{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/157188"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/157188","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Autonomous UAV Navigation using Millimeter Wave Radar","abstract":"We present the design, implementation and evaluation of MilliNavigator, an autonomous navigation system for drones capable of mapping, path-planning, self-localizing, and navigating in indoor environments by leveraging strategically-placed millimeter wave anchors. Autonomous drones are an increasingly relevant tool for completing and automating hard-to-reach tasks. State of the art navigation systems rely primarily on cameras and GPS for environmental perception and self-localization. These solutions can impose restrictions on existing systems, which limit their navigable environment to well-lit, outdoors, and unobstructed paths. This thesis presents MilliNavigator, the first system to use millimeter wave radar and anchor-aware path planning to achieve high accuracy, 6DOF, online localization. By generating a localization precision score map from known anchor deployments, the system jointly optimizes travel distance and localization performance. We implemented and evaluated MilliNavigator on a drone built with commercial, off-the-shelf parts. We ran over 165 successful missions across 7 different tag deployments. Our system successfully achieved 7.9cm overall median error and had a 90th percentile error of less than 21cm.","abstract_html":"We present the design, implementation and evaluation of MilliNavigator, an autonomous navigation system for drones capable of mapping, path-planning, self-localizing, and navigating in indoor environments by leveraging strategically-placed millimeter wave anchors. Autonomous drones are an increasingly relevant tool for completing and automating hard-to-reach tasks. State of the art navigation systems rely primarily on cameras and GPS for environmental perception and self-localization. These solutions can impose restrictions on existing systems, which limit their navigable environment to well-lit, outdoors, and unobstructed paths. This thesis presents MilliNavigator, the first system to use millimeter wave radar and anchor-aware path planning to achieve high accuracy, 6DOF, online localization. By generating a localization precision score map from known anchor deployments, the system jointly optimizes travel distance and localization performance. We implemented and evaluated MilliNavigator on a drone built with commercial, off-the-shelf parts. We ran over 165 successful missions across 7 different tag deployments. Our system successfully achieved 7.9cm overall median error and had a 90th percentile error of less than 21cm.","abstract_has_math":false,"creators":["Herrera, Joshua I."],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. 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Autonomous drones are an increasingly relevant tool for completing and automating hard-to-reach tasks. State of the art navigation systems rely primarily on cameras and GPS for environmental perception and self-localization. These solutions can impose restrictions on existing systems, which limit their navigable environment to well-lit, outdoors, and unobstructed paths. This thesis presents MilliNavigator, the first system to use millimeter wave radar and anchor-aware path planning to achieve high accuracy, 6DOF, online localization. By generating a localization precision score map from known anchor deployments, the system jointly optimizes travel distance and localization performance. We implemented and evaluated MilliNavigator on a drone built with commercial, off-the-shelf parts. We ran over 165 successful missions across 7 different tag deployments. Our system successfully achieved 7.9cm overall median error and had a 90th percentile error of less than 21cm."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M.Eng."]},{"key":"dc:title","label":"Title","values":["Autonomous UAV Navigation using Millimeter Wave Radar"]}]}],"canonical_facts":{"dc:contributor.advisor":["Adib, Fadel"],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science"],"dc:creator":["Herrera, Joshua I."],"dc:date.accessioned":["2024-10-09T18:27:08Z"],"dc:date.available":["2024-10-09T18:27:08Z"],"dc:date.issued":["2024-09"],"dc:description.abstract":["We present the design, implementation and evaluation of MilliNavigator, an autonomous navigation system for drones capable of mapping, path-planning, self-localizing, and navigating in indoor environments by leveraging strategically-placed millimeter wave anchors. Autonomous drones are an increasingly relevant tool for completing and automating hard-to-reach tasks. State of the art navigation systems rely primarily on cameras and GPS for environmental perception and self-localization. These solutions can impose restrictions on existing systems, which limit their navigable environment to well-lit, outdoors, and unobstructed paths. This thesis presents MilliNavigator, the first system to use millimeter wave radar and anchor-aware path planning to achieve high accuracy, 6DOF, online localization. By generating a localization precision score map from known anchor deployments, the system jointly optimizes travel distance and localization performance. We implemented and evaluated MilliNavigator on a drone built with commercial, off-the-shelf parts. We ran over 165 successful missions across 7 different tag deployments. 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