{"id":{"repo_id":"buffalo","oai_identifier":"oai:ubir.buffalo.edu:10477/86632"},"canonical_url":"https://search.dev.ndltd.org/etd/buffalo/oai:ubir.buffalo.edu:10477/86632","repository":{"repo_id":"buffalo","name":"Buffalo","base_url":"https://ubir.buffalo.edu/oai/request"},"display":{"title":"Mission Area Encoding and Monitoring Methods for Swarm Robotic Applications","abstract":"M.S.","abstract_html":"M.S.","abstract_has_math":false,"creators":["Collins, Leighton"],"institution":"State University of New York at Buffalo","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Chowdhury, Souma","Mechanical and Aerospace Engineering"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-02-21T21:35:50Z","date_published":"2025-02-21T21:35:50Z","updated_at":"2026-07-27T19:05:32Z","subjects":["mechanical engineering","robotics","artificial intelligence"],"languages":["eng"],"rights":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/10477/86632","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Chowdhury, Souma","Mechanical and Aerospace Engineering"]},{"key":"dc:creator","label":"Author","values":["Collins, Leighton"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-02-21T21:35:50Z","2020"]},{"key":"dc:publisher","label":"Institution","values":["State University of New York at Buffalo"]},{"key":"dc:type","label":"Dc Type","values":["Text","Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["mechanical engineering","robotics","artificial intelligence"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/10477/86632"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["M.S.","This thesis presents work done in the area of swarm robotics, with the goal of advancing the practicality, feasibility, and applicability of swarm robots to solve complex problems. Two major algorithmic tools are developed for improving the performance of swarm robotics systems. The first is a dynamic area monitoring algorithm, which uses clustering and auctioning of cells in a discretized space to balance the load between agents, based on their initial states. This algorithm accounts for discontinuities in a specified area, and provides an optimized ordered list of way-points per agent using a discrete, computationally efficient, nearest neighbor path planning algorithm. The second tool is an automated topological encoder of geographical maps, which feeds into a unique topological graph generator. This algorithm provides as many map samples from which to train swarm learning models, drastically expanding the data set available, allowing more generalization of learned behaviors. These two algorithms can further help to strengthen the effectiveness and reliability of swarm systems for current and future applications.","**To request an accessible version of the file(s) associated with this item, contact library@buffalo.edu. Please include the item's persistent URL [http://hdl.handle.net/. . .] in your request.**"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Mission Area Encoding and Monitoring Methods for Swarm Robotic Applications"]}]}],"canonical_facts":{"dc:contributor":["Chowdhury, Souma","Mechanical and Aerospace Engineering"],"dc:creator":["Collins, Leighton"],"dc:date":["2025-02-21T21:35:50Z","2020"],"dc:description":["M.S.","This thesis presents work done in the area of swarm robotics, with the goal of advancing the practicality, feasibility, and applicability of swarm robots to solve complex problems. Two major algorithmic tools are developed for improving the performance of swarm robotics systems. The first is a dynamic area monitoring algorithm, which uses clustering and auctioning of cells in a discretized space to balance the load between agents, based on their initial states. This algorithm accounts for discontinuities in a specified area, and provides an optimized ordered list of way-points per agent using a discrete, computationally efficient, nearest neighbor path planning algorithm. The second tool is an automated topological encoder of geographical maps, which feeds into a unique topological graph generator. This algorithm provides as many map samples from which to train swarm learning models, drastically expanding the data set available, allowing more generalization of learned behaviors. These two algorithms can further help to strengthen the effectiveness and reliability of swarm systems for current and future applications.","**To request an accessible version of the file(s) associated with this item, contact library@buffalo.edu. Please include the item's persistent URL [http://hdl.handle.net/. . .] in your request.**"],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/10477/86632"],"dc:language":["eng"],"dc:publisher":["State University of New York at Buffalo"],"dc:rights":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."],"dc:subject":["mechanical engineering","robotics","artificial intelligence"],"dc:title":["Mission Area Encoding and Monitoring Methods for Swarm Robotic Applications"],"dc:type":["Text","Thesis"]},"updated_at":"2026-07-27T19:05:32Z"}