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Maximizing Miniature Aerial Vehicles, Stephen Griffiths, Jeffery Brian Saunders, Andrew Curtis, Blake Barber, Timothy W. Mclain, Randal W. Beard
Maximizing Miniature Aerial Vehicles, Stephen Griffiths, Jeffery Brian Saunders, Andrew Curtis, Blake Barber, Timothy W. Mclain, Randal W. Beard
Faculty Publications
Despite the tremendous potential demonstrated by miniature aerial vehicles (MAV) in numerous applications, they are currently limited to operations in open air space, far away from obstacles and terrain. To broaden the range of applications for MAVs, methods to enable operation in environments of increased complexity must be developed. In this article, we presented two strategies for obstacle and terrain avoidance that provide a means for avoiding obstacles in the flight path and for staying centered in a winding corridor.
Random City Generator Technical Report, Brandon Call
Random City Generator Technical Report, Brandon Call
Faculty Publications
The Brigham Young University (BYU) Multiple Agent Intelligent Coordination and Control (MAGICC) research lab researchs many aspects of small unmanned air vehicles (UAV). To facilitate this research, students have created a UAV simulator called Aviones. In order to increase the capability of Aviones to simulate an urban environment, the ability to draw buildings, streets and vehicles have been added. This document outlines how cities are created and how a researcher can use them in conjunction with Aviones.
Vector Field Path Following For Small Unmanned Aerial Vehicles, Derek R. Nelson, D. Blake Barber, Timothy W. Mclain, Randal W. Beard
Vector Field Path Following For Small Unmanned Aerial Vehicles, Derek R. Nelson, D. Blake Barber, Timothy W. Mclain, Randal W. Beard
Faculty Publications
This paper presents a new method for unmanned aerial vehicle path following using vector fields to represent desired ground track headings to direct the vehicle onto the desired path. The key feature of this approach is that ground track heading error and lateral following error approach zero asymptotically even in the presence of constant wind disturbances. Methods for following straight-line and circularorbit paths, as well as combinations of straight lines and arcs, are presented. Experimental results validate the effectiveness of this path following approach for small air vehicles flying in high-wind conditions.
Vision-Based Target Localization From A Fixed-Wing Miniature Air Vehicle, Joshua D. Redding, Timothy W. Mclain, Randal W. Beard, Clark N. Taylor
Vision-Based Target Localization From A Fixed-Wing Miniature Air Vehicle, Joshua D. Redding, Timothy W. Mclain, Randal W. Beard, Clark N. Taylor
Faculty Publications
This paper presents a method for localizing a ground-based object when imaged from a small fixed-wing unmanned aerial vehicle (UAV). Using the pixel location of the target in an image, with measurements of UAV position and attitude, and camera pose angles, the target is localized in world coordinates. This paper presents a study of possible error sources and localization sensitivities to each source. The localization method has been implemented and experimental results are presented demonstrating the localization of a target to within 11 m of its known location.