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Geng, Robert Accepted Thesis Early SP24.pdf (2.43 MB)

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ETD Abstract Container

Abstract Header

Mobile Anchor Point Machine Learning Cooperative Localization for Multiagent Multirotor Systems

Geng, Robert Joseph
http://orcid.org/0009-0000-9105-8299
http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1701442605202068

Abstract Details

2024, Master of Science (MS), Ohio University, Mechanical Engineering (Engineering and Technology).
Aerial Vehicle (UAS) remote and autonomous operation is a growing industry with applications in defense and the commercial space. UAS's can use the Global Positioning System (GPS) to determine their position when signals are available. Operational environments like urban canyons, forested areas, and indoors can block the UAS from receiving GPS signals and prevent the receiver from determining a position, which can hinder the vehicle's ability to successfully complete the mission. Unreliable positioning from GPS data requires UAS's to have an alternative means of localization, which can be accomplished by utilizing ultra-wideband ranging to landmarks and other aerial systems. Machine learning can be used to train models to utilize raw data from intervehicle distance sensors on the agent and anchors to determine the location of the agent without access to GPS data. This work will explore using machine learning as an adaptable localization system using neural networks and gradient descent methods. Fully trained neural networks will be capable of learning specific noise models and performing cooperative localization using unfiltered intervehicle ranges and anchor positions.
Jay Wilhelm (Advisor)
Sergio Ulloa (Committee Member)
Dusan Sormaz (Committee Member)
Chris Bartone (Committee Member)
68 p.
Aerospace Engineering; Computer Science
Machine Learning; Cooperative Localization; Neural Networks; GPS denied navigation; Trilateration; Residual Neural Network

Recommended Citations

Citations

  • Geng, R. J. (2024). Mobile Anchor Point Machine Learning Cooperative Localization for Multiagent Multirotor Systems [Master's thesis, Ohio University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1701442605202068

    APA Style (7th edition)

  • Geng, Robert. Mobile Anchor Point Machine Learning Cooperative Localization for Multiagent Multirotor Systems. 2024. Ohio University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1701442605202068.

    MLA Style (8th edition)

  • Geng, Robert. "Mobile Anchor Point Machine Learning Cooperative Localization for Multiagent Multirotor Systems." Master's thesis, Ohio University, 2024. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1701442605202068

    Chicago Manual of Style (17th edition)

ohiou1701442605202068
56
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This open access ETD is published by Ohio University and OhioLINK.