This thesis investigates new convex optimisation techniques for motion and pose estimation. Numerous computer vision problems can be formulated as optimisation problems. These optimisation problems are generally solved via linear techniques using the singular value decomposition or iterative methods under an L2 norm minimisation. Linear techniques have the advantage of offering a closed-form solution that is simple to implement. The quantity being minimised is, however, not geometrically or statistically meaningful. Conversely, L2 algorithms rely on iterative estimation, where a cost function is minimised using algorithms such as Levenberg-Marquardt, Gauss-Newton, gradient descent or conjugate gradient. The cost functions involved are geometrically interpretable and can statistically be optimal under an assumption of Gaussian noise. However, in addition to their sensitivity to initial conditions, these algorithms are often slow and bear a high probability of getting trapped in a local minimum or producing infeasible solutions, even for small noise levels. In light of the above, in this thesis we focus on developing new techniques for finding solutions via a convex optimisation framework that are globally optimal. Presently convex optimisation techniques in motion estimation have revealed enormous advantages. Indeed, convex optimisation ensures getting a global minimum, and the cost function is geometrically meaningful. Moreover, robust optimisation is a recent approach for optimisation under uncertain data. In recent years the need to cope with uncertain data has become especially acute, particularly where real-world applications are concerned. In such circumstances, robust optimisation aims to recover an optimal solution whose feasibility must be guaranteed for any realisation of the uncertain data. Although many researchers avoid uncertainty due to the added complexity in constructing a robust optimisation model and to lack of knowledge as to the nature of these uncertainties, and especially their propagation, in this thesis robust convex optimisation, while estimating the uncertainties at every step is investigated for the motion estimation problem. First, a solution using convex optimisation coupled to the recursive least squares (RLS) algorithm and the robust H filter is developed for motion estimation. In another solution, uncertainties and their propagation are incorporated in a robust L convex optimisation framework for monocular visual motion estimation. In this solution, robust least squares is combined with a second order cone program (SOCP). A technique to improve the accuracy and the robustness of the fundamental matrix is also investigated in this thesis. This technique uses the covariance intersection approach to fuse feature location uncertainties, which leads to more consistent motion estimates. Loop-closure detection is crucial in improving the robustness of navigation algorithms. In practice, after long navigation in an unknown environment, detecting that a vehicle is in a location it has previously visited gives the opportunity to increase the accuracy and consistency of the estimate. In this context, we have developed an efficient appearance-based method for visual loop-closure detection based on the combination of a Gaussian mixture model with the KD-tree data structure. Deploying this technique for loop-closure detection, a robust L convex posegraph optimisation solution for unmanned aerial vehicle (UAVs) monocular motion estimation is introduced as well. In the literature, most proposed solutions formulate the pose-graph optimisation as a least-squares problem by minimising a cost function using iterative methods. In this work, robust convex optimisation under the L norm is adopted, which efficiently corrects the UAV’s pose after loop-closure detection. To round out the work in this thesis, a system for cooperative monocular visual motion estimation with multiple aerial vehicles is proposed. The cooperative motion estimation employs state-of-the-art approaches for optimisation, individual motion estimation and registration. Three-view geometry algorithms in a convex optimisation framework are deployed on board the monocular vision system for each vehicle. In addition, vehicle-to-vehicle relative pose estimation is performed with a novel robust registration solution in a global optimisation framework. In parallel, and as a complementary solution for the relative pose, a robust non-linear H solution is designed as well to fuse measurements from the UAVs’ on-board inertial sensors with the visual estimates. The suggested contributions have been exhaustively evaluated over a number of real-image data experiments in the laboratory using monocular vision systems and range imaging devices. In this thesis, we propose several solutions towards the goal of robust visual motion estimation using convex optimisation. We show that the convex optimisation framework may be extended to include uncertainty information, to achieve robust and optimal solutions. We observed that convex optimisation is a practical and very appealing alternative to linear techniques and iterative methods.

As the use of Unmanned Aerial Systems (UAS) becomes more common in both civilian/commercial and military applications, so too has the risk of injury to individuals and third parties on the ground. The purpose of this research is to further enhance methods currently in use for performing flight path risk assessment for UAS, as well as improve upon an existing software tool: Quantitative Approach and Departure Risk Assessment (QUADRA). The primary focus is upon the incorporation of building information to determine the protection offered to sheltered populations, reevaluate the probability of fatality models used in aircraft failures to more accurately determine the risk for smaller UAS systems, and to provide a metric for determining the number of individuals that are adversely affected by the noise of the autonomous system as it performs its mission. / Master of Science / Unmanned Aerial Vehicles are aircraft that are operated without a pilot onboard. More conventionally known as drones, these aircraft can be at increased risk to individuals on the ground as there is no pilot in the aircraft to course correct should the aircraft fail. Due to the potential for drones to fail and thus injure people on the ground, a method for determining the number of people injured or killed by an aircraft for its mission has been developed. These methods identify areas on the ground where the aircraft may land, as well as the potential number of fatalities for a given mission. To minimize the risk to people on the ground, the flight path of the drone is changed until a lower risk flight path is found. Similarly, the sound produced by these drones is used to determine the number of people who may hear the aircraft as it is flying overhead, as well as the number of people who may be annoyed or disturbed by this noise.

This thesis investigates new convex optimisation techniques for motion and pose estimation. Numerous computer vision problems can be formulated as optimisation problems. These optimisation problems are generally solved via linear techniques using the singular value decomposition or iterative methods under an L2 norm minimisation. Linear techniques have the advantage of offering a closed-form solution that is simple to implement. The quantity being minimised is, however, not geometrically or statistically meaningful. Conversely, L2 algorithms rely on iterative estimation, where a cost function is minimised using algorithms such as Levenberg-Marquardt, Gauss-Newton, gradient descent or conjugate gradient. The cost functions involved are geometrically interpretable and can statistically be optimal under an assumption of Gaussian noise. However, in addition to their sensitivity to initial conditions, these algorithms are often slow and bear a high probability of getting trapped in a local minimum or producing infeasible solutions, even for small noise levels. In light of the above, in this thesis we focus on developing new techniques for finding solutions via a convex optimisation framework that are globally optimal. Presently convex optimisation techniques in motion estimation have revealed enormous advantages. Indeed, convex optimisation ensures getting a global minimum, and the cost function is geometrically meaningful. Moreover, robust optimisation is a recent approach for optimisation under uncertain data. In recent years the need to cope with uncertain data has become especially acute, particularly where real-world applications are concerned. In such circumstances, robust optimisation aims to recover an optimal solution whose feasibility must be guaranteed for any realisation of the uncertain data. Although many researchers avoid uncertainty due to the added complexity in constructing a robust optimisation model and to lack of knowledge as to the nature of these uncertainties, and especially their propagation, in this thesis robust convex optimisation, while estimating the uncertainties at every step is investigated for the motion estimation problem. First, a solution using convex optimisation coupled to the recursive least squares (RLS) algorithm and the robust H filter is developed for motion estimation. In another solution, uncertainties and their propagation are incorporated in a robust L convex optimisation framework for monocular visual motion estimation. In this solution, robust least squares is combined with a second order cone program (SOCP). A technique to improve the accuracy and the robustness of the fundamental matrix is also investigated in this thesis. This technique uses the covariance intersection approach to fuse feature location uncertainties, which leads to more consistent motion estimates. Loop-closure detection is crucial in improving the robustness of navigation algorithms. In practice, after long navigation in an unknown environment, detecting that a vehicle is in a location it has previously visited gives the opportunity to increase the accuracy and consistency of the estimate. In this context, we have developed an efficient appearance-based method for visual loop-closure detection based on the combination of a Gaussian mixture model with the KD-tree data structure. Deploying this technique for loop-closure detection, a robust L convex posegraph optimisation solution for unmanned aerial vehicle (UAVs) monocular motion estimation is introduced as well. In the literature, most proposed solutions formulate the pose-graph optimisation as a least-squares problem by minimising a cost function using iterative methods. In this work, robust convex optimisation under the L norm is adopted, which efficiently corrects the UAV’s pose after loop-closure detection. To round out the work in this thesis, a system for cooperative monocular visual motion estimation with multiple aerial vehicles is proposed. The cooperative motion estimation employs state-of-the-art approaches for optimisation, individual motion estimation and registration. Three-view geometry algorithms in a convex optimisation framework are deployed on board the monocular vision system for each vehicle. In addition, vehicle-to-vehicle relative pose estimation is performed with a novel robust registration solution in a global optimisation framework. In parallel, and as a complementary solution for the relative pose, a robust non-linear H solution is designed as well to fuse measurements from the UAVs’ on-board inertial sensors with the visual estimates. The suggested contributions have been exhaustively evaluated over a number of real-image data experiments in the laboratory using monocular vision systems and range imaging devices. In this thesis, we propose several solutions towards the goal of robust visual motion estimation using convex optimisation. We show that the convex optimisation framework may be extended to include uncertainty information, to achieve robust and optimal solutions. We observed that convex optimisation is a practical and very appealing alternative to linear techniques and iterative methods.

No description available.

Unmanned Aerial Vehicles (UAVs) are being used more frequently in surveillance scenarios for both civilian and military applications. One such application addresses a UAV patrolling a perimeter, where certain stations can receive alerts at random intervals. Once the UAV arrives at an alert site it can take two actions: 1. Loiter and gain information about the site. 2. Move on around the perimeter. The information that is gained is transmitted to an operator to allow him to classify the alert. The information is a function of the amount of time the UAV is at the alert site, also called the dwell time, and the maximum delay. The goal of the optimization is to classify the alert so as to maximize the expected discounted information gained by the UAV's actions at a station about an alert. This optimization problem can be readily solved using Dynamic Programming. Even though this approach generates feasible solutions, there are reasons to experiment with different approaches. A complication for Dynamic Programming arises when the perimeter patrol problem is expanded. This is that the number of states increases rapidly when one adds additional stations, nodes, or UAVs to the perimeter. This in effect greatly increases the computation time making the determination of the solution intractable. The following attempts to alleviate this problem by implementing a Reinforcement Learning technique to obtain the optimal solution, more specifically Q-Learning. Reinforcement Learning is a simulation-based version of Dynamic Programming and requires lesser information to compute sub-optimal solutions. The effectiveness of the policies generated using Reinforcement Learning for the perimeter patrol problem have been corroborated numerically in this thesis.

<p> In this thesis, we address the problem of sensor management with particular application to using unmanned aerial vehicles (U AV s) for multi target tracking. Also, we present a decision based approach for controlling information flow in decentralized multi-target multi-sensor data fusion.</p> <p> Considering the problem of sensor management for multitarget tracking, we study the problem of decision based control of a group of UAVs carrying out surveillance over a region that includes a number of moving targets. The objective is to maximize the information obtained and to track as many targets as possible with the maximum possible accuracy. Uncertainty in the information obtained by each UAV regarding the location of the ground targets are addressed in the problem formulation. We propose an altered version of a classical Value Iteration algorithm, one of the most commonly used techniques to calculate the optimal policy for Markov Decision Processes (MDPs) based on Dynamic Element Matching (DEM) algorithms. DEM algorithms, widely used for reducing harmonic distortion in Digital-to-Analog converters, are used as a core element in the modified algorithm. We introduce and demonstrate a number of new performance metrics, to verify the effectiveness of an MDP policy, especially useful for quantifying the impact of the modified DEM-based Value Iteration algorithm on an MDP policy. Also, we introduce a multi-level hierarchy of MDPs controlling each of the UAV s. Each level in the hierarchy solves a problem at a different level of abstraction. Simulation results are presented on a representative multisensor-multitarget tracking problem showing a significant improvement in performance compared to the classical algorithm. The proposed method demonstrated robust performance while guaranteeing polynomial computational complexity.</p> <p> Decentralized multisensor-multitarget tracking has numerous advantages over singlesensor or single-platform tracking. In this thesis, we present a solution for one of the main problems in decentralized tracking, namely, distributed information transfer and fusion among the participating platforms. We present a decision mechanism for collaborative distributed data fusion that provides each platform with the required data for the fusion process while substantially reducing redundancy in the information flow in the overall system. We consider a distributed data fusion system consisting of platforms that are decentralized, heterogenous, and potentially unreliable. The proposed approach, which is based on Markov Decision Processes with introduced hierarchial structure will control the information exchange and data fusion process. The information based objective function is based on the Posterior Cramer-Rao lower bound and constitutes the basis of a reward structure for Markov decision processes which are used, together with decentralized lookup substrate, to control the data fusion process. We analyze three distributed data fusion algorithms - associated measurement fusion, tracklet fusion and track-to-track fusion. The thesis also provides a detailed analysis of communication and computational load in distributed tracking algorithms. Simulation examples demonstrate the operation and the performance results of the system.</p> <p> In this thesis, we also present the development of a multisensor-multitarget tracking testbed for simulating large-scale distributed scenarios, capable of handling multiple, heterogeneous sensors, targets and data fusion methods</p>. / Thesis / Doctor of Philosophy (PhD)

No description available.

Electric powered Unmanned Aerial Vehicles (UAVs) have emerged as a promi- nent aviation concept due to the advantageous such as stealth operation and zero emission. In addition, fuel cell powered electric UAVs are more attrac- tive as a result of the long endurance capability of the propulsion system. This dissertation investigates novel power management architecture for fuel cell and battery powered unmanned aerial vehicle propulsion application. The research work focused on the development of a power management system to control the hybrid electric propulsion system whilst optimizing the fuel cell air supplying system performances. The multiple power sources hybridization is a control challenge associated with the power management decisions and their implementation in the power electronic interface. In most applications, the propulsion power distribu- tion is controlled by using the regulated power converting devices such as unidirectional and bidirectional converters. The amount of power shared with the each power source is depended on the power and energy capacities of the device. In this research, a power management system is developed for polymer exchange membrane fuel cell and Lithium-Ion battery based hybrid electric propulsion system for an UAV propulsion application. Ini- tially, the UAV propulsion power requirements during the take-off, climb, endurance, cruising and maximum velocity are determined. A power man- agement algorithm is developed based on the UAV propulsion power re- quirement and the battery power capacity. Three power states are intro- duced in the power management system called Start-up power state, High power state and Charging power state. The each power state consists of the power management sequences to distribute the load power between the battery and the fuel cell system. A power electronic interface is developed Electric powered Unmanned Aerial Vehicles (UAVs) have emerged as a promi- nent aviation concept due to the advantageous such as stealth operation and zero emission. In addition, fuel cell powered electric UAVs are more attrac- tive as a result of the long endurance capability of the propulsion system. This dissertation investigates novel power management architecture for fuel cell and battery powered unmanned aerial vehicle propulsion application. The research work focused on the development of a power management system to control the hybrid electric propulsion system whilst optimizing the fuel cell air supplying system performances. The multiple power sources hybridization is a control challenge associated with the power management decisions and their implementation in the power electronic interface. In most applications, the propulsion power distribu- tion is controlled by using the regulated power converting devices such as unidirectional and bidirectional converters. The amount of power shared with the each power source is depended on the power and energy capacities of the device. In this research, a power management system is developed for polymer exchange membrane fuel cell and Lithium-Ion battery based hybrid electric propulsion system for an UAV propulsion application. Ini- tially, the UAV propulsion power requirements during the take-off, climb, endurance, cruising and maximum velocity are determined. A power man- agement algorithm is developed based on the UAV propulsion power re- quirement and the battery power capacity. Three power states are intro- duced in the power management system called Start-up power state, High power state and Charging power state. The each power state consists of the power management sequences to distribute the load power between the battery and the fuel cell system. A power electronic interface is developed with a unidirectional converter and a bidirectional converter to integrate the fuel cell system and the battery into the propulsion motor drive. The main objective of the power management system is to obtain the controlled fuel cell current profile as a performance variable. The relationship between the fuel cell current and the fuel cell air supplying system compressor power is investigated and a referenced model is developed to obtain the optimum compressor power as a function of the fuel cell current. An adaptive controller is introduced to optimize the fuel cell air supplying system performances based on the referenced model. The adaptive neuro-fuzzy inference system based controller dynamically adapts the actual compressor operating power into the optimum value defined in the reference model. The online learning and training capabilities of the adaptive controller identify the nonlinear variations of the fuel cell current and generate a control signal for the compressor motor voltage to optimize the fuel cell air supplying system performances. The hybrid electric power system and the power management system were developed in real time environment and practical tests were conducted to validate the simulation results.

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.

This work outlines a design for a small form-factor flight control system designed to fly in a wide variety of airframes. The system was designed with future expansion in mind while providing a complete, all-in-one solution to meet present needs. This system as presented meets most needs while remaining relatively low cost. It has a completely integrated IMU solution as well as on- board GPS. It is capable of basic waypoint navigation. This solution was testing using software and hardware-in-the-loop simulation which proved its functionality.