step Planning","Humanoid Robot","Model Predictive Control","Quadratic Programming"],"dc:title":["Multi-step recovery strategy for humanoid robots using model predictive control"],"dc:type":["text"],"thesis:degree_discipline":["Mechanical Engineering"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:44Z"}