Unmanned Ground Vehicles (UGVs) play a vital role in preserving human life during hostile military operations and extend our reach by exploring extraterrestrial worlds during space missions. These systems generally have to operate in unstructured environments which contain dynamic variables and unpredictable obstacles, making the seemingly simple task of traversing from A-B extremely difficult. Terrain is one of the biggest obstacles within these environments as it could potentially cause a vehicle to become stuck and render it useless, therefore autonomous systems must possess the ability to directly sense terrain conditions. Current autonomous vehicles use look-ahead vision systems and passive laser scanners to navigate a safe path around obstacles; however these methods lack detail when considering terrain as they make predictions using estimations of the terrain’s appearance alone. This study establishes a more accurate method of measuring, classifying and monitoring terrain in real-time. A novel instrument for measuring direct terrain features at the wheel-terrain contact interface is presented in the form of the Force Sensing Wheel (FSW). Additionally a classification method using unique parameters of the wheel-terrain interaction is used to identify and monitor terrain conditions in real-time. The combination of both the FSW and real-time classification method facilitates better traversal decisions, creating a more Terrain Capable system.

This thesis examines the problem of large computational loads generated by safety re- lated algorithms for Unmanned Aircraft Systems (UAS). Efficient and accurate methods for multiple sensor fault detection and Sense-And-Avoid systems for UAS are proposed. A novel sensor fault detection method is proposed and tested by simulation. The method detects multiple sensor faults by evaluating normal and faulty hypotheses for each sensor sequentially using measurements obtained from sensors on-board the air- craft. A Six-Degrees-of-Freedom flight model for a Navion aircraft is used to simulate faulty sensor data to test the fault detection method. The proposed sequential fault detection method detects faulty sensors, the update process is fast and maintains a more accurate state-estimate than the parallel fault detection method. For Sense-And-Avoid systems, an efficient method for estimating the probability of conflict between traffic in a non-cooperative environment is proposed. Estimating low probabilities of conflict using 'naive' Direct Monte Carlo method generates a significant computational load. The proposed method uses a technique called Subset Simulation where small failure probabilities are computed as a product of larger conditional failure probabilities - reducing the computational load whilst improving the accuracy of the probability estimates. The utility of the approach is demonstrated by modelling a series of conflicting and potentially conflicting scenarios based on the standard Rules of the Air specified by the International Civil Aviation Organization.

Micro Aerial Vehicles (MAVs), particularly multi-rotor MAVs have gained significant popularity in the autonomous robotics research field. The small size and agility of these aircraft makes them safe to use in contained environments. As such MAVs have numerous applications with respect to both the commercial and research fields, such as Search and Rescue (SaR), surveillance, inspection and aerial mapping. In order for an autonomous MAV to safely and reliably navigate within a given environment the control system must be able to determine the state of the aircraft at any given moment. The state consists of a number of extrinsic variables such as the position, velocity and attitude of the MAV. The most common approach for outdoor operations is the Global Positioning System (GPS). While GPS has been widely used for long range navigation in open environments, its performance degrades significantly in constrained environments and is unusable indoors. As a result state estimation for MAVs in such constrained environments is a popular and exciting research area. Many successful solutions have been developed using laser-range finder sensors. These sensors provide very accurate measurements at the cost of increased power and weight requirements. Cameras offer an attractive alternative state estimation sensor; they offer high information content per image coupled with light weight and low power consumption. As a result much recent work has focused on state estimation on MAVs where a camera is the only exteroceptive sensor. Much of this recent work focuses on single MAVs, however it is the author's belief that the full potential and benefits of the MAV platform can only be realised when teams of MAVs are able to cooperatively perform tasks such as SaR or mapping. Therefore the work presented in this thesis focuses on the problem of vision-based navigation for MAVs from a multi-robot perspective. Multi-robot visual navigation presents a number of challenges, as not only must the MAVs be able to estimate their state from visual observations of the environment but they must also be able to share the information they gain about their environment with other members of the team in a meaningful fashion. The meaningful sharing of observations is achieved when the MAVs have a common frame of reference for both positioning and observations. Such meaningful information sharing is key to achieving cooperative multi-robot navigation. In this thesis two main ideas are explored to address these issues. Firstly the idea of appearance based (re)-localisation is explored as a means of establishing a common reference frame for multiple MAVs. This approach allows a team of MAVs to very easily establish a common frame of reference prior to starting their mission. The common reference frame allows all subsequent operations, such as surveillance or mapping, to proceed with direct cooperative between all MAVs. The second idea focuses on the structure and nature of the inter-robot communication with respect to visual navigation; the thesis explores how a partially distributed architecture can be used to vastly improve the scalability and robustness of a multi-MAV visual navigation framework. A navigation framework would not be complete without a means of control. In the multi-robot setting the control problem is complicated by the need for inter-robot collision avoidance. This thesis presents a MAV trajectory controller based on a combination of classical control theory and distributed Velocity Obstacle (VO) based collision avoidance. Once a means of control is established an autonomous multi-MAV team requires a mission. One such mission is the task of exploration; that is exploration of a previously unknown environment in order to produce a map and/or search for objects of interest. This thesis also addressed the problem of multi-robot exploration using only the sparse interest-point data collected from the visual navigation system. In a multi-MAV exploration scenario the problem of task allocation, assigning areas to each MAV to explore, can be a challenging one. An auction-based protocol is considered to address the task allocation problem. The two applications discussed, VO-based trajectory control and auction-based environment exploration, form two case studies which serve as the partial basis of the evaluation of the navigation solutions presented in this thesis. In summary the visual navigation systems presented in this thesis allow MAVs to cooperatively perform task such as collision avoidance and environment exploration in a robust and efficient manner, with large teams of MAVs. The work presented is a step in the direction of fully autonomous teams of MAVs performing complex, dangerous and useful tasks in the real world.

A high level of survivability is important to protect military personnel and equipment and is central to UK defence policy. Integrated Survivability is the systems engineering methodology to achieve optimum survivability at an affordable cost, enabling a mission to be completed successfully in the face of a hostile environment. “Integrated Helicopter Survivability” is an emerging discipline that is applying this systems engineering approach within the helicopter domain. Philosophically the overall survivability objective is ‘zero attrition’, even though this is unobtainable in practice. The research question was: “How can helicopter survivability be assessed in an integrated way so that the best possible level of survivability can be achieved within the constraints and how will the associated methods support the acquisition process?” The research found that principles from safety management could be applied to the survivability problem, in particular reducing survivability risk to as low as reasonably practicable (ALARP). A survivability assessment process was developed to support this approach and was linked into the military helicopter life cycle. This process positioned the survivability assessment methods and associated input data derivation activities. The system influence diagram method was effective at defining the problem and capturing the wider survivability interactions, including those with the defence lines of development (DLOD). Influence diagrams and Quality Function Deployment (QFD) methods were effective visual tools to elicit stakeholder requirements and improve communication across organisational and domain boundaries. The semi-quantitative nature of the QFD method leads to numbers that are not real. These results are suitable for helping to prioritise requirements early in the helicopter life cycle, but they cannot provide the quantifiable estimate of risk needed to demonstrate ALARP. The probabilistic approach implemented within the Integrated Survivability Assessment Model (ISAM) was developed to provide a quantitative estimate of ‘risk’ to support the approach of reducing survivability risks to ALARP. Limitations in available input data for the rate of encountering threats leads to a probability of survival that is not a real number that can be used to assess actual loss rates. However, the method does support an assessment across platform options, provided that the ‘test environment’ remains consistent throughout the assessment. The survivability assessment process and ISAM have been applied to an acquisition programme, where they have been tested to support the survivability decision making and design process. The survivability ‘test environment’ is an essential element of the survivability assessment process and is required by integrated survivability tools such as ISAM. This test environment, comprising of threatening situations that span the complete spectrum of helicopter operations requires further development. The ‘test environment’ would be used throughout the helicopter life cycle from selection of design concepts through to test and evaluation of delivered solutions. It would be updated as part of the through life capability management (TLCM) process. A framework of survivability analysis tools requires development that can provide probabilistic input data into ISAM and allow derivation of confidence limits. This systems level framework would be capable of informing more detailed survivability design work later in the life cycle and could be enabled through a MATLAB® based approach. Survivability is an emerging system property that influences the whole system capability. There is a need for holistic capability level analysis tools that quantify survivability along with other influencing capabilities such as: mobility (payload / range), lethality, situational awareness, sustainability and other mission capabilities. It is recommended that an investigation of capability level analysis methods across defence should be undertaken to ensure a coherent and compliant approach to systems engineering that adopts best practice from across the domains. Systems dynamics techniques should be considered for further use by Dstl and the wider MOD, particularly within the survivability and operational analysis domains. This would improve understanding of the problem space, promote a more holistic approach and enable a better balance of capability, within which survivability is one essential element. There would be value in considering accidental losses within a more comprehensive ‘survivability’ analysis. This approach would enable a better balance to be struck between safety and survivability risk mitigations and would lead to an improved, more integrated overall design.

This thesis presents stability and performance analyses for switched systems subject to arbitrary and constrained switching signals. Analysis techniques based on common quadratic Lyapunov functions are first introduced as these can be very effective in coping with arbitrary, unconstrained switching signals. However, for systems subject to switching signals which are time constrained, less conservative tools, based on dwell-time analysis, are introduced and extended for the computation of L2-gain estimates. Robust L2-gain and H2 state-feedback controllers syntheses follow from this analysis. An L2 performance analysis tool, for piecewise linear systems, is also given and used to the design of piecewise linear state-feedback controllers. The performance analysis and controller synthesis techniques mentioned above have been applied to the control of the longitudinal axis of the ADMIRE aircraft fighter benchmark model. Simulations show that a switched state-feedback controller provides better tracking than a simple LQR feedback gain.

The use of Unmanned Aerial Vehicles (UAVs) is becoming ever more common by people or organisations who wish to get information about an area quickly and without a human presence. As a result, there has been a concerted effort to develop systems that allow the deployment of UAVs in disaster scenarios, in order to aid first responders with collecting imagery and other sensory data without putting human lives at risk. In particular, work has focused on developing autonomous systems to minimise the involvement of overstretched first responder personnel, and to ensure action can be taken by the UAVs quickly, co-operatively, and with close to optimal results. Key to this work, is the idea of enabling coordinated UAVs to explore a disaster space to discover incidents and then to allow more detailed examination, imagery, or sensing of these locations. Consequently, in this thesis we examine the challenge of coordinating exploratory and task-responsive UAVs in the presence of prior (but uncertain) beliefs about incident locations, and the combination of their roles together. To do this, we first identify the key components of such a system as: path planning, task allocation, and using belief data for predictive UAV placement. Subsequently, we introduce our contributions in the form of a complete, decentralised system for a single explorative path planner to minimise the time to identify incidents, to allocate incidents to UAVs as tasks, and to place UAVs prior to new tasks being found. Having demonstrated the efficacy of this solution in experimental scenarios, we extend the formulation of our explorative path-planning problem to multiple UAVs by constructing a coordinated, factored Monte-Carlo Tree Search algorithm for use in a discretised space representation of a disaster area. Subsequently, we detail the performance of our new algorithm against uncoordinated alternatives using real data from the 2010 Haiti earthquake. We demonstrate the performance benefits of our method via the metric of people discovered in the simulation; showing improvements of up to 23% in cases with ten UAVs. This is the first application of this technique to very large action spaces of the type encountered in realistic disaster scenarios. Finally, we modify our coordinated exploration algorithm to function in a continuous action space. This represents the first example of a continuous factored coordinated Monte-Carlo Tree Search algorithm. We evaluated this algorithm on the same Haiti dataset as the discretised version, but with a new sensor model simulating mobile phone signal detection to represent the types of sensors deployed by first responders. In addition to the benefits of a more realistic model of the environment, we found improvements in survivor localisation times of up to 20% over the discrete algorithm; demonstrating the value in our approach. As such, the contributions presented in this thesis advance the state of the art in UAV coordination algorithms, and represent a progression towards the widespread deployment of autonomous platforms that can aid rescue workers in disaster situations and—ultimately—save lives.

The work presented in this thesis concerns the development of a value driven engineering design assessment framework and its application to the conceptual design of an Unmanned Air System (UAS) to be utilised in a defence application. This research demonstrates the implementation of the value driven design philosophy in this framework, identifying value enhancing designs, with value not converted to monetary worth and as perceived by all stakeholders involved. A multi-criteria and multi-stakeholder decision making analysis is adopted to address their preferences as well as to study their interacting strategic choices. The ultimate objective of this framework is to convert engineering design to a decision making analysis with multiple conflicting objectives of multiple stakeholders considered. This framework is capable of providing a product definition and estimation of all performance and cost related attributes for the conceptual phase. However, instead of pertaining to a single aircraft concept, a broad range of combinations of UAS configurations and geometries is generated by systematically searching alternative concepts and design configurations through a novel parameterization of the aircraft geometric topologies. Value, related to the designed system’s capabilities or performance and lifecycle cost, is used to compare different alternatives in the decision making of engineering design through the appropriate value model. Following a value focused approach, a novel multi-attribute value model is introduced for objectively capturing the stakeholder’s preferences and expectations. Furthermore, a more sophisticated multi-attribute utility model, based on standard Multi-attribute Utility Theory, is employed in the evaluation. Game Theory as an optimization tool is used to develop a novel hybrid cooperative/non-cooperative non-zero sum, complete information game among all involved stakeholders as players. This game successfully addresses the stakeholders’ preferences in a functional outcome-focused way, resolving the high indeterminacy of the alternative designs through a cooperative game. At the same time, their strategic interactions are captured in a process-focused non-cooperative game. Hence, the optimal design is identified through the simultaneous employment of the Nash bargaining solution and the Nash equilibrium.

A Sense-and-Avoid (SAA) capability is required for the safe integration of Unmanned Aerial Vehicles (UAVs) into civilian airspace. Given their safety-critical nature, SAA algorithms must undergo rigorous verification and validation before deployment. The validation of UAV SAA algorithms requires identifying challenging situations that the algorithms have difficulties in handling. By building on ideas from Search-Based Software Testing, this thesis proposes an evolutionary-search-based approach that automatically identifies such situations to support the validation of SAA algorithms. Specifically, in the proposed approach, the behaviours of UAVs under the control of selected SAA algorithms are examined with agent-based simulations. Evolutionary search is used to guide the simulations to focus on increasingly challenging situations in a large search space defined by (the variations of) parameters that configure the simulations. An open-source tool has been developed to support the proposed approach so that the process can be partially automated. Positive results were achieved in a preliminary evaluation of the proposed approach using a simple two-dimensional SAA algorithm. The proposed approach was then further demonstrated and evaluated using two case studies, applying it to a prototype of an industry-level UAV collision avoidance algorithm (specifically, ACAS XU) and a multi-UAV conflict resolution algorithm (specifically, ORCA-3D). In the case studies, the proposed evolutionary-search-based approach was empirically compared with some plausible rivals (specifically, random-search-based approaches and a deterministic-global-search-based approach). The results show that the proposed approach can identify the required challenging situations more effectively and efficiently than the random-search-based approaches. The results also show that even though the proposed approach is a little less competitive than the deterministic-global-search-based approach in terms of effectiveness in relatively easy cases, it is more effective and efficient in more difficult cases, especially when the objective function becomes highly discontinuous. Thus, the proposed evolutionary-search-based approach has the potential to be used for supporting the validation of UAV SAA algorithms although it is not possible to show that it is the best approach.

The ability to quickly fabricate small unmanned aircraft through additive manufacturing methods opens a range of new possibilities for the design and optimisation of these vehicles. In this thesis, we propose a design loop that makes use of surrogate modelling and additive manufacturing to reduce the design and optimisation time of scientific small unmanned aircraft. Additive manufacturing reduces the time and effort required to fabricate a complete aircraft, allowing for rapid design iterations and flight testing. Co-Kriging surrogate models allow data collected from test flights to correct Kriging models trained with numerically simulated data. The resulting model provides physically accurate and computationally cheap aircraft performance predictions. A global optimiser is used to search this model to find an optimal design for a bespoke aircraft. We apply the proposed design loop in a real-world case study. A parameterised joined wing aircraft is optimised to fulfil the mission requirements of a sensorcraft, or a small unmanned aircraft capable of carrying a payload of scientific sensors. Following the proposed design loop, three parametric aircraft were fabricated using additive manufacturing and flight tested. These flight testing data were used to construct a co-Kriging surrogate model capable of being used for the rapid optimisation of future sensorcraft.

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