This work focuses on path planning for an autonomous UAV to tour and cover multiple regions in the shortest time. The three challenges to be solved are - finding the right optimal order to tour the neighborhoods, determining entry and exit points to neighborhoods, and covering each neighborhood. Two approaches have been explored and compared to achieve this goal - a TSP - Greedy and TSP - Dijkstra's. Both of them use a TSP solution to determine the optimal order of touring. They also use the same back and forth motion to cover each region. However, while the first approach uses a brute force to determine the the next closest node of entry or exit, the second approach utilizes the Dijkstra's algorithm to compute all possible paths to every node in the graph, and therefore choose the shortest pairs of entry and exit for each region, that would generate the shorter path, overall. The main contribution of this work is to implement an existing algorithm to combine the touring and covering problem, and propose a new algorithm to perform the same. Empirical results comparing performances of both approaches are included. Hardware experiments are performed on a spraying hexacopter, using the TSP - Greedy approach. Unique system characteristics are studied to make conclusions about stability of the platform. Future directions are identified to improve both software and hardware performance. / Master of Science / In a world with a rapidly growing population and resources depleting faster, increasing efficiency and productivity has become paramount. Until now, automation has helped cope with the world’s increasing demand for food. However, studies have shown that automation in itself will be insufficient in improving crop output in the coming years. Fortunately, another technology that is taking big leaps in terms of technological advances - Information Technology, when combined with automation, presents itself as a viable option. This takes agriculture towards a site-specific approach for all crop monitoring, growth and protection activities and is know as Precision Agriculture. Spraying fluids on crops using a UAV is on of the prominent problems being researched in this field. This work presents two approaches - TSP - Greedy and TSP - Dijkstra’s to tour or visit and spray multiple regions that have been previously identified in the shortest time. While the TSP - Greedy algorithm is an implementation of an existing approach, the TSP - Dijkstra’s algorithm is a new approach proposed in this work. The solution to TSP or Traveling Salesman Problem generates the optimal order to visit the regions. The ’Greedy’ or ’Dijsktra’s’ approaches define entry and exit points for each region, that gives the shortest path overall. Images of areas with weed afflicted regions marked on them are used as the input for this algorithm. The TSP-Greedy approach is used in performing hardware experiments. Data collected from these experiments are used to analyze performance of an Unmanned Aerial Vehicle (UAV) platform. Water has been used as the spraying fluid for testing the sprayer assembly. GPS or Global Positioning System is used for navigation of the UAV. Future directions are identified to improve both software and hardware performance.

Flue-cured tobacco (Nicotiana tabacum L.) is a high value-per-acre crop that is intensively managed to optimize the yield of high quality cured leaf. Aerial remote sensing, specifically unmanned aerial vehicles (UAVs), present flue-cured tobacco producers and researchers with a potential tool for scouting and crop management. A two-year study, conducted in Southside Virginia at the Southern Piedmont Agricultural Research and Extension Center and on commercial farms, assessed the potential of aerial remote sensing in flue-cured tobacco. The effort encompassed two key objectives. First, examine the use of the enhanced normalized difference vegetation index (ENDVI) for separating flue-cured tobacco varieties and nitrogen rates. Secondly, develop hyperspectral indices and/or machine learning classification models capable of detecting Phytophthora nicotianae (black shank) incidence in flue-cured tobacco. In 2017, UAV-acquired ENDVI surveys demonstrated the ability to consistently separate between flue-cured tobacco varieties and nitrogen rates from topping to harvest. In 2018, ENDVI revealed significant differences among N-rates as early as 34 days after transplanting. Two hyperspectral indices were developed to detect black shank incidence based on differences in the spectral profiles of asymptomatic flue-cured tobacco plants compared to those with black shank symptoms. Testing of the indices showed significant differences between the index values of healthy and symptomatic plants (alpha = 0.05). In addition, the indices were able to detect black shank symptoms pre-symptomatically (alpha = 0.09). Subspace linear discriminant analysis, a machine learning classification, was also used for prediction of black shank incidence with up to 85.7% classification accuracy. / Master of Science / Unmanned Aerial Vehicle’s (UAVs) or drones, as they are commonly referred to, may have potential as a tool in flue-cured tobacco research and production. UAVs combined with sensors and cameras provide the opportunity to gather a large amount of data on a particular crop, which may be useful in crop management. Given the intensive management of flue-cured tobacco, producers may benefit from extra insight on how to better assess threats to yield such as under-fertilization and disease pressure. A two-year study was conducted in Southside Virginia at the Southern Piedmont Agricultural Research and Extension Center and on commercial farms. There were two objectives to this effort. First, assess the ability of UAV-acquired multispectral near-infrared imagery to separate flue-cured tobacco varieties and nitrogen rates. Secondly, develop hyperspectral indices and machine learning models that can accurately predict the incidence of black shank in flue-cured tobacco. Flue-cured tobacco nitrogen rates were significantly different in 2017 from 59 days after transplanting to harvest using UAV-acquired near-infrared imagery. In 2018, heavy rainfall may have led to nitrogen leaching from the soil resulting in nitrogen rates being significantly different as early as 34 days after transplanting. The imagery also showed a significant relationship with variety maturation type in the late stages of crop development during ripening. Two hyperspectral indices were developed and one machine learning model was trained. Each had the ability to detect black shank incidence in fluecured tobacco pre-symptomatically, as well as separated black shank infested plants from healthy plants.

Unmanned Aerial Systems (UAS) are aircraft without a human pilot on board. They are comprised of a ground-based autonomous or human operated control system, an unmanned aerial vehicle (UAV) and a communication, command and control (C3) link between the two systems. UAS are widely used in military warfare, wildfire mapping, aerial photography, etc primarily to collect and process large amounts of data. While they are highly efficient in data collection and processing, they are susceptible to software espionage and data manipulation. This research aims to provide a novel solution to enhance the security of the flight controller thereby contributing to a secure and robust UAS. The proposed solution begins by introducing a new technology in the domain of flight controllers and how it can be leveraged to overcome the limitations of current flight controllers. The idea is to decouple the applications running on the flight controller from the task of data validation. The authenticity of all external data processed by the flight controller can be checked without any additional overheads on the flight controller, allowing it to focus on more important tasks. To achieve this, we introduce an adjacent controller whose sole purpose is to verify the integrity of the sensor data. The controller is designed using minimal resources from the reconfigurable logic of an FPGA. The secondary I/O processor is implemented on an incipient Zynq SoC based flight controller. The soft-core microprocessor running on the configurable logic of the FPGA serves as a first level check on the sensor data coming into the flight controller thereby forming a trusted boundary layer. / Master of Science / UAV is an aerial vehicle which does not carry a human operator, uses aerodynamic forces to lift the vehicle and is controlled either autonomously by an onboard computer or remotely controlled by a pilot on ground. The software application running on the onboard computer is known as flight controller. It is responsible for guidance and trajectory tracking capabilities of the aircraft. A UAV consists of various sensors to measure parameters such as orientation, acceleration, air speed, altitude, etc. A sensor is a device that detects or measures a physical property. The flight controller continuously monitors the sensor values to guide the UAV along a specific trajectory. Successful maneuvering of a UAV depends entirely on the data from sensors, thus making it vulnerable to sensor data attacks using fabricated physical stimuli. These kind of attacks can trigger an undesired response or mask the occurrence of actual events. In this thesis, we propose a novel approach where we perform a first-level check on the incoming sensor data using a dedicated low cost hardware designed to protect data integrity. The data is then forwarded to the flight controller for further access and processing.

The development and implementation of a flight test program for an unmanned aircraft is a multidisciplinary challenge. This thesis presents the development and implementation of a rigorous test program for the flight test of a Geometrically Scaled Joined Wing SensorCraft Remotely Piloted Vehicle from concept through successful flight test. The design methodology utilized in the development of the test program is presented, along with the extensive formal review process required for the approval of the test plan by the Air Force Research Laboratory. The design, development and calibration of a custom instrumentation package is also presented along with the setup, procedure and results from all testing. Results are presented for a wind tunnel test for air data boom calibration, propulsion system static thrust testing, a bifilar pendulum test for experimental calculation of mass moments of inertia, a static structural loading test for structural design validation, a full taxi test and a successful first flight. / Master of Science

In situations where information infrastructure is destroyed or not available, on-demand information infrastructure is pivotal for the success of rescue missions. In this paper, a drone-carried on-demand information infrastructure for long-distance WiFi transmission system is developed. It can be used in the areas including emergency response, public event, and battlefield. In years development, the Drone WIFI System has developed from single-CPU platform, twin-CPU platform, Atmega2560 platform to NVIDIA Jetson TX2 platform. By the upgrade of the platform, the hardware shows more and more reliable and higher performance which make the application of the platform more and more exciting. The latest TX2 platform can provide real time and thermal video transmission, also application of deep learning of object recognition and target tracing. All these up-to-date technology brings more application scenarios to the system. Therefore, the system can serve more people in more scenarios.

In situations where information infrastructure is destroyed or not available, on-demand information infrastructure is pivotal for the success of rescue missions. In this paper, a drone-carried on demand information infrastructure for long-distance WiFi transmission system is developed. It can be used in the areas including emergency response, public event, and battlefield. The WiFi network can be connected to the Internet to extend WiFi access to areas where WiFi and other Internet infrastructures are not available. In order to establish a local area network to propagate WIFI service, directional antennas and wireless routers are used to create it. Due to unstable working condition on the flying drones, a precise heading turning stage is designed to maintain the two directional antennas facing to each other. Even if external interferences change the heading of the drones, the stages will automatically rotate back to where it should be to offset the bias. Also, to maintain the same flying altitude, a ground controller is designed to measure the height of the drones so that the directional antennas can communicate to each other successfully. To verify the design of the whole system, quite a few field experiments were performed. Experiments results indicates the design is reliable, viable and successful. Especially at disaster areas, it’ll help people a lot.

This dissertation progressively addresses research problems related to the trajectory optimization for fuel cell powered UAVs, from propulsion system model development, to optimal trajectory analyses and optimal trajectory applications. A dynamic model of a fuel cell powered UAV propulsion system is derived by combining a fuel cell system dynamic model, an electric motor dynamic model, and a propeller performance model. The influence of the fuel cell system dynamics on the optimal trajectories of a fuel cell powered UAV is investigated in two phases. In the first phase, the optimal trajectories of a fuel cell powered configuration and that of a conventional gas powered configuration are compared for point-to-point trajectory optimization problems with different performance index functions. In the second phase, the influence of the fuel cell system parameters on the optimal fuel consumption cost of the minimum fuel point-to-point optimal trajectories is investigated. This dissertation also presents two applications for the minimum fuel point-to-point optimal trajectories of a fuel cell powered UAV: three-dimensional minimum fuel route planning and path generation, and fuel cell system size optimization with respect to a UAV mission.

Thesis (PhD (Electrical and Electronic Engineering))--Stellenbosch University, 2008. / A strategy for the design of an effective, practically feasible, robust, computationally efficient autopilot for three dimensional manoeuvre flight control of Unmanned Aerial Vehicles is presented. The core feature of the strategy is the design of attitude independent inner loop acceleration controllers. With these controllers implemented, the aircraft is reduced to a point mass with a steerable acceleration vector when viewed from an outer loop guidance perspective. Trajectory generation is also simplified with reference trajectories only required to be kinematically feasible. Robustness is achieved through uncertainty encapsulation and disturbance rejection at an acceleration level. The detailed design and associated analysis of the inner loop acceleration controllers is carried out for the case where the airflow incidence angles are small. For this case it is shown that under mild practically feasible conditions the inner loop dynamics decouple and become linear, thereby allowing the derivation of closed form pole placement solutions. Dimensional and normalised non-dimensional time variants of the inner loop controllers are designed and their respective advantages highlighted. Pole placement constraints that arise due to the typically weak non-minimum phase nature of aircraft dynamics are developed. A generic, aircraft independent guidance control algorithm, well suited for use with the inner loop acceleration controllers, is also presented. The guidance algorithm regulates the aircraft about a kinematically feasible reference trajectory. A number of fundamental basis trajectories are presented which are easily linkable to form complex three dimensional manoeuvres. Results from simulations with a number of different aircraft and reference trajectories illustrate the versatility and functionality of the autopilot. Key words: Aircraft control, Autonomous vehicles, UAV flight control, Acceleration control, Aircraft guidance, Trajectory tracking, Manoeuvre flight control.

Thesis (MScEng (Electrical and Electronic Engineering))--University of Stellenbosch, 2009. / This thesis details the development and comparison of two linear control systems that performhover control for a vertical take-off and landing unmanned aerial vehicle. A non-linear mathematical model of the aircraft dynamics is developed. A classical successive loop closure control approach is presented, which applies static gains to the decoupled model around hover. A variable gain approach is presented using optimal control, which linearises the aircraftmodel around its state at fixed time steps. Simulation performance and robustness results are examined for both systems. Different aspects of both controller design processes and results are compared, including navigational performance, robustness and ease of use.

Thesis (MScEng (Electrical and Electronic Engineering))--University of Stellenbosch, 2009. / This thesis describes the successful development of an autopilot for an unmanned radio controlled helicopter. It presents a non-linear helicopter model. An adaptive linearised model is derived and used to design a controller. The adaptive full state controller is tested in various ways, including two aerobatic manoeuvres. A number of analyses are performed on the controller, including its robustness to parameter changes, noisy estimates, wind and processing power. The controller is compared with a non-adaptive counterpart, which leads to the conception, design and analysis of a much improved control structure. Practical flight test results are presented and analysed. In some instances available literature was reworked and re-derived to produce a genericmodel-controller package that can easily be adapted for helicopters of any make, model and size.