Date of Award:
Master of Science (MS)
Electrical and Computer Engineering
Accurate estimation of an Unmanned Aerial Vehicle’s (UAV’s) location is critical for the operation of the UAV when it is controlled completely by its onboard processor. This can be particularly challenging in environments in which GPS is not available (GPS-denied). Many of the options previously explored for estimation of a UAV’s location without the use of GPS require more sophisticated processors than can feasibly be mounted on a UAV because of weight, size, and power restrictions. Many options are also aimed at indoor operation without the range capabilities to scale to outdoor operations. This research explores an alternative method of GPS-denied navigation which utilizes line-of-sight measurements to self-describing fiducials to aid in position determination. Each self-describing fiducial is an easily identifiable object fixed at a specific location. Each fiducial relays data containing its specific location to the observing UAV. The UAV can measure its relative position to the fiducial using camera images. This measurement can be combined with measurements from an Inertial Measurement Unit (IMU) to obtain a more accurate estimate of the UAV’s location. In this research, a simulation is used to validate and assess the performance of algorithms used to estimate the UAV’s position using these measurements. This research analyzes the effectiveness of the estimation algorithm when used with various IMUs and fiducial spacings. The effect of how quickly camera images of fiducials can be captured and processed is also analyzed. Preparations for demonstrating this system with hardware are then presented and discussed, including options for fiducial type and a way to measure the true position of the UAV. The results from the simulated scenarios and the hardware demonstration preparation are analyzed, and future work is discussed.
Strate, Amanda J., "Self-Describing Fiducials for GPS-Denied Navigation of Unmanned Aerial Vehicles" (2020). All Graduate Theses and Dissertations. 7988.
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