Multirotor UAS Sense and Avoid with Sensor Fusion

<p>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.</p>

Name thesis:degree_name

Master of Science in Unmanned and Autonomous Systems Engineering

Level thesis:degree_level

Thesis - Open Access

Discipline thesis:degree_discipline

Electrical, Computer, Software, and Systems Engineering

Year

2019

Author dc:creator

• Buchholz, Jonathan Mark

• UAS
• drones
• multirotor
• sensor fusion
• Automotive Engineering

Repository record dc:identifier

https://commons.erau.edu/edt/496

OAI identifier oai:identifier

oai:commons.erau.edu:edt-1496