Autonomous unmanned air vehicles (UAVs) are critical to current and future military, civil, and commercial operations. Despite their importance, no previous textbook has accessibly introduced UAVs to students in the engineering, computer, and science disciplines--until now. Small Unmanned Aircraft provides a concise but comprehensive description of the key concepts and technologies underlying the dynamics, control, and guidance of fixed-wing unmanned aircraft, and enables all students with an introductory-level background in controls or robotics to enter this exciting and important area. The authors explore the essential underlying physics and sensors of UAV problems, including low-level autopilot for stability and higher-level autopilot functions of path planning. The textbook leads the student from rigid-body dynamics through aerodynamics, stability augmentation, and state estimation using onboard sensors, to maneuvering through obstacles. To facilitate understanding, the authors have replaced traditional homework assignments with a simulation project using the MATLAB/Simulink environment. Students begin by modeling rigid-body dynamics, then add aerodynamics and sensor models. They develop low-level autopilot code, extended Kalman filters for state estimation, path-following routines, and high-level path-planning algorithms. The final chapter of the book focuses on UAV guidance using machine vision. Designed for advanced undergraduate or graduate students in engineering or the sciences, this book offers a bridge to the aerodynamics and control of UAV flight.
UA.I.B.K20 Preflight familiarization, inspection, and actions for aircraft operations. According to 14 CFR part 107, who is responsible for determining the performance of a small unmanned aircraft?
UA.I.B.K21b (Refer to FAA-CT-8080-2H, Figure 78.) You have been contracted to inspect towers located approximately 4NM southwest of the Sioux Gateway (SUX) airport operating an unmanned aircraft. What is the maximum altitude above ground level (AGL) that you are authorized to operate over the top of the towers?
UA.II.A.K1b General airspace: Class C controlled airspace. According to 14 CFR part 107 the remote pilot in command (PIC) of a small unmanned aircraft planning to operate within Class C airspace
UA.IV.A.K1b General loading and performance: Balance, stability, and center of gravity. To ensure that the unmanned aircraft center of gravity (CG) limits are not exceeded, follow the aircraft loading instructions specified in the
UA.V.B.K2 (Refer to FAA-CT-8080-2H, Figure 21, Area 1.) After receiving authorization from ATC to operate a small UA near Minot International airport (MOT) while the control tower is operational, which radio communication frequency could be used to monitor manned aircraft and ATC communications?
UA.V.B.K6a Sources for airport data: Aeronautical charts. (Refer to FAA-CT-8080-2H, Figure 26, area 4.) You have been hired to inspect the tower under construction at 46.9N and 98.6W, near Jamestown Regional (JMS). What must you receive prior to flying your unmanned aircraft in this area?
UA.V.B.K6a Sources for airport data: Aeronautical charts. (Refer to FAA-CT-8080-2H, Figure 20, area 3.) With ATC authorization, you are operating your small unmanned aircraft approximately 4 SM southeast of Elizabeth City Regional Airport (ECG). What hazard is indicated to be in that area?
Abstract:Coastal tidal marshes are essential ecosystems for both economic and ecological reasons. They necessitate regular monitoring as the effects of climate change begin to be manifested in changes to marsh vegetation healthiness. Small unmanned aerial systems (sUAS) build upon previously established remote sensing techniques to monitor a variety of vegetation health metrics, including biomass, with improved flexibility and affordability of data acquisition. The goal of this study was to establish the use of RGB-based vegetation indices for mapping and monitoring tidal marsh vegetation (i.e., Spartina alterniflora) biomass. Flights over tidal marsh study sites were conducted using a multi-spectral camera on a quadcopter sUAS near vegetation peak growth. A number of RGB indices were extracted to build a non-linear biomass model. A canopy height model was developed using sUAS-derived digital surface models and LiDAR-derived digital terrain models to assess its contribution to the biomass model. Results found that the distance-based RGB indices outperformed the regular radio-based indices in coastal marshes. The best-performing biomass models used the triangular greenness index (TGI; R2 = 0.39) and excess green index (ExG; R2 = 0.376). The estimated biomass revealed high biomass predictions at the fertilized marsh plots in the Long-Term Research in Environmental Biology (LTREB) project at the study site. The sUAS-extracted canopy height was not statistically significant in biomass estimation but showed similar explanatory power to other studies. Due to the lack of biomass samples in the inner estuary, the proposed biomass model in low marsh does not perform as well as the high marsh that is close to shore and accessible for biomass sampling. Further research of low marsh is required to better understand the best conditions for S. alterniflora biomass estimation using sUAS as an on-demand, personal remote sensing tool.Keywords: unmanned aircraft; biomass; coastal; wetland; RGB; drone 2b1af7f3a8