{"id":{"repo_id":"baylor","oai_identifier":"oai:baylor-ir.tdl.org:2104/10359"},"canonical_url":"https://search.dev.ndltd.org/etd/baylor/oai:baylor-ir.tdl.org:2104/10359","repository":{"repo_id":"baylor","name":"Baylor University","base_url":"https://baylor-ir.tdl.org/server/oai/request"},"display":{"title":"A neural inspired grid cell aid for robot intertial navigation.","abstract":"Accurate position information is needed for a robot's guidance and control systems. Therefore, the navigation system is foundational for a robot's interactions with the world. This thesis explores a biologically inspired method of navigation with the goal of improving navigational accuracy. The desire to integrate brain-inspired methods with conventional signal processing methods is based on animals' innate ability to successfully navigate through habitats. Therefore, it is reasonable to explore the way animals process brain signals to navigate and leverage it for robot navigation. This thesis uses a combinatorial model of map formation and localization with grid cells to aid dead reckoning with an accelerometer and gyroscope to show grid cells are a viable aid in idiothetic navigation. The results show that the grid cell aided navigation systems shows better performance with longer paths and higher noise values with an improvement of 57.24 cm for a 32 m path with 3 σ noise.","abstract_html":"Accurate position information is needed for a robot's guidance and control systems. Therefore, the navigation system is foundational for a robot's interactions with the world. This thesis explores a biologically inspired method of navigation with the goal of improving navigational accuracy. The desire to integrate brain-inspired methods with conventional signal processing methods is based on animals' innate ability to successfully navigate through habitats. Therefore, it is reasonable to explore the way animals process brain signals to navigate and leverage it for robot navigation. This thesis uses a combinatorial model of map formation and localization with grid cells to aid dead reckoning with an accelerometer and gyroscope to show grid cells are a viable aid in idiothetic navigation. The results show that the grid cell aided navigation systems shows better performance with longer paths and higher noise values with an improvement of 57.24 cm for a 32 m path with 3 σ noise.","abstract_has_math":false,"creators":["Martinez, Moises, 1993-"],"institution":"Baylor University.","degree_name":"M.S.E.C.E.","degree_level":"Masters","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Koziol, Scott M."],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-05","date_published":"2018-05","updated_at":"2026-07-24T01:07:49Z","subjects":["Grid cells.","Navigation.","Inertial navigation system.","Vector navigation.","Comparison.","Analysis."],"languages":["en"],"rights":["Baylor University works are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 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The desire to integrate brain-inspired methods with conventional signal processing methods is based on animals' innate ability to successfully navigate through habitats. Therefore, it is reasonable to explore the way animals process brain signals to navigate and leverage it for robot navigation. This thesis uses a combinatorial model of map formation and localization with grid cells to aid dead reckoning with an accelerometer and gyroscope to show grid cells are a viable aid in idiothetic navigation. The results show that the grid cell aided navigation systems shows better performance with longer paths and higher noise values with an improvement of 57.24 cm for a 32 m path with 3 σ noise."],"dc:format.mimetype":["application/pdf"],"dc:identifier.uri":["https://hdl.handle.net/2104/10359"],"dc:language.iso":["en"],"dc:rights":["Baylor University works are protected by copyright. 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