Aerospace Engineering Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/2737
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Item An Observer for Estimating Translational Velocity from Optic Flow and Radar(2011) Gerardi, Steven Anthony; Humbert, James S; Aerospace Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This thesis presents the development of a discrete time observer for estimating state information from optic flow and radar measurements. It is shown that estimates of translational and rotational speed can be extracted using a least squares inversion for wide fields of view or, with the addition of a Kalman Filter, for small fields of view. The approach is demonstrated in a simulated three dimensional urban environment on an autonomous quadrotor micro-air-vehicle (MAV). A state feedback control scheme is designed, whereby the gains are found via static H∞, and implemented to allow trajectory following. The proposed state estimation scheme and feedback method are shown to be sufficient for enabling autonomous navigation of an MAV. The resulting methodology has the advantages of computational speed and simplicity, both of which are imperative for implementation on MAVs due to stringent size, weight, and power requirements.Item Stochastic Properties of Wide Field Integrated Optic Flow Measurements(2009) Owen, Scott; Humbert, James S; Aerospace Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Wide Field Integration (WFI) is a biologically inspired method of spatially decomposing optic flow estimates to extract relevant behavioral cues for navigation. In this thesis, a framework is developed that allows the direct application of a Kalman filter to improve the state information extracted from optic flow measurements. In addition, the noise properties of optic flow measurements are characterized, and an architecture to propagate the uncertainty in optic flow measurements to WFI state estimates is formalized. The closed-loop performance of a ground robot maneuvering in a straight tunnel using WFI outputs is then analyzed using three different algorithms to compute optic flow. The performance of the robot is characterized by its ability to track the tunnel centerline, and the accuracy of the WFI state estimates are compared with the true state estimates using a visual motion capture system. Lastly, the Kalman filter is implemented on a ground robot and the modified closed-loop performance is analyzed.