Date of Award
Level of Access Assigned by Author
Master of Engineering (ME)
Second Committee Member
Third Committee Member
Additional Committee Members
As Unmanned Aerial Vehicle (UAV), or “drone” applications expand, new methods for sensing, navigating and avoiding obstacles need to be developed. The project applies an Extended Kalman Filter (EKF) to a simulated quadcopter vehicle though Matlab in order to estimate not only the vehicle state but the world state around the vehicle. The EKF integrates multiple sensor readings from range sensors, IMU sensors, and radiation sensors and combines this information to optimize state estimates. The result is an estimated world map to be used in vehicle navigation and obstacle avoidance.
The simulation handles the physics behind the vehicle flight. As a result of the motors there will be two primary forces acting on the vehicle; thrust and drag. These forces provide linear and rotational accelerations. These values are integrated to determine vehicle flight path and the vehicle state. Coupled into the integration process is the ability to control motor speed. Thus, a controller is built into the vehicle such that desired vehicle states (position, velocity, and orientation) can be achieved. Additionally, sensing methods are simulated following known detection procedures and statistics to provide realistic sensing models.
The EKF is used here due to non-linear components of the estimation model. The estimation model includes the states for the vehicle, nearby world objects, and nearby radiation measurements. By combining IMU and GPS data with additional measurement data for an EKF, an estimation method attempts to combine all three types of information to build state estimates with minimized error. The EKF outputs the estimated state including vehicle, obstacle, and radioactive source locations. This information is used to avoid collisions and to identify and localize radioactive sources.
Brackett, Shawn S., "Integrated Environment and Proximity Sensing for UAV Applications" (2017). Electronic Theses and Dissertations. 2796.