Date of Award
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
Dr. Sam Siewert
First Committee Member
Dr. Richard Stansbury
Second Committee Member
Dr. Douglas Isenberg
Third Committee Member
Dr. Steven Bruder
Fourth Committee Member
Dr. 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.
Scholarly Commons Citation
Buchholz, Jonathan Mark, "Multirotor UAS Sense and Avoid with Sensor Fusion" (2019). Doctoral Dissertations and Master's Theses. 496.
https://commons.erau.edu/edt/496
