{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/101286"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/101286","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":"This work presents a strategy for coordinated multi-agent weeding under conditions of partial environmental information. Our goal is to demonstrate the feasibility of coordination strategies for improving the weeding performance of autonomous agricultural robots. We show that, given a sufficient number of agents, the algorithm can successfully weed fields with various initial seed bank densities, even when multiple days are allowed to elapse before weeding commences. Furthermore, the use of coordination between agents is demonstrated to strongly improve system performance as the number of agents increases, enabling the system to eliminate all the weeds in the field, as in the case of full environmental information, when the planner without coordination failed to do so. As a domain to test our algorithms, we have developed an open source simulation environment, Weed World, which allows real-time visualization of coordinated weeding policies, and includes realistic weed generation. In this work, experiments are conducted to determine the required number of agents and their required transit speed, for given initial seed bank densities and varying allowed days before the start of the weeding process.","abstract_html":"This work presents a strategy for coordinated multi-agent weeding under conditions of partial environmental information. Our goal is to demonstrate the feasibility of coordination strategies for improving the weeding performance of autonomous agricultural robots. We show that, given a sufficient number of agents, the algorithm can successfully weed fields with various initial seed bank densities, even when multiple days are allowed to elapse before weeding commences. Furthermore, the use of coordination between agents is demonstrated to strongly improve system performance as the number of agents increases, enabling the system to eliminate all the weeds in the field, as in the case of full environmental information, when the planner without coordination failed to do so. As a domain to test our algorithms, we have developed an open source simulation environment, Weed World, which allows real-time visualization of coordinated weeding policies, and includes realistic weed generation. In this work, experiments are conducted to determine the required number of agents and their required transit speed, for given initial seed bank densities and varying allowed days before the start of the weeding process.","abstract_has_math":false,"creators":["McAllister, Wyatt Spalding"],"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":["Chowdhary, Girish"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-09-04T20:46:57Z","date_published":"2018-09-04T20:46:57Z","updated_at":"2026-07-22T22:24:38Z","subjects":["Multi-Robot Coordination, Multi-Agent Planning, Agricultural Robotics"],"languages":["en"],"rights":["2018 Wyatt McAllister"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/101286","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Chowdhary, Girish"]},{"key":"dc:creator","label":"Author","values":["McAllister, Wyatt Spalding"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2018-09-04T20:46:57Z","2020-09-05T09:15:09Z","2018-04-26","2018-05"]},{"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":["Multi-Robot Coordination, Multi-Agent Planning, Agricultural Robotics"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["2018 Wyatt McAllister"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/101286"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["This work presents a strategy for coordinated multi-agent weeding under conditions of partial environmental information. Our goal is to demonstrate the feasibility of coordination strategies for improving the weeding performance of autonomous agricultural robots. We show that, given a sufficient number of agents, the algorithm can successfully weed fields with various initial seed bank densities, even when multiple days are allowed to elapse before weeding commences. Furthermore, the use of coordination between agents is demonstrated to strongly improve system performance as the number of agents increases, enabling the system to eliminate all the weeds in the field, as in the case of full environmental information, when the planner without coordination failed to do so. As a domain to test our algorithms, we have developed an open source simulation environment, Weed World, which allows real-time visualization of coordinated weeding policies, and includes realistic weed generation. In this work, experiments are conducted to determine the required number of agents and their required transit speed, for given initial seed bank densities and varying allowed days before the start of the weeding process.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2020-05-01","The student, Wyatt McAllister, accepted the attached license on 2018-04-25 at 18:51.","The student, Wyatt McAllister, submitted this Thesis for approval on 2018-04-25 at 18:52.","This Thesis was approved for publication on 2018-04-26 at 09:08.","DSpace SAF Submission Ingestion Package generated from Vireo submission #12151 on 2018-08-31 at 17:26:13","Made available in DSpace on 2018-09-04T20:46:57Z (GMT). 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Our goal is to demonstrate the feasibility of coordination strategies for improving the weeding performance of autonomous agricultural robots. We show that, given a sufficient number of agents, the algorithm can successfully weed fields with various initial seed bank densities, even when multiple days are allowed to elapse before weeding commences. Furthermore, the use of coordination between agents is demonstrated to strongly improve system performance as the number of agents increases, enabling the system to eliminate all the weeds in the field, as in the case of full environmental information, when the planner without coordination failed to do so. As a domain to test our algorithms, we have developed an open source simulation environment, Weed World, which allows real-time visualization of coordinated weeding policies, and includes realistic weed generation. In this work, experiments are conducted to determine the required number of agents and their required transit speed, for given initial seed bank densities and varying allowed days before the start of the weeding process.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2020-05-01","The student, Wyatt McAllister, accepted the attached license on 2018-04-25 at 18:51.","The student, Wyatt McAllister, submitted this Thesis for approval on 2018-04-25 at 18:52.","This Thesis was approved for publication on 2018-04-26 at 09:08.","DSpace SAF Submission Ingestion Package generated from Vireo submission #12151 on 2018-08-31 at 17:26:13","Made available in DSpace on 2018-09-04T20:46:57Z (GMT). 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