Browsing by Author "White, Brian A."

Now showing 1 - 5 of 5
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    Airborne behaviour monitoring using Gaussian processes with map information
    (Institution of Engineering and Technology, 2013-07-31T00:00:00Z) Oh, Hyondong; Shin, Hyosang; Kim, Seungkeun; Tsourdos, Antonios; White, Brian A.
    This paper proposes an airborne behaviour monitoring methodology of ground vehicles based on a statistical learning approach with domain knowledge given by road map information. To monitor and track the moving ground target using UAVs aboard a moving target indicator, an interactive multiple model (IMM) filter is firstly applied. {\color{red}The IMM filter consists of an on-road moving mode using a road-constrained filter and an off-road moving mode using a conventional filter.} Mode probability is also calculated from the IMM filter, and it provides deviation of the vehicle from the road. Then, a novel hybrid algorithm for anomalous behaviour recognition is developed using a Gaussian process regression on velocity profile along the one-dimensionalised position of the vehicle, as well as the deviation of the vehicle. To verify the feasibility and benefits of the proposed approach, a numerical simulation is performed using realistic car trajectory data in a city traffic.
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    Airborne mapping of complex obstacles using 2D Splinegon
    (2008-06-04T00:00:00Z) Lazarus, Samuel B.; Shanmugavel, Madhavan; Tsourdos, Antonios; Zbikowski, Rafal; White, Brian A.
    This paper describes a recently proposed algorithm in mapping the unknown obstacle in a stationary environment where the obstacles are represented as curved in nature. The focus is to achieve a guaranteed performance of sensor based navigation and mapping. The guaranteed performance is quantified by explicit bounds of the position estimate of an autonomous aerial vehicle using an extended Kalman filter and to track the obstacle so as to extract the map of the obstacle. This Dubins path planning algorithm is used to provide a flyable and safe path to the vehicle to fly from one location to another. This description takes into account the fact that the vehicle is made to fly around the obstacle and hence will map the shape of the obstacle using the 2D-Splinegon technique. This splinegon technique, the most efficient and a robust way to estimate the boundary of a curved nature obstacles, can provide mathematically provable performance guarantees that are achievable in practice.
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    On-line evolutionary algorithm guidance for multiple missiles against multiple targets
    (Elsevier, 2004-06-18) Hughes, Evan J.; White, Brian A.
    This paper details the application of a Cooperative Coevolution On-Line Evolutionary Algorithm (CCOLEA) to the guidance of a swarm of multiple missiles, against multiple targets. The CCOLEA trades the spatial distribution of missiles at impact, against the cost of re-aiming the missiles' seekers onto their final targets. A parallel approach is used where each missile optimises its own performance, based on limited information from the other missiles. The decision making processis thus distributed between the missiles giving distributed coordination.
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    Path planning of multiple autonomous vehicles
    (Cranfield University, 2007-06-18T09:37:00Z) Shanmugavel, M.; Tsourdos, Antonios; White, Brian A.
    Safe and simultaneous arrival of constant speed, constant altitude UAVs on target is solved by design of paths of equal lengths. The starting point of the solution is the well-known Dubins path which is composed of circular arcs and line segments, thus requiring only one simple manoeuvre - constant rate turn. An explicit bound can be imposed on the rate during the design and the resulting paths are the minimum time solution of the problem. However, transition between arc and line segment entails discontinuous changes in lateral accelerations (latax), making this approach impractical for real fixed wing UAVs. Therefore, the Dubins solution is replaced with clothoid and also a novel one, based on quintic Pythagorean Hodograph (PH) curves, whose latax demand is continuous. The clothoid solution is direct as in the case of the Dubins path. The PH path is chosen for its rational functional form. The clothoid and the PH paths are designed to have lengths close to the lengths of the Dubins paths to stay close to the minimum time solution. To derive the clothoid and the PH paths that way, the Dubins solution is first interpreted in terms of Differential Geometry of curves using the path length and curvature as the key parameters. The curvature of a Dubins path is a piecewise constant and discontinuous function of its path length, which is a differential geometric expression of the discontinuous latax demand involved in transitions between the arc and the line segment. By contrast, the curvature of the PH path is a fifth order polynomial of its path length. This is not only continuous, also has enough design parameters (polynomial coefficients) to meet the latax (curvature) constraints (bounds) and to make the PH solution close to the minimum time one. The offset curves of the PH path are used to design a safety region along each path. The solution is simplified by dividing path planning into two phases. The first phase produces flyable paths while the second phase produces safe paths. Three types of paths are used: Dubins, clothoid and Pythagorean Hodograph (PH). The paths are produced both in 2D and 3D. In two dimensions, the Dubins path is generated using Euclidean and Differential geometric principles. It is shown that the principles of Differential geometry are convenient to generalize the path with the curvature. Due to the lack of curvature continuity of the Dubins path, paths with curvature continuity are considered. In this respect, initially the solution with the Dubins path is extended to produce clothoid path. Latter the PH path is produced using interpolation technique. Flyable paths in three dimensions are produced with the spatial Dubins and PH paths. In the second phase, the flyable paths are tuned for simultaneous arrival on target. The simultaneous arrival is achieved by producing the paths of equal lengths. Two safety conditions: (i) minimum separation distance and (ii) non-intersection of paths at equal distance are defined to maneuver in free space. In a cluttered space, an additional condition, threat detection and avoidance is defined to produce safe paths. The tuning is achieved by increasing the curvature of the paths and by creating an intermediate way-point. Instead of imposing safety constraints, the flyable paths are tested for meeting the constraints. The path is replanned either by creating a new way-point or by increasing the curvature between the way-points under consideration. The path lengths are made equal to that of a reference path.
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    Unmanned aerial vehicle route planning on a dynamically changing waypoint based map for exploration purposes
    (2009-12-17T00:00:00Z) Kladis, Georgios P.; Economou, John T.; Tsourdos, Antonios; White, Brian A.; Knowles, Kevin
    In the included work the Unmanned Aerial Vehicle (UAV) mission is represented by energy graphs motivated by the analysis in [1]. The problem of the shortest path routing is revisited when a dynamically changing environment is considered. It is assumed that information about the map is received while on flight due to events. In addition, UAVs are required, while on mission, to "scout" areas of interest which involves extracting as much intelligence as possible and traversing it in the most safe flyable means. Hence, the UAV should be capable of integrating knowledge from a variety of sources and re-plan its mission accordingly in order to fulfil objectives. Motivated by the previous, depending on the decision making process, the notion of a "temporary" optimum path can be of physical and functional sense. The problem is modeled as a multistage decision making process, where each stage is triggered by an event and is characterized by a current starting point, an area for reconnaissance purposes and a final destination. Hence, given the current availability between paths, the objective is to devise a policy that leads from an origin or current known location to a destination node while traversing the unknown region of interest with the minimal energy demand.