Browsing by Subject "Unmanned Aerial Vehicle"

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    Autonomous altitude estimation of a miniature helicopter using a single onboard camera.
    (2010-04) Cherian, Anoop
    Autonomous estimation of the altitude of an Unmanned Aerial Vehicle (UAV) is extremely important when dealing with flight maneuvers like landing, steady flight, etc. Vision based techniques for solving this problem have been underutilized. In this thesis, we propose a new algorithm to estimate the altitude of a UAV from top-down aerial images taken from a single on-board camera. We use a semi-supervised machine learning approach to solve the problem. The basic idea of our technique is to learn the mapping between the texture information contained in an image to a possible altitude value. We learn an over complete sparse basis set from a corpus of unlabeled images capturing the texture variations. This is followed by regression of this basis set against a training set of altitudes. Finally, a spatio-temporal Markov Random Field is modeled over the altitudes in test images, which is maximized over the posterior distribution using the MAP estimate by solving a quadratic optimization problem with L1 regularity constraints. The method is evaluated in a laboratory setting with a real helicopter and is found to provide promising results with sufficiently fast turnaround time.
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    Model-based Fault Detection For Low-cost UAV Actuators
    (2016-09) Lakshminarayan, Inchara
    The focus of this thesis is on the use of analytical redundancy to improve the reliability of low-cost unmanned aerial vehicles (UAVs). Specifically, a model-based fault detection algorithm is designed and tested for one critical UAV component: a servo-actuator. As the name suggests, a key requirement to developing this type of fault detection algorithm on actuators is the availability of an accurate actuator model. This is accomplished by developing a dedicated Arduino based experimental test-bed to analyze servos. Using input-output data from these experiments, a second/third-order dynamic model is identified for healthy actuators using system identification methods in MATLAB software. Using the identified model, a fault detection filter is designed based on polynomial basis vectors to generate a residual proportional to fault. The performance of the fault detection algorithm is experimentally tested on both healthy and faulty actuators and the detection thresholds are set. Finally, the actuator model and the fault detection filter are validated using actuator commands from recent flight tests conducted at the University of Minnesota.