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 climb angle, minimum turn radius, maximum turn rate, and maximum bank angle. Applications in UAV control and mission planning are also proposed.

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 urban navigation missions included reactive path following, sensor noise, and flight test design and execution. The main challenge was for an SUAV to successfully navigate through a mountainous canyon by reactively altering its own preplanned path to avoid canyon walls and other stationary obstacles. A robust path following method for SUAVs that uses a vector field approach to track functionally curved paths is presented along with flight test results. In these results, the average tracking error for an SUAV following a variety of curved paths is 3.4~m for amplitudes ranging between 10 and 100~m and spatial periods between 125 and 500~m. Additionally, a reactive path following method is presented that allows a UAV to continually offset or bias its planned path as distance information from the left and right ranging sensors is computed. This allows the UAV to to center itself between potential hazards even with imperfect waypoint path planning. Flight results of an SUAV reactively navigating through mountainous canyons experimentally verify the feasibility of this approach. In a flight test through Goshen Canyon in central Utah, an SUAV biased its planned path by 3 to 10~m to the right as it flew to center itself through the canyon and avoid the possibility of crashing into a canyon wall.

Inexpensive, rapidly deployable, camera-equipped Unmanned Aerial Vehicle (UAV) systems can potentially assist with a huge number of tasks. However, in many cases such as wilderness search and rescue (WiSAR), the potential users of the system may not be trained as pilots. Simple interface concepts can be used to build an interaction layer that allows an individual with minimal operator training to use the system to facilitate a search or inspection task. We describe an analysis of WiSAR as currently accomplished and show how a UAV system might fit into the existing structure. We then discuss preliminary system design efforts for making UAV-enabled search possible and practical. Finally, we present both a carefully controlled experiment and partially structured field trials that illustrate principles for making UAV-assisted search a reality. Our experiments show that the traditional method for controlling a camera-enabled UAV is significantly more difficult than integrated methods. Success and troubles during field trials illustrate several desiderata and information needs for a UAV search system.

Mapping their location and extent is a critical step in noxious weed management. One of the most common methods of mapping noxious weeds is to walk the perimeter of each patch with a handheld GPS receiver. This is the method used at Camp Williams, a National Guard Bureau training facility in Utah where this study was conducted. It was proposed that a low-cost Unmanned Aerial Vehicle (UAV) that made use of a hobbyist remote control airplane equipped with a Global Positioning System (GPS) receiver and digital camera could be used along with automated post-processing techniques to reduce the cost of weed mapping compared to the on foot method. Two noxious weeds were studied: musk thistle (Carduus nutans) and dalmation toadflax (Linaria dalmatica). The musk thistle was visually identifiable in the imagery but the dalmation toadflax was confused with yellow sweet clover (Melilotus officinalis). It was found that after the automated post-processing the photos were not positioned well enough to produce a consistent and accurate weed perimeter. A supervised classification was attempted with imagery of the musk thistle, however, the accuracy of the classification was too low to be able to identify the weed perimeter from the classification. To achieve accurate results the photos had to be registered to a base image and the perimeter of each patch hand digitized. The time it took to do so increased the costs well above the on foot method. A number of improvements to the UAV could make the image registration step unnecessary. There are other applications for which this low cost UAV could be used.

Vertical Take-Off and Land (VTOL) Unmanned Air Vehicles (UAVs) possess several desirable characteristics, such as being able to hover and take-off or land in confined areas. One type of VTOL airframe, the tailsitter, has all of these advantages, as well as being able to fly in the more energy-efficient level flight mode. The tailsitter can track trajectories that successfully transition between hover and level flight modes. Three methods for performing transitions are described: a simple controller, a feedback linearization controller, and an adaptive controller. An autopilot navigational state machine with appropriate transitioning between level and hover waypoints is also presented. The simple controller is useful for performing a immediate transition. It is very quick to react and maintains altitude during the maneuver, but tracking is not performed in the lateral direction. The feedback linearization controller and adaptive controller both perform equally well at tracking transition trajectories in lateral and longitudinal directions, but the adaptive controller requires knowledge of far fewer parameters.

Autonomous miniature air vehicles (MAVs) are becoming increasingly popular platforms for the collection of data about an area of interest for military and commercial applications. Two challenges that often present themselves in the process of collecting this data. First, winds can be a significant percentage of the MAV's airspeed and can affect the analysis of collected data if ignored. Second, the majority of MAV's video is transmitted using RF analog transmitters instead of the more desirable digital video due to the computational intensive compression requirements of digital video. This two-part thesis addresses these two challenges. First, this thesis presents an innovative method for estimating the wind velocity using an optical flow sensor mounted on a MAV. Using the flow of features measured by the optical flow sensor in the longitudinal and lateral directions, the MAV's crab-angle is estimated. By combining the crab-angle with measurements of ground track from GPS and the MAV's airspeed, the wind velocity is computed. Unlike other methods, this approach does not require the use of a “varying” path (flying at multiple headings) or the use of magnetometers. Second, this thesis presents an efficient and effective method for video compression by drastically reducing the computational cost of motion estimation. When attempting to compress video, motion estimation is usually more than 80% of the computation required to compress the video. Therefore, we propose to estimate the motion and reduce computation by using (1) knowledge of camera locations (from available MAV IMU sensor data) and (2) the projective geometry of the camera. Both of these methods are run onboard a MAV in real time and their effectiveness is demonstrated through simulated and experimental results.

A quadrotor is an omnidirectional unmanned air vehicle that is suitable for indoor flight because of its ability to hover and maneuver in confined spaces. The remote operation of this type of vehicle is difficult due to a lack of sensory perception; typically, the view from the onboard camera is the only information transmitted to the pilot. This thesis proposes using force feedback exerted by the command input device on the hand of the pilot to assist in avoiding collisions while navigating in indoor environments. Five candidate algorithms are presented for calculating the forces to be felt by the pilot based on the quadrotor's position and velocity in the indoor environment. The candidates include a parametric algorithm based on the dynamics of the quadrotor, two time-to-impact algorithms, and two algorithms that employ virtual springs between the quadrotor and obstacles. A method of incorporating the position of the command input device to improve the usability and effectiveness of the algorithms is also presented. A framework for simulating the quadrotor dynamics, indoor environment, and force feedback algorithms is described. In the simulation, the pilot commands a simulated quadrotor, using a commercial haptic interface, as it flies in an indoor environment. The pilot receives force feedback cues as the quadrotor navigates around obstacles. Two methods of control were used for the simulation. In the first method, displacements of the haptic interface correspond to velocity commands to the quadrotor. In the second method, displacements of the input correspond to desired roll and pitch commands. Two user study experiments, one for each control method, were performed to compare the force feedback algorithms in simulation. The results of the velocity control experiment suggest that higher force levels help to avoid collisions and that the time to impact algorithm results in fewer collisions than having no force, but is not significantly better than the other algorithms. The results of the angle control experiment suggest that the time to impact algorithm is clearly the best in terms of hits and hit length and has no disadvantages compared to the other algorithms. Finally, to demonstrate the force feedback algorithms and software in a real-world environment, the system was interfaced with a physical quadrotor. The quadrotor system is described and the results of the tests are presented.

We present a method of performing precision landings of a vertical take-off and landing (VTOL) unmanned air vehicle (UAV) with the use of an onboard vision sensor and information about the aircraft's orientation and altitude above ground level (AGL). A method for calculating the 3-dimensional location of the UAV relative to a ground target of interest is presented as well as a navigational controller to position the UAV above the target. A method is also presented to prevent the UAV from moving in a way that will cause the ground target of interest to go out of view of the UAV's onboard camera. These methods are tested in simulation and in hardware and resulting data is shown. Hardware flight testing yielded an average position estimation error of 22 centimeters. The method presented is capable of performing precision landings of VTOL UAV's with submeter accuracy.

Unmanned air vehicles (UAVs) have become an important area of research. UAVs are used in many environments which may have previously unknown obstacles or sources of danger. This research addresses the problem of obstacle mapping and path planning while the UAV is in flight. Online obstacle mapping is achieved through the use of a multi-resolution map. As sensor information is received, a quadtree is built up to hold the information based upon the uncertainty associated with the measurement. Once a quadtree map of obstacles is built up, we desire online path re-planning to occur as quickly as possible. We introduce the idea of a quadtree rapidly-exploring random tree (RRT), which will be used as the online path re-planning algorithm. This approach implements a variable sized step instead of the fixed-step size usually used in the RRT algorithm. This variable step uses the structure of the quadtree to determine the step size. The step size grows larger or smaller based upon the size of the area represented by the quadtree it passes through. Finally this approach is tested in a simulation environment. The results show that the quadtree RRT requires fewer steps on average than a standard RRT to find a path through an area. It also has a smaller variance in the number of steps taken by the path planning algorithm in comparison to the standard RRT.

In this thesis we develop methods that facilitate an aerial rendezvous between two air vehicles. The objective of this research is to produce a method that can be used to insert a miniature air vehicle behind a rendezvous vehicle and then track that vehicle to enable a visual rendezvous. For this research we assume the rendezvous vehicle is following a relatively stable and roughly elliptical orbit. Path planners and controllers have been developed that can be used to effectively intercept the rendezvous vehicle by inserting the MAV onto the orbit of interest. A method for planning and following time-optimal Dubins airplane interception paths between a miniature air vehicle and the rendezvous vehicle is presented. We demonstrate how a vector field path following a scheme can be used for navigation along these time-optimal Dubins airplane paths. A post-orbit insertion tracking method is also presented which can be used to track the target vehicle on an arbitrarily oriented elliptical orbit while maintaining a specified following distance. We also present controllers that can be used for disturbance rejection during the orbit-insertion and interception operations. All of these methods were implemented in simulation and with hardware. Results from these implementations are presented and analyzed.