Date of Award

Fall 12-2019

Access Type

Thesis - Open Access

Degree Name

Master of Science in Unmanned and Autonomous Systems Engineering

Department

Electrical, Computer, Software, and Systems Engineering

Committee Chair

Sam Siewert

First Committee Member

Richard Stansbury

Second Committee Member

Douglas Isenberg

Third Committee Member

Steven Bruder

Fourth Committee Member

Iacopo Gentilini

Abstract

In this thesis, the key concepts of independent autonomous Unmanned Aircraft Systems (UAS) are explored including obstacle detection, dynamic obstacle state estimation, and avoidance strategy. This area is explored in pursuit of determining the viability of UAS Sense and Avoid (SAA) in static and dynamic operational environments. This exploration is driven by dynamic simulation and post-processing of real-world data. A sensor suite comprised of a 3D Light Detection and Ranging (LIDAR) sensor, visual camera, and 9 Degree of Freedom (DOF) Inertial Measurement Unit (IMU) was found to be beneficial to autonomous UAS SAA in urban environments. Promising results are based on to the broadening of available information about a dynamic or fixed obstacle via pixel-level LIDAR point cloud fusion and the combination of inertial measurements and LIDAR point clouds for localization purposes. However, there is still a significant amount of development required to optimize a data fusion method and SAA guidance method.

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