Unmanned vehicles are increasingly common in our world today. Self-driving ground vehicles and unmanned aerial vehicles (UAVs) such as quadcopters have become the fastest growing area of automated vehicles research. These systems use three main processes to autonomously travel from one location to another: guidance, navigation, and controls (GNC). Guidance refers to the process of determining a desired path of travel or trajectory, affecting velocities and orientations. Examples of guidance activities include path planning and obstacle avoidance. Effective guidance decisions require knowledge of one’s current location. Navigation systems typically answer questions such as: “Where am I? What is my orientation? How fast am I going?” Finally, the process is tied together when controls are implemented. Controls use navigation estimates (e.g., “Where I am now?”) and the desired trajectory from guidance processes (e.g., “Where do I want to be?”) to control the moving parts of the system to accomplish relevant goals.
Navigation in autonomous vehicles involves intelligently combining information from several sensors to produce accurate state estimations. To date, global positioning systems(GPS) occupy a crucial place in most navigation systems. However, GPS is not universally reliable. Even when available, GPS can be easily spoofed or jammed, rendering it useless. Thus, navigation within GPS-denied environments is an area of deep interest in both military and civilian applications. Image-aided inertial navigation is an alternative navigational solution in GPS-denied environments. One form of image-aided navigation measures the bearing from the vehicle to a feature or landmark of known location using a single lens imager, such as a camera, to deduce information about the vehicle’s position and attitude.
This work uncovers and explores several of the impacts of trajectories and land mark distributions on the navigation information gained from this type of aiding measurement. To do so, a modular system model and extended Kalman filter (EKF) are described and implemented. A quadrotor system model is first presented. This model is implemented and then used to produce sensor data for several trajectories of varying shape, altitude, and landmark density. Next, navigation data is produced by running the sensor data through an EKF. The data is plotted and examined to determine effects of each variable. These effects are then explained. Finally, an equation describing the quantity of information in each measurement is derived and related to the patterns seen in the data. The resulting equation is then used to explain selected patterns in the data. Other uses of this equation are presented, including applications to path planning and landmark placement.
| Identifer | oai:union.ndltd.org:UTAHS/oai:digitalcommons.usu.edu:etd-7996 |
| Date | 01 December 2017 |
| Creators | Lamoreaux, Ryan D. |
| Publisher | DigitalCommons@USU |
| Source Sets | Utah State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | All Graduate Theses and Dissertations |
| Rights | Copyright for this work is held by the author. Transmission or reproduction of materials protected by copyright beyond that allowed by fair use requires the written permission of the copyright owners. Works not in the public domain cannot be commercially exploited without permission of the copyright owner. Responsibility for any use rests exclusively with the user. For more information contact digitalcommons@usu.edu. |
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