{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/121564"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/121564","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Distributed game-theoretic trajectory planning for multi-agent interactions","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-12-04 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-12-04 without embargo terms","abstract_has_math":false,"creators":["Williams, Zachary James"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Mehr, Negar Z"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-08","date_published":"2023-08","updated_at":"2026-07-22T22:25:00Z","subjects":["Dynamic Game Theory","Multi-agent Navigation","Potential Games","Path Planning For Multiple Robots","Multi-robot Systems","Interactive Trajectory Planning"],"languages":["en","eng"],"rights":["Copyright 2023 Zachary James Williams"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/121564","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Mehr, Negar Z"]},{"key":"dc:creator","label":"Author","values":["Williams, Zachary James"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2023-08","2023-07-21"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"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":["Dynamic Game Theory","Multi-agent Navigation","Potential Games","Path Planning For Multiple Robots","Multi-robot Systems","Interactive Trajectory Planning"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2023 Zachary James Williams"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/121564"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-12-04 without embargo terms","The student, Zachary Williams, accepted the attached license on 2023-07-20 at 19:55.","The student, Zachary Williams, submitted this Thesis for approval on 2023-07-20 at 19:59.","This Thesis was approved for publication on 2023-07-21 at 10:06.","DSpace SAF Submission Ingestion Package generated from Vireo submission #19762 on 2023-12-04 at 17:03:37","In this work, we develop a scalable, local trajectory optimization algorithm that enables robots to interact with other agents. It has been shown that the interactions of multiple agents can be successfully captured in game-theoretic formulations, where the interaction outcome can be best modeled via the equilibria of the underlying dynamic game. However, it is challenging to compute equilibria of dynamic games as it involves simultaneously solving a set of coupled optimal control problems. Existing solvers operate in a centralized fashion and do not scale up tractably to multiple interacting agents. We enable scalable distributed game-theoretic planning by leveraging the structure inherent in multi-agent interactions, namely, interactions belonging to the class of dynamic potential games. Since equilibria of dynamic potential games can be found by minimizing a single potential function, we can apply distributed and decentralized control techniques to seek equilibria of multi-agent interactions in a scalable and distributed manner. We compare the performance of our algorithm with a centralized interactive planner in a number of simulation studies and demonstrate that our algorithm results in better efficiency and scalability. We further evaluate our method in hardware experiments involving multiple quadcopters."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Distributed game-theoretic trajectory planning for multi-agent interactions"]}]}],"canonical_facts":{"dc:contributor":["Mehr, Negar Z"],"dc:creator":["Williams, Zachary James"],"dc:date":["2023-08","2023-07-21"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-12-04 without embargo terms","The student, Zachary Williams, accepted the attached license on 2023-07-20 at 19:55.","The student, Zachary Williams, submitted this Thesis for approval on 2023-07-20 at 19:59.","This Thesis was approved for publication on 2023-07-21 at 10:06.","DSpace SAF Submission Ingestion Package generated from Vireo submission #19762 on 2023-12-04 at 17:03:37","In this work, we develop a scalable, local trajectory optimization algorithm that enables robots to interact with other agents. It has been shown that the interactions of multiple agents can be successfully captured in game-theoretic formulations, where the interaction outcome can be best modeled via the equilibria of the underlying dynamic game. However, it is challenging to compute equilibria of dynamic games as it involves simultaneously solving a set of coupled optimal control problems. Existing solvers operate in a centralized fashion and do not scale up tractably to multiple interacting agents. We enable scalable distributed game-theoretic planning by leveraging the structure inherent in multi-agent interactions, namely, interactions belonging to the class of dynamic potential games. Since equilibria of dynamic potential games can be found by minimizing a single potential function, we can apply distributed and decentralized control techniques to seek equilibria of multi-agent interactions in a scalable and distributed manner. We compare the performance of our algorithm with a centralized interactive planner in a number of simulation studies and demonstrate that our algorithm results in better efficiency and scalability. We further evaluate our method in hardware experiments involving multiple quadcopters."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/121564"],"dc:language":["en","eng"],"dc:rights":["Copyright 2023 Zachary James Williams"],"dc:subject":["Dynamic Game Theory","Multi-agent Navigation","Potential Games","Path Planning For Multiple Robots","Multi-robot Systems","Interactive Trajectory Planning"],"dc:title":["Distributed game-theoretic trajectory planning for multi-agent interactions"],"dc:type":["text"],"thesis:degree_discipline":["Electrical & Computer Engr"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:00Z"}