Guanhong Gao

Date of Award


Document Type

Thesis - Open Access

Degree Name

Master of Science in Aerospace Engineering


Aerospace Engineering

Committee Chair

Dr. Richard Prazenica

First Committee Member

Dr. Hever Moncayo

Second Committee Member

Dr. Troy Henderson


As the number of potential applications for Unmanned Aerial Vehicles (UAVs) keeps rising steadily, the chances that these devices will operate in close proximity to static or dynamic obstacles also increases. Therefore, collision avoidance is an important challenge to overcome for Unmanned Aerial Vehicle operations. Electro-optical devices have several advantages such as light weight, low cost, low algorithm requirements with respect to computational power and possibly night vision capabilities. Therefore, vision-based Unmanned Aerial Vehicle collision avoidance has received considerable attention. Although much progress has been made in collision avoidance systems (CAS), most approaches are focused on two-dimensional environments. In order to operate in complex three-dimensional urban environments, three-dimensional collision avoidance systems are required. This thesis develops a three-dimensional vision-based collision avoidance system to provide sense and avoid capabilities for unmanned aerial vehicles (UAVs) operating in complex urban environments with multiple static and dynamic collision threats. This collision avoidance system is based on the principle of proportional navigation (Pro-Nav), which states that a collision will occur when the line-of-sight (LOS) angles to another object remain constant. According to this guidance law, monocular electro-optical devices can be implemented on Unmanned Aerial Vehicles, which can provide measurements of the line-of-sight angles, indicating potential collision threats. In this thesis, the guidance laws were applied to a nonlinear, six degree-of-freedom Unmanned Aerial Vehicles model in different two-dimensional or three dimensional simulation environments with a varying number of static and dynamic obstacles.