{"id":{"repo_id":"uoit","oai_identifier":"oai:ontariotechu.scholaris.ca:10155/1457"},"canonical_url":"https://search.dev.ndltd.org/etd/uoit/oai:ontariotechu.scholaris.ca:10155/1457","repository":{"repo_id":"uoit","name":"Ontario Institute of Technology","base_url":"https://ontariotechu.scholaris.ca/server/oai/request"},"display":{"title":"A robust and efficient autonomous exploration methodology of unknown environments for multi-robot systems","abstract":"Multi-robot systems can provide effective solutions for exploring and inspecting environments where it is unpractical or unsafe for humans, however, adequate coordination of the multi-robot system is a challenging initiative. A robust and efficient methodology for exploration of unknown environments is presented using a k-means method to improve traditional task allocation schemes. The k-means method proposed is an efficient technique due to the algorithm’s quick convergence time and its ability to segment a previously unknown map in a logical manner. In this method, a global executive receives frontiers from local robots, filters them, clusters them using the k-means method, and then reassigns them to the agents. A framework is developed in Robot Operating System (ROS) to test the effectiveness of the k-means method. The method is tested over a series of simulations and real-world tests, where it provided significant reductions in exploration time and distance travelled compared to other methods.","abstract_html":"Multi-robot systems can provide effective solutions for exploring and inspecting environments where it is unpractical or unsafe for humans, however, adequate coordination of the multi-robot system is a challenging initiative. A robust and efficient methodology for exploration of unknown environments is presented using a k-means method to improve traditional task allocation schemes. The k-means method proposed is an efficient technique due to the algorithm’s quick convergence time and its ability to segment a previously unknown map in a logical manner. In this method, a global executive receives frontiers from local robots, filters them, clusters them using the k-means method, and then reassigns them to the agents. A framework is developed in Robot Operating System (ROS) to test the effectiveness of the k-means method. The method is tested over a series of simulations and real-world tests, where it provided significant reductions in exploration time and distance travelled compared to other methods.","abstract_has_math":false,"creators":["Goodwin, Lillian"],"institution":"University of Ontario Institute of Technology","degree_name":"Master of Applied Science (MASc)","degree_level":null,"degree_discipline":"Mechanical Engineering","degree_department":null,"school":null,"contributors":[],"advisors":["Nokleby, Scott B."],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-05-01","date_published":"2022-05-01","updated_at":"2026-07-24T05:35:32Z","subjects":["Multi-Robot Systems (MRS)","Frontier exploration","K-means","Robot Operating System","Optimization"],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10155/1457","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Nokleby, Scott B."]},{"key":"dc:creator","label":"Author","values":["Goodwin, Lillian"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2022-05-17T16:25:46Z","2022-06-14T18:08:09Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2022-05-17T16:25:46Z","2022-06-14T18:08:09Z"]},{"key":"dc:date.issued","label":"Date","values":["2022-05-01"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Mechanical Engineering"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Applied Science (MASc)"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Ontario Institute of Technology"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Multi-Robot Systems (MRS)","Frontier exploration","K-means","Robot Operating System","Optimization"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10155/1457"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Multi-robot systems can provide effective solutions for exploring and inspecting environments where it is unpractical or unsafe for humans, however, adequate coordination of the multi-robot system is a challenging initiative. A robust and efficient methodology for exploration of unknown environments is presented using a k-means method to improve traditional task allocation schemes. The k-means method proposed is an efficient technique due to the algorithm’s quick convergence time and its ability to segment a previously unknown map in a logical manner. In this method, a global executive receives frontiers from local robots, filters them, clusters them using the k-means method, and then reassigns them to the agents. A framework is developed in Robot Operating System (ROS) to test the effectiveness of the k-means method. 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