The usage of unmanned aerial systems (UAS), also called drones, has grown at an increasing rate, with expectations of the number of unmanned aircraft (UA) to triple between 2019 and 2023 as commercial and government usage of UAS increases as per the Federal Aviation Administration. As the usage of UA increases, the probability of a UA crash resulting in injuries of 3rd parties on the ground also increases. The goal of this research was to create a method and software tool that gives the user an accurate representation of the risk to 3rd parties on the ground associated with a given flight plan. The main area of focus was on large rotorcraft and fixed-wing aircraft that are used by the military and that have the potential to do large amounts of damage if a crash were to occur. How unique types of failures affect the ground area at risk and the UA crash characteristics and how these characteristics affect population on the ground were all considered. With this information, a probability of fatality value is calculated, which helps the user determine if the mission risk is acceptable. The ability to optimize this flight path to find the lowest risk flight path is also possible, based upon user specifications. / Master of Science / Understanding the likelihood of an undesired event occurring is vital for the use of any system in the real world. This is especially true in the case of aircraft, were an undesired event can likely cause loss of life. A new area of aircraft that require additional insight into the failure characteristics are unmanned aerial systems, often referred to as drones. Drones do not have a pilot inside the aircraft, who could correct for any failures that might occur. Due to this potential inability to correct for a failure, a method must be developed to gain a better understanding of the potential failures and risks involved in drone operations. The method developed during this work was turned into a software tool, which allows a mission for a drone to be mapped out and the risk to be determined. Due to the drones being unmanned the risk is taken as the expected number of fatalities to the 3rd party individuals on the ground. This expected number of fatalities is determined by the population density of the area the flight is occurring over, and the crash characteristics for the aircraft. These methods and accompanying assumptions are outlined in the body of this work.

This thesis compares three variations of the Bayesian network as an aid for decision-making using uncertain information. After reviewing the basic theory underlying probabilistic graphical models and Bayesian estimation, the thesis presents a user-defined static Bayesian network, a static Bayesian network in which the parameter values are learned from data, and a dynamic Bayesian network with learning. As a basis for the comparison, these models are used to provide a prior assessment of the safety of flight of a small unmanned aircraft, taking into consideration the state of the aircraft and weather. The results of the analysis indicate that the dynamic Bayesian network is more effective than the static networks at predicting safety of flight. / Master of Science / This thesis used probabilities to aid decision-making using uncertain information. This thesis presents three models in the form of networks that use probabilities to aid the assessment of flight safety for a small unmanned aircraft. All three methods are forms of Bayesian networks, graphs that map causal relationships between random variables. Each network models the flight conditions and state of the aircraft; two of the networks are static and one varies with time. The results of the analysis indicate that the dynamic Bayesian network is more effective than the static networks at predicting safety of flight.

Operation of Unmanned Air Vehicles (UAVs) has increased significantly over the past few years. However, routine operation in non-segregated airspace remains a challenge, primarily due to nature of the environment and restrictions and challenges that accompany this. Currently, tight human control is envisaged as a means to achieve the oft quoted requirements of transparency , equivalence and safety. However, the problems of high cost of human operation, potential communication losses and operator remoteness remain as obstacles. One means of overcoming these obstacles is to devolve authority, from the ground controller to an on-board system able to understand its situation and make appropriate decisions when authorised. Such an on-board system is known as an Autonomous System. The nature of the autonomous system, how it should be designed, when and how authority should be transferred and in what context can they be allowed to control the vehicle are the general motivation for this study. To do this, the system must overcome the negative aspects of differentiators that exist between UASs and manned aircraft and introduce methods to achieve required increases in the levels of versatility, cost, safety and performance. The general thesis of this work is that the role and responsibility of an airborne autonomous system are sufficiently different from those of other conventionally controlled manned and unmanned systems to require a different architectural approach. Such a different architecture will also have additional requirements placed upon it in order to demonstrate acceptable levels of Transparency, Equivalence and Safety. The architecture for the system is developed from an analysis of the basic requirements and adapted from a consideration of other, suitable candidates for effective control of the vehicle under devolved authority. The best practices for airborne systems in general are identified and amalgamated with established principles and approaches of robotics and intelligent agents. From this, a decision architecture, capable of interacting with external human agencies such as the UAS Commander and Air Traffic Controllers, is proposed in detail. This architecture has been implemented and a number of further lessons can be drawn from this. In order to understand in detail the system safety requirements, an analysis of manned and unmanned aircraft accidents is made. Particular interest is given to the type of control moding of current unmanned aircraft in order to make a comparison, and prediction, with accidents likely to be caused by autonomously controlled vehicles. The effect of pilot remoteness on the accident rate is studied and a new classification of this remoteness is identified as a major contributor to accidents A preliminary Bayesian model for unmanned aircraft accidents is developed and results and predictions are made as an output of this model. From the accident analysis and modelling, strategies to improve UAS safety are identified. Detailed implementations within these strategies are analysed and a proposal for more advanced Human-Machine Interaction made. In particular, detailed analysis is given on exemplar scenarios that a UAS may encounter. These are: Sense and Avoid , Mission Management Failure, Take Off/Landing, and Lost Link procedures and Communications Failure. These analyses identify the nature of autonomous, as opposed to automatic, operation and clearly show the benefits to safety of autonomous air vehicle operation, with an identifiable decision architecture, and its relationship with the human controller. From the strategies and detailed analysis of the exemplar scenarios, proposals are made for the improvement of unmanned vehicle safety The incorporation of these proposals into the suggested decision architecture are accompanied by analysis of the levels of benefit that may be expected. These suggest that a level approaching that of conventional manned aircraft is achievable using currently available technologies but with substantial architectural design methodologies than currently fielded.

Operation of Unmanned Air Vehicles (UAVs) has increased significantly over the past few years. However, routine operation in non-segregated airspace remains a challenge, primarily due to nature of the environment and restrictions and challenges that accompany this. Currently, tight human control is envisaged as a means to achieve the oft quoted requirements of transparency , equivalence and safety. However, the problems of high cost of human operation, potential communication losses and operator remoteness remain as obstacles. One means of overcoming these obstacles is to devolve authority, from the ground controller to an on-board system able to understand its situation and make appropriate decisions when authorised. Such an on-board system is known as an Autonomous System. The nature of the autonomous system, how it should be designed, when and how authority should be transferred and in what context can they be allowed to control the vehicle are the general motivation for this study. To do this, the system must overcome the negative aspects of differentiators that exist between UASs and manned aircraft and introduce methods to achieve required increases in the levels of versatility, cost, safety and performance. The general thesis of this work is that the role and responsibility of an airborne autonomous system are sufficiently different from those of other conventionally controlled manned and unmanned systems to require a different architectural approach. Such a different architecture will also have additional requirements placed upon it in order to demonstrate acceptable levels of Transparency, Equivalence and Safety. The architecture for the system is developed from an analysis of the basic requirements and adapted from a consideration of other, suitable candidates for effective control of the vehicle under devolved authority. The best practices for airborne systems in general are identified and amalgamated with established principles and approaches of robotics and intelligent agents. From this, a decision architecture, capable of interacting with external human agencies such as the UAS Commander and Air Traffic Controllers, is proposed in detail. This architecture has been implemented and a number of further lessons can be drawn from this. In order to understand in detail the system safety requirements, an analysis of manned and unmanned aircraft accidents is made. Particular interest is given to the type of control moding of current unmanned aircraft in order to make a comparison, and prediction, with accidents likely to be caused by autonomously controlled vehicles. The effect of pilot remoteness on the accident rate is studied and a new classification of this remoteness is identified as a major contributor to accidents A preliminary Bayesian model for unmanned aircraft accidents is developed and results and predictions are made as an output of this model. From the accident analysis and modelling, strategies to improve UAS safety are identified. Detailed implementations within these strategies are analysed and a proposal for more advanced Human-Machine Interaction made. In particular, detailed analysis is given on exemplar scenarios that a UAS may encounter. These are: Sense and Avoid , Mission Management Failure, Take Off/Landing, and Lost Link procedures and Communications Failure. These analyses identify the nature of autonomous, as opposed to automatic, operation and clearly show the benefits to safety of autonomous air vehicle operation, with an identifiable decision architecture, and its relationship with the human controller. From the strategies and detailed analysis of the exemplar scenarios, proposals are made for the improvement of unmanned vehicle safety The incorporation of these proposals into the suggested decision architecture are accompanied by analysis of the levels of benefit that may be expected. These suggest that a level approaching that of conventional manned aircraft is achievable using currently available technologies but with substantial architectural design methodologies than currently fielded. / ©Cranfield University © BAE Systems

Anomaly detection aims to identify the data samples that do not conform to a known normal (regular) behavior. As the definition of an anomaly is often ambiguous, unsupervised and semi-supervised deep learning (DL) algorithms that primarily use unlabeled datasets to model normal (regular) behaviors, are popularly studied in this context. The unmanned aerial system (UAS) can use contextual anomaly detection algorithms to identify interesting objects of concern in applications like search and rescue, disaster management, public security etc. This thesis presents a novel multi-stage framework that supports detection of frames with unknown anomalies, localization of anomalies in the detected frames, and validation of detected frames for incremental semi-supervised learning, with the help of a human operator. The proposed architecture is tested on two new datasets collected for a UAV-based system. In order to detect and localize anomalies, it is important to both model the normal data distribution accurately as well as formulate powerful discriminant (anomaly scoring) techniques. We implement a generative adversarial network (GAN)-based anomaly detection architecture to study the effect of loss terms and regularization on the modeling of normal (regular) data and arrive at the most effective anomaly scoring method for the given application. Following this, we use incremental semi-supervised learning techniques that utilize a small set of labeled data (obtained through validation from a human operator), with large unlabeled datasets to improve the knowledge-base of the anomaly detection system. / Master of Science / Anomaly detection aims to identify the data samples that do not conform to a known normal (regular) behavior. As the definition of an anomaly is often ambiguous, most techniques use unlabeled datasets, to model normal (regular) behaviors. The availability of large unlabeled datasets combined with novel applications in various domains, has led to an increasing interest in the study of anomaly detection. In particular, the unmanned aerial system (UAS) can use contextual anomaly detection algorithms to identify interesting objects of concern in applications like search and rescue (SAR), disaster management, public security etc. This thesis presents a novel multi-stage framework that supports detection and localization of unknown anomalies, as well as the validation of detected anomalies, for incremental learning, with the help of a human operator. The proposed architecture is tested on two new datasets collected for a UAV-based system. In order to detect and localize anomalies, it is important to both model the normal data distribution accurately and formulate powerful discriminant (anomaly scoring) techniques. To this end, we study the state-of-the-art generative adversarial networks (GAN)-based anomaly detection algorithms for modeling of normal (regular) behavior and formulate effective anomaly detection scores. We also propose techniques to incrementally learn the new normal data as well as anomalies, using the validation provided by a human operator. This framework is introduced with the aim to support temporally critical applications that involve human search and rescue, particularly in disaster management.

This thesis details the design and construction of an anechoic chamber for acoustic testing and measurements of large UAS propellers. Three propellers are considered, as they are common propeller designs used for large UAS today. The knowledge and practices involved with acoustic testing and measurements in anechoic chambers, as well as the results of noise studies related to large UAS, are not widely available due in large part to the limited availability and use of large UAS in the public domain. Using established principles related to fundamental acoustic theory and propeller noise, the aeroacoustic noise from large UAS propellers was measured to study and evaluate the reduction in total aerodynamic noise. This data and research provides the ability to evaluate propeller noise in relation to the overall detectability of large unmanned aircraft systems.

he digital revolution in the latter part of the twentieth century has resulted in the increased use and development of Cyber-Physical Systems. Two of which are Unmanned Aerial Systems (UAS) and Integrated Vehicle Health Management (IVHM). Both are relatively new areas of interest to academia, military, and commercial organisations. Designing IVHM for a UAS is no easy task – the complexity inherent in UAS, with projects involving multiple partners/organisations; multiple stakeholders are also interested in the IVHM. IVHM needs to justify itself throughout the life of the UAS, and the lack of established knowledge makes it hard to know where to start. The establishment and analysis of requirements for IVHM on UAS is known to be important and costly – and for IVHM a complex one. There are multiple stakeholders to satisfy and ultimately the needs of the customer, all demanding different things from the IVHM, and with limited resources they need to be prioritised. There are also many hindrances to this: differences in language between stakeholders, customers failing to see the benefits, scheduling conflicts, no operational data. The contribution to knowledge in this thesis is the IVHM Requirements Deployment (IVHM-RD) – a method for a designer of UAS IVHM to build a tool which can consolidate and evaluate the various stakeholder’s requirements. When the tool is subsequently populated with knowledge from individual Subject Matter Experts (SMEs), it provides a prioritised set of IVHM requirements. The IVHM-RD has been tested on two design cases and generalised for the use with other designs. Analysis of the process has been conducted and in addition the results of the design cases have been analysed in three ways: how the results relate to each design case, comparison between the two cases, and how much the relationships between requirements are understood. A validation exercise has also been conducted to establish the legitimacy of the IVHM-RD process. This research is likely to have an impact on the elicitation and analysis of IVHM requirements for UAS – and the wider design process of IVHM. The IVHM-RD process should also prove of use to designers of IVHM on other assets. The populations of the design cases also provide information which could be useful to other designer and future research.

The United States infrastructure, e.g. roads and bridges, are in a critical condition. Inspection, monitoring, and maintenance of these infrastructure in the traditional manner can be expensive, dangerous, time-consuming, and tied to human judgment (the inspector). Non-contact methods can help overcoming these challenges. In this dissertation two aspects of non-contact methods are explored: inspections using unmanned aerial systems (UASs), and conditions assessment using image processing and machine learning techniques. This presents a set of investigations to determine a guideline for remote autonomous bridge inspections.

In wildlife management, using cutting edge technology and science to monitor greater sage-grouse (Centrocercus urophasianus; sage-grouse) populations, enables land managers to better assess the impact of their management decisions. Having precise counts of sage-grouse lek attendance, and specifically male lek attendance, is an important metric used to evaluate population status and response to conservation actions (Gifford et.al, 2013, Dahlgren et al., 2016). Leks are seasonal breeding sites where males perform a ritualistic courtship dance for females. Our case study examined if a Remotely Piloted Aircraft (RPA) was effective in detecting, and counting, sage-grouse during the lek season (early March to late April). More specifically, this research used a Forward-Looking Infrared (FLIR) camera (a thermal camera) to detect sage-grouse and determine body temperatures of individual sage-grouse to determine if temperature data can be used to identify displaying male sage-grouse. These images can be used to document the activity and behavior of sage-grouse and can be revisited at future times to document changes in bird numbers as well as perform additional statistical analyses. We conducted 5 flights and on a per-flight basis, we identified an average of 4.4 displaying males, 13.4 non-displaying males, and 5.6 female sage-grouse. We found that the average size and average maximum temperature of the three sage-grouse categories differed where females were smaller with an average body size of 325 cm2, an average maximum temperature of 14.6 C ̊, and a smaller average thermal range of 2.47 C ̊. Non-displaying male body size was approximately 488 cm2, with a maximum average temperature of 17.2 C ̊, and an average thermal range of 4.66C ̊. Displaying male body size was the largest at approximately 655 cm2, an average maximum temperature of 27.5C ̊, with the largest average range of 12.39C ̊. Our study demonstrates that RPA and infrared technology can be used to conduct accurate sage-grouse lek attendance counts. Further, results of this study will also provide a guideline for the use of RPA’s to monitor sage-grouse and other lekking species.

In recent years unmanned aircraft systems (UAS) have been successfully used in a wide variety of applications. Their value as surveillance platforms has been proven repeatedly in both military and civilian domains. As substitutes to human inhabited aircraft, they fulfill missions that are dull, dirty and dangerous. Representative examples of successful use of UAS are in areas including battlefield assessment, reconnaissance, port security, wildlife protection, wildfire detection, search and rescue missions, border security, resource exploration and oil spill detection. The reliance of almost every UAS application on the ability to sense, detect, see and avoid from a distance has motivated this thesis, attempting to further investigate this issue. In particular, among the various types of UAS, small scale unmanned rotorcraft or Vertically Take-off and Landing, (VTOL) vehicles have been chosen to serve as the sensor carrier platforms because of their operational flexibility. In this work we address the problem of object identification and tracking in a largely unknown dynamic environment under the additional constraint of real-time operation and limited computational power. In brief, the scope of this thesis can be stated as follows: Design a vision system for a small autonomous helicopter that will be able to: Identify arbitrary objects using a minimal description model and a-priori knowledge; Track objects of interest; Operate in real-time; Operate in a largely unknown, dynamically changing, outdoors environment under the following constraints: Limited processing power and payload; Low cost, off-the-shelf components. The main design directives remain that of real-time execution and low price, high availability components. It is in a sense an investigation for the minimum required hardware and algorithmic complexity to accomplish the desired tasks. After development, the system was evaluated as to its suitability in an array of applications. The ones that were chosen for that purpose were: Detection of semi-concealed objects; Detection of a group of ground robots; Traffic monitoring. Adequate performance was demonstrated in all of the above cases.