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Full-Text Articles in Engineering
Vision-Based Target Geo-Location Using A Fixed-Wing Miniature Air Vehicle, D. Blake Barber, Joshua D. Redding, Timothy W. Mclain, Randal W. Beard, Clark N. Taylor
Vision-Based Target Geo-Location Using A Fixed-Wing Miniature Air Vehicle, D. Blake Barber, Joshua D. Redding, Timothy W. Mclain, Randal W. Beard, Clark N. Taylor
Faculty Publications
This paper presents a method for determining the GPS location of a ground-based object when imaged from a fixed-wing miniature air vehicle (MAV). Using the pixel location of the target in an image, with measurements of MAV position and attitude, and camera pose angles, the target is localized in world coordinates. The main contribution of this paper is to present four techniques for reducing the localization error. In particular, we discuss RLS filtering, bias estimation, flight path selection, and wind estimation. The localization method has been implemented and flight tested on BYU’s MAV testbed and experimental results are presented demonstrating …
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.
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.
Forest Fire Monitoring Using Multiple Unmanned Air Vehicles, Timothy Mclain, Randal W. Beard, Ryan S. Holt, Joseph W. Egbert, Justin M. Bradley, Clark N. Taylor
Forest Fire Monitoring Using Multiple Unmanned Air Vehicles, Timothy Mclain, Randal W. Beard, Ryan S. Holt, Joseph W. Egbert, Justin M. Bradley, Clark N. Taylor
Faculty Publications
The ability to gather and process information on the condition of forest fires is essential to fighting the fires in a cost-effective, safe, and efficient manner. While high-altitude, long-endurance (HALE) unmanned air vehicles (UAVs) are currently used for fire surveillance; they are an expensive and scarce resource. As a proposed alternative, low-altitude, short-endurance (LASE) UAVs offer lower costs, quicker response times, and high- resolution information. In recent years, advances in solid-state sensor and autopilot technology have made LASE UAVs a feasible alternative. This paper overviews a current research project conducted by Brigham Young University and NASA Ames. In the project, …
Flight Testing Small, Electric Powered Unmanned Aerial Vehicles, Jon N. Ostler
Flight Testing Small, Electric Powered Unmanned Aerial Vehicles, Jon N. Ostler
Theses and Dissertations
Flight testing methods are developed to find the drag polar for small UAVs powered by electric motors with fixed-pitch propellers. Wind tunnel testing was used to characterize the propeller-motor efficiency. The drag polar was constructed using data from flight tests. The proposed methods were implemented for a small UAV. A drag polar was found for this aircraft with CDo equal to 0.021, K1 equal to 0.229, and K2 equal to -0.056. This drag polar was then used to find the following performance parameters; maximum velocity, minimum velocity, velocity for maximum range, velocity for maximum endurance, maximum rate of climb, maximum …
Learning Real-Time A* Path Planner For Unmanned Air Vehicle Target Sensing, Jason K. Howlett, Timothy W. Mclain, Michael A. Goodrich
Learning Real-Time A* Path Planner For Unmanned Air Vehicle Target Sensing, Jason K. Howlett, Timothy W. Mclain, Michael A. Goodrich
Faculty Publications
This paper presents a path planner for sensing closely-spaced targets from a fixed-wing unmanned air vehicle (UAV) having a specified sensor footprint. The planner is based on the learning real-time A* (LRTA*) search algorithm and produces dynamically feasible paths that accomplish the sensing objectives in the shortest possible distance. A tree of candidate paths that span the area of interest is created by assembling primitive turn and straight sections of a specified step size in a sequential fashion from the starting position of the UAV. An LRTA* search of the tree produces feasible paths any time during its execution and …
Remote Terrain Navigation For Unmanned Air Vehicles, Stephen R. Griffiths
Remote Terrain Navigation For Unmanned Air Vehicles, Stephen R. Griffiths
Theses and Dissertations
There are many applications for which small unmanned aerial vehicles (SUAVs) are well suited, including surveillance, reconnaissance, search and rescue, convoy support, and short-range low-altitude perimeter patrol missions. As technologies for microcontrollers and small sensors have improved, so have the capabilities of SUAVs. These improvements in SUAV performance increase the possibility for hazardous missions through mountainous and urban terrain in the successful completion of many of these missions. The focus of this research was on remote terrain navigation and the issues faced when dealing with limited onboard processing and limited payload and power capabilities. Additional challenges associated with canyon and …
Design Of 'Iris', A Small Autonomous Surveillance Uav, Jennifer Boyce, Ryan Carr, Donovan Chipman, Greg Larson, Nathan Hopkins, Doug F. Hunsaker, W. Jerry Bowman
Design Of 'Iris', A Small Autonomous Surveillance Uav, Jennifer Boyce, Ryan Carr, Donovan Chipman, Greg Larson, Nathan Hopkins, Doug F. Hunsaker, W. Jerry Bowman
Mechanical and Aerospace Engineering Faculty Publications
This paper documents the design process used for a small autonomous surveillance UAV. The most significant requirements for the plane were size (man-packable), endurance (about 1 hour) and cost (essentially disposable). The plane that resulted, named "Iris", is a tailless plane with a 45 cm wing span and a total mass of less than 200g. During flight tests, it achieved an endurance of 52 minutes. The estimated cost to manufacture the planes was $343, excluding the autopilot.