551 research outputs found

    Immune-system inspired approach for decentralized multi-agent control

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    This paper contains the first steps towards the development of a fully decentralized system framework. The novel approach that has been taken is derived from the inherent properties of the immune system. An assessment of the proposed control architecture has been performed by comparison with a more typical approach under a search and suppress kind of mission for an unmanned fleet

    Fast non-monotone submodular maximisation subject to a matroid constraint

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    In this work we present the first practical . 1 e −ǫ . -approximation algorithm to maximise a general non-negative submodular function subject to a matroid constraint. Our algorithm is based on combining the decreasing-threshold procedure of Badanidiyuru and Vondrak (SODA 2014) with a smoother version of the measured continuous greedy algorithm of Feldman et al. (FOCS 2011). This enables us to obtain an algorithm that requires O( nr2 ǫ4 . ¯ d+ ¯ d ¯ d .2 log2 ( n ǫ )) value oracle calls, where n is the cardinality of the ground set, r is the matroid rank, and ¯ d, ¯ d ∈R+ are the absolute values of the minimum and maximum marginal values that the function f can take i.e.: − ¯ d ≤fS(i) ≤ ¯ d, for all i ∈E and S ⊆E, where E is the ground set. The additional value oracle calls with respect to the work of Badanidiyuru and Vondrak come from the greater spread in the sampling of the multilinear extension that the possibility of negative marginal values introduce

    Integrated reconfigurable control and guidance based on evaluation of degraded performance

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    The present paper is focused on analysing an integrated reconfigurable control and guidance approach for recovering a small fixed-wing UAV from different actuator faults, which cover locked in place (stuck) and loss of effectiveness. The model of the UAV Aerosonde is used to develop a reconfigurable control system based on the control allocation technique for a variety of faults, such as locked-in-place control surfaces. It is shown through simulation that the developed technique is successful to recover the aircraft from various faults but cannot guarantee success on the planned mission. For mission scenarios where performance degradation is such that the prescribed trajectory cannot be achieved, a reconfigurable guidance system is developed, which is capable of adapting parameters such as the minimum turning radius and the look-ahead distance for obstacle avoidance, to allow the vehicle to dynamically generate a path which guides the aircraft around the no-fly zones taking into account the post-fault reduced performance. Path following is performed by means of a non-linear lateral guidance law and a collision avoidance algorithm is implemented as well. Finally, the integration of control reconfiguration and guidance adaptation is carried out to maximise probabilities of post-failure success in the mission. A methodology is developed, using an error based control allocation parameter, as a measure of performance degradation, which links both reconfiguration and guidance systems. The developed method, although approximate, is proven to be an efficient way of allocating the required degree of reconfiguration in guidance commands when an accurate prediction of the actual performance is not available

    Trajectory optimization for target localisation with bearing-only measurement

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    This paper considers the problem of twodimensional (2D) constrained trajectory optimisation of a pointmass aerial robot for constant-manoeuvring target localisation using bearing-only measurement. A performance metric that can be utilised in trajectory optimisation to maximise target observability is proposed first based on geometric conditions. One-step optimal manoeuvre that maximises the observability criterion is then derived analytically for moving targets. The heading angle constraint is also incorporated in the proposed optimal manoeuvre derivation to support practical application. Numerical simulations with some comparisons are presented to validate the analytical findings

    Computational guidance using sparse Gauss-Hermite quadrature differential dynamic programming

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    This paper proposes a new computational guidance algorithm using differential dynamic programming and sparse Gauss-Hermite quadrature rule. By the application of sparse Gauss-Hermite quadrature rule, numerical differentiation in the calculation of Hessian matrices and gradients in differential dynamic programming is avoided. Based on the new differential dynamic programming approach developed, a three-dimensional computational algorithm is proposed to control the impact angle and impact time for an air-to-surface interceptor. Extensive numerical simulations are performed to show the effectiveness of the proposed approach

    Track-oriented multiple hypothesis tracking based on Tabu search and Gibbs sampling

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    In order to circumvent the curse of dimensionality in multiple hypothesis tracking data association, this paper proposes two efficient implementation algorithms using Tabu search and Gibbs sampling. As the first step, we formulate the problem of generating the best global hypothesis in multiple hypothesis tracking as the problem of finding a maximum weighted independent set of a weighted undirected graph. Then, the metaheuristic Tabu search with two basic movements is designed to find the global optimal solution of the problem formulated. To improve the computational efficiency, this paper also develops a sampling based algorithm based on Gibbs sampling. The problem formulated for the Tabu search-based algorithm is reformulated as a maximum product problem to enable the implementation of Gibbs sampling. The detailed algorithm is then designed and the convergence is also theoretically analyzed. The performance of the two algorithms proposed are verified through numerical simulations and compared with that of a mainstream multiple dimensional assignment implementation algorithm. The simulation results confirm that the proposed algorithms significantly improve the computational efficiency while maintaining or even enhancing the tracking performance

    Evolutionary game theory based multi-objective optimization for control allocation of over-actuated system

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    This research presents multi-objective optimization for control allocation problem based on the Evolutionary Game Theory to solve distribution of redundant control input on the over actuated system in real-time. Optimizing the conflicting objectives, an evolutionary game theory based approach with replicator dynamics is used to find the optimal weighting using the weighted sum method. The main idea of this method is that the best strategy or dominant solution can be selected as a solution that survives among other non-dominant solutions. The Evolutionary Game Theory considers strategies as a player and investigates how these strategies can survive using replicator dynamics with payoff matrix. The numerical simulation results show the optimal weightings selected by Evolutionary Game and how the payoff has been changed in replicator dynamics

    Constrained multiple model bayesian filtering for target tracking in cluttered environment

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    This paper proposes a composite Bayesian filtering approach for unmanned aerial vehicle trajectory estimation in cluttered environments. More specifically, a complete model for the measurement likelihood function of all measurements, including target-generated observation and false alarms, is derived based on the random finite set theory. To accommodate several different manoeuvre modes and system state constraints, a recursive multiple model Bayesian filtering algorithm and its corresponding Sequential Monte Carlo implementation are established. Compared with classical approaches, the proposed method addresses the problem of measurement uncertainty without any data associations. Numerical simulations for estimating an unmanned aerial vehicle trajectory generated by generalised proportional navigation guidance law clearly demonstrate the effectiveness of the proposed formulation

    Distributed joint probabilistic data association filter with hybrid fusion strategy

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    This paper investigates the problem of distributed multitarget tracking (MTT) over a large-scale sensor network, consisting of low-cost sensors. Each local sensor runs a joint probabilistic data association filter to obtain local estimates and communicates with its neighbors for information fusion. The conventional fusion strategies, i.e., consensus on measurement (CM) and consensus on information (CI), are extended to MTT scenarios. This means that data association uncertainty and sensor fusion problems are solved simultaneously. Motivated by the complementary characteristics of these two different fusion approaches, a novel distributed MTT algorithm using a hybrid fusion strategy, e.g., a mix of CM and CI, is proposed. A distributed counting algorithm is incorporated into the tracker to provide the knowledge of the total number of sensor nodes. The new algorithm developed shows advantages in preserving boundedness of local estimates, guaranteeing global convergence to the optimal centralized version and being implemented without requiring no global information, compared with other fusion approaches. Simulations clearly demonstrate the characteristics and tracking performance of the proposed algorithm
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