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In-Flight Route Re-planning for Endurance Reconnaissance Unmanned Aerial Vehicles

机译:耐航侦察无人机的飞行中路线重新规划

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This paper presents in-flight route replanning method for high altitude endurance (HAE) reconnaissance UAV when new targets are detected. Firstly mathematics model for replanning is built, which is a multi-objective optimization under multi-constraint. Secondly, after image quality is forecasted by general image quality equation, multi-objective evolutionary algorithm is adopted to find the Pareto non-dominant route set, aiming at maximizing image quality of each target and minimizing route length under constraints. At last, fuzzy selection is applied to select the optimal route from the Pareto non-dominant route set refer to weights of image quality, route length and risk given by expert system. The simulated result showed the rationality of the method.
机译:本文提出了一种在发现新目标时用于高空耐航(HAE)侦察无人机的飞行路线重新规划方法。首先建立了用于重新规划的数学模型,该模型是在多约束下的多目标优化。其次,在通过一般图像质量方程式对图像质量进行预测后,采用多目标进化算法找到帕累托非优势路径集,以最大化每个目标的图像质量,并在约束下最小化路径长度。最后,根据专家系统给出的图像质量,路径长度和风险权重,应用模糊选择从帕累托非主导路径集中选择最优路径。仿真结果表明了该方法的合理性。

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