The objective of the master’s thesis work is to investigate how an autonomous underwater vehicle (AUV) should act in an underwater tunnel environment. The thesis proposes sensors, control strategies, mission statement, among others, required for tunnel assignments. A behavior-based control (BBC) strategy has been developed to control the AUV. The BBC is used in the middle level of the vehicle control, i.e. the reactive control system which describes how the AUV navigates through a tunnel, while other events are considered. The control strategy has also been separated into two parts, and these are: controlling the AUV’s heading and controlling the AUV to a desired distance from the tunnel wall. To be able to evaluate the performance of the system, a graphical user interface (GUI) has been developed. The GUI enables the operator to change control settings during simulations. Two proposed control strategies are presented with simulated results. / Syftet med examensarbetet är att undersöka hur en autonom undervattensfarkost (AUV) bör agera i en undervattenstunnel miljö. Avhandlingen föreslår sensorer, reglerstrategier, uppdragsbeskrivning med mera som krävs för tunneluppdrag. En beteendebaserad (behavior-based) reglerstrategi har utvecklats för att styra AUV:n. Reglerstrategin används i mellersta nivån i farkostens reglering, det vill säga den reaktiva regleringen som beskriver hur farkosten ska styra genom en tunnel samtidigt som andra händelser beaktas. Reglerstrategin har även delats upp i två delar: reglering av AUV:ns kurs och reglering av AUV:n till ett önskat avstånd från tunnelns vägg. För att kunna verifiera funktionaliteten av systemet så har även ett grafiskt användargränssnitt utvecklats. Gränssnittet möjliggör att man kan ändra reglerparametrar under en simulering. Två föreslagna reglerstrategier presenteras med tillhörande resultat.

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

In the last decade, the development and deployment of Uninhabited Airborne Vehicles (UAVs) has increased dramatically. This has in turn increased the desire to operate UAVs in civilian-airspace. Current UAV platforms can be integrated into civilian-airspace, with other air traffic, however they place a high burden on their human operators in order to do so. In order to meet the competing objectives of improved integration and low operator workload it will be necessary to increase the intelligence on-board the UAV. This thesis presents the results of the research which has been conducted into increasing the on-board intelligence of the UAV. The intent in increasing the on-board intelligence is to improve the ability of a UAV to integrate into civilian-airspace whilst also reducing the workload placed upon the UAV's operator. The research has focused upon increasing the intelligence in two key areas: mission planning; and mission piloting. Mission planning is the process of determining how to fly from one location to another, whilst avoiding entities (eg. airspace boundaries and terrain) on the way. Currently this task is typically performed by a trained human operator. This thesis presents a novel multidisciplinary approach for enabling a UAV to perform, on-board, its own mission planning. The novel approach draws upon techniques from the 3D graphics and robotics fields in order to enable the UAV to perform its own mission planning. This enables the UAV's operator to provide the UAV with the locations (waypoints) to fly to. The UAV will then determine for itself how to reach the locations safely. This relieves the UAV's operator of the burden of performing the mission planning for the UAV. As part of this novel approach to on-board mission planning, the UAV constructs and maintains an on-board situational awareness of the airspace environment. Through techniques drawn from the 3D graphics field the UAV becomes capable of constructing and interacting with a 3D digital representation of the civilian-airspace environment. This situational awareness is a fundamental component of enabling the UAV to perform its own mission planning and piloting. The mission piloting research has focused upon the areas of collision avoidance and communications. These are tasks which are often handled by a human operator. The research identified how these processes can be performed on-board the UAV through increasing the on-board intelligence. A unique approach to collision avoidance was developed, which was inspired by robotics techniques. This unique approach enables the UAV to avoid collisions in a manner which adheres to the applicable Civil Aviation Regulations, as defined by the Civil Aviation Safety Authority (CASA) of Australia. Furthermore, the collision avoidance algorithms prioritise avoiding collisions which would result in a loss of life or injury. Finally, the communications research developed a natural language-based interface to the UAV. Through this interface, the UAV can be issued commands and can also be provided with updated situational awareness information. The research focused upon addressing issues related to using natural language for a civilian-airspace-integrated UAV. This area has not previously been addressed. The research led to the definition of a vocabulary targeted towards a civilian-airspace-integrated UAV. This vocabulary caters for the needs of both Air Traffic Controllers and general UAV operators. This requires that the vocabulary cater for a diverse range of skill levels. The research established that a natural language-based communications system could be applied to a civilian-airspace-integrated UAV for both command and information updates. The end result of this research has been the development of the Intelligent Mission Planner and Pilot (IMPP). The IMPP represents the practical embodiment of the novel algorithms developed throughout the research. The IMPP was used to evaluate the performance of the algorithms which were developed. This testing process involved the execution of over 3000 hours of simulated flights. The testing demonstrated the high performance of the algorithms developed in this research. The research has led to the successful development of novel on-board situational awareness, mission planning, collision avoidance and communications capabilities. This thesis presents the development, implementation and testing of these capabilities. The algorithms which provide these capabilities go beyond the existing body of knowledge and provide a novel contribution to the established research. These capabilities enable the UAV to perform its own mission planning, avoid collisions and receive natural language-based communications. This provides the UAV with a direct increase in the intelligence on-board the UAV, which is the core objective of this research. This increased on-board intelligence improves the integration of the UAV into civilian-airspace whilst also reducing the operator's workload.

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