{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/108240"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/108240","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Multi-agent planning for coordinated robotic weed killing","abstract":"The student, Wyatt McAllister, submitted this Dissertation for approval on 2020-05-08 at 13:51.","abstract_html":"The student, Wyatt McAllister, submitted this Dissertation for approval on 2020-05-08 at 13:51.","abstract_has_math":false,"creators":["McAllister, Wyatt Spalding"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Chowdhary, Girish","Davis, Adam","Srikant, Rayadurgam","Belabbas, Mohamed Ali"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020-08-27T00:49:52Z","date_published":"2020-08-27T00:49:52Z","updated_at":"2026-07-22T22:24:48Z","subjects":["Multi-agent, coordinated robotics, industrial agriculture"],"languages":["en"],"rights":["Copyright 2020 Wyatt McAllister"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/108240","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Chowdhary, Girish","Davis, Adam","Srikant, Rayadurgam","Belabbas, Mohamed Ali"]},{"key":"dc:creator","label":"Author","values":["McAllister, Wyatt Spalding"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2020-08-27T00:49:52Z","2022-08-27T00:51:40Z","2020-05-08","2020-05"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"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":["Multi-agent, coordinated robotics, industrial agriculture"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2020 Wyatt McAllister"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/108240"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["The student, Wyatt McAllister, submitted this Dissertation for approval on 2020-05-08 at 13:51.","This Dissertation was approved for publication on 2020-05-08 at 16:01.","This work presents techniques for predictive modeling of weed growth, as well as an improved planning index to be used in conjunction with these techniques, for the purpose of improving the performance of coordinated weeding algorithms being developed for industrial agriculture. We demonstrate that the evolving Gaussian process method applied to measurements from the agents can predict the evolution of the field within the realistic simulation environment Weed World. In addition to prediction, this method provides physical insight into the seed bank distribution of the field. In this work we extend the evolving Gaussian process model in two important ways. First, we have developed a model that has a bias term, and we show how it is connected to the seed bank distribution. Secondly, we show that one may decouple the component of the model representing weed growth from the component which varies with the seed bank distribution, and adapt the latter online. We compare this predictive approach with one that relies on known properties of the weed growth model, and show that the evolving Gaussian process method gives better results, even without assuming this model information. Finally, we use an improved planning index, entropic value-at-risk (EVaR) in conjunction with the Whittle index, which allows a balanced trade-off between exploration and exploitation, and ensures model improvement when used with these various prediction schemes.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2022-05-01","The student, Wyatt McAllister, accepted the attached license on 2020-05-08 at 13:50.","DSpace SAF Submission Ingestion Package generated from Vireo submission #14931 on 2020-08-25 at 17:39:48","Made available in DSpace on 2020-08-27T00:49:52Z (GMT). No. of bitstreams: 2 MCALLISTER-DISSERTATION-2020.pdf: 14363948 bytes, checksum: 8ed0a0c8135d61606ff8969ab188d185 (MD5) LICENSE.txt: 4213 bytes, checksum: 1a4a219a2b3a681cbb32c800529f7bbf (MD5) Previous issue date: 2020-05-08","Embargo set by: Seth Robbins for item 115854 Lift date: 2022-08-27T00:50:22Z Reason: Author requested closed access (OA after 2yrs) in Vireo ETD system","Embargo set by: Seth Robbins for item 115854 Lift date: 2022-08-27T00:51:40Z Reason: Author requested closed access (OA after 2yrs) in Vireo ETD system","Author requested closed access (OA after 2yrs) in Vireo ETD system","Limited"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Multi-agent planning for coordinated robotic weed killing"]}]}],"canonical_facts":{"dc:contributor":["Chowdhary, Girish","Davis, Adam","Srikant, Rayadurgam","Belabbas, Mohamed Ali"],"dc:creator":["McAllister, Wyatt Spalding"],"dc:date":["2020-08-27T00:49:52Z","2022-08-27T00:51:40Z","2020-05-08","2020-05"],"dc:description":["The student, Wyatt McAllister, submitted this Dissertation for approval on 2020-05-08 at 13:51.","This Dissertation was approved for publication on 2020-05-08 at 16:01.","This work presents techniques for predictive modeling of weed growth, as well as an improved planning index to be used in conjunction with these techniques, for the purpose of improving the performance of coordinated weeding algorithms being developed for industrial agriculture. We demonstrate that the evolving Gaussian process method applied to measurements from the agents can predict the evolution of the field within the realistic simulation environment Weed World. In addition to prediction, this method provides physical insight into the seed bank distribution of the field. In this work we extend the evolving Gaussian process model in two important ways. First, we have developed a model that has a bias term, and we show how it is connected to the seed bank distribution. Secondly, we show that one may decouple the component of the model representing weed growth from the component which varies with the seed bank distribution, and adapt the latter online. We compare this predictive approach with one that relies on known properties of the weed growth model, and show that the evolving Gaussian process method gives better results, even without assuming this model information. Finally, we use an improved planning index, entropic value-at-risk (EVaR) in conjunction with the Whittle index, which allows a balanced trade-off between exploration and exploitation, and ensures model improvement when used with these various prediction schemes.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2022-05-01","The student, Wyatt McAllister, accepted the attached license on 2020-05-08 at 13:50.","DSpace SAF Submission Ingestion Package generated from Vireo submission #14931 on 2020-08-25 at 17:39:48","Made available in DSpace on 2020-08-27T00:49:52Z (GMT). No. of bitstreams: 2 MCALLISTER-DISSERTATION-2020.pdf: 14363948 bytes, checksum: 8ed0a0c8135d61606ff8969ab188d185 (MD5) LICENSE.txt: 4213 bytes, checksum: 1a4a219a2b3a681cbb32c800529f7bbf (MD5) Previous issue date: 2020-05-08","Embargo set by: Seth Robbins for item 115854 Lift date: 2022-08-27T00:50:22Z Reason: Author requested closed access (OA after 2yrs) in Vireo ETD system","Embargo set by: Seth Robbins for item 115854 Lift date: 2022-08-27T00:51:40Z Reason: Author requested closed access (OA after 2yrs) in Vireo ETD system","Author requested closed access (OA after 2yrs) in Vireo ETD system","Limited"],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/108240"],"dc:language":["en"],"dc:rights":["Copyright 2020 Wyatt McAllister"],"dc:subject":["Multi-agent, coordinated robotics, industrial agriculture"],"dc:title":["Multi-agent planning for coordinated robotic weed killing"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Electrical & Computer Engr"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:48Z"}