{"id":{"repo_id":"buffalo","oai_identifier":"oai:ubir.buffalo.edu:10477/86622"},"canonical_url":"https://search.dev.ndltd.org/etd/buffalo/oai:ubir.buffalo.edu:10477/86622","repository":{"repo_id":"buffalo","name":"Buffalo","base_url":"https://ubir.buffalo.edu/oai/request"},"display":{"title":"Motion Planning for Autonomous Vehicles in Sensing Applications","abstract":"Ph.D.","abstract_html":"Ph.D.","abstract_has_math":false,"creators":["Piao, Sixu; 0000-0003-1800-8716"],"institution":"State University of New York at Buffalo","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Ren, Kui","Computer Science and Engineering"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-02-21T21:35:43Z","date_published":"2025-02-21T21:35:43Z","updated_at":"2026-07-27T19:05:32Z","subjects":["computer science"],"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/10477/86622","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Ren, Kui","Computer Science and Engineering"]},{"key":"dc:creator","label":"Author","values":["Piao, Sixu; 0000-0003-1800-8716"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-02-21T21:35:43Z","2020"]},{"key":"dc:publisher","label":"Institution","values":["State University of New York at Buffalo"]},{"key":"dc:type","label":"Dc Type","values":["Text","Dissertation"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["computer science"]}]},{"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/10477/86622"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Ph.D.","Motion planning is a fundamental research area in robotics, as well as a core functional requirement of autonomous vehicles which have attracted a lot of attention recently. For example, Unmanned Aerial Vehicles (UAVs) have been widely applied in different areas, such as aerial videography, construction inspection, logistics and disaster response. Besides, tremendous amount of efforts from both academia and industry have been devoted into the development of self-driving cars, which are going to change the way transportation looks like in the future. In all kinds of applications of autonomous vehicles, motion planning is the key task of deciding what to do given a certain situation and a goal. In this dissertation, we propose to use UAVs to autonomously perform sensing tasks which are labor-intensive and time-consuming for human. Specifically, we design motion planning algorithms for an UAV to autonomously construct Channel State Information (CSI) map, which is an important process for building indoor localization systems. Indoor localization is a fundamental technique with huge impact on people's daily life due to the popularity of Location-Based Services. Among various approaches proposed in the literature, CSI fingerprinting based approach is shown to be both effective and practical since it can provide adequate accuracy with low overhead for users. However, the major drawback that limits its wide application is the huge amount of human effort required to build the fingerprint map. Therefore, we aim to address this problem by automating the fingerprint map construction process using UAVs. To achieve this goal, we propose novel solutions for solving the challenges in building such an autonomous sensing system. Given the limited battery capacity of commodity UAVs, it is extremely important yet challenging to optimize energy effciency for the UAV during the CSI measurement task. To address this challenge, we formulate the Minimum-Energy Motion Planning (MEMP) problem for the UAV as an optimization problem based on a novel graph model that includes the cost of possible actions for the UAV. Then we show that the formulated MEMP problem can be solved efficiently in practice by presenting a proof of reduction from MEMP to the classic Generalized Traveling Salesman Problem. More importantly, our algorithm greatly improves the energy efficiency for the UAV in the CSI measurement task compared to existing coverage path planning algorithms. Last but not least, we implement the system on a commercially of-the-shelf programmable drone equipped with a CSI measurement module, and also demonstrate by experiments that accurate indoor localization can be achieved using the CSI data collected by our UAV system.","**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":["Motion Planning for Autonomous Vehicles in Sensing Applications"]}]}],"canonical_facts":{"dc:contributor":["Ren, Kui","Computer Science and Engineering"],"dc:creator":["Piao, Sixu; 0000-0003-1800-8716"],"dc:date":["2025-02-21T21:35:43Z","2020"],"dc:description":["Ph.D.","Motion planning is a fundamental research area in robotics, as well as a core functional requirement of autonomous vehicles which have attracted a lot of attention recently. For example, Unmanned Aerial Vehicles (UAVs) have been widely applied in different areas, such as aerial videography, construction inspection, logistics and disaster response. Besides, tremendous amount of efforts from both academia and industry have been devoted into the development of self-driving cars, which are going to change the way transportation looks like in the future. In all kinds of applications of autonomous vehicles, motion planning is the key task of deciding what to do given a certain situation and a goal. In this dissertation, we propose to use UAVs to autonomously perform sensing tasks which are labor-intensive and time-consuming for human. Specifically, we design motion planning algorithms for an UAV to autonomously construct Channel State Information (CSI) map, which is an important process for building indoor localization systems. Indoor localization is a fundamental technique with huge impact on people's daily life due to the popularity of Location-Based Services. Among various approaches proposed in the literature, CSI fingerprinting based approach is shown to be both effective and practical since it can provide adequate accuracy with low overhead for users. However, the major drawback that limits its wide application is the huge amount of human effort required to build the fingerprint map. Therefore, we aim to address this problem by automating the fingerprint map construction process using UAVs. To achieve this goal, we propose novel solutions for solving the challenges in building such an autonomous sensing system. Given the limited battery capacity of commodity UAVs, it is extremely important yet challenging to optimize energy effciency for the UAV during the CSI measurement task. To address this challenge, we formulate the Minimum-Energy Motion Planning (MEMP) problem for the UAV as an optimization problem based on a novel graph model that includes the cost of possible actions for the UAV. Then we show that the formulated MEMP problem can be solved efficiently in practice by presenting a proof of reduction from MEMP to the classic Generalized Traveling Salesman Problem. More importantly, our algorithm greatly improves the energy efficiency for the UAV in the CSI measurement task compared to existing coverage path planning algorithms. Last but not least, we implement the system on a commercially of-the-shelf programmable drone equipped with a CSI measurement module, and also demonstrate by experiments that accurate indoor localization can be achieved using the CSI data collected by our UAV system.","**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/10477/86622"],"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":["computer science"],"dc:title":["Motion Planning for Autonomous Vehicles in Sensing Applications"],"dc:type":["Text","Dissertation"]},"updated_at":"2026-07-27T19:05:32Z"}