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首页> 外文期刊>Aerospace science and technology >Grey wolf optimization based sense and avoid algorithm in a Bayesian framework for multiple UAV path planning in an uncertain environment
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Grey wolf optimization based sense and avoid algorithm in a Bayesian framework for multiple UAV path planning in an uncertain environment

机译：贝叶斯框架中基于灰狼优化的感知和避免算法，用于不确定环境中的多种无人机路径规划

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Unmanned Air Vehicles (UAVs), which have been popular in the military context, have recently attracted attention of many researchers because of their potential civilian applications. However, before UAVs can fly in civilian airspace, they need to be able to navigate safely to their goal while maintaining separation with other manned and unmanned aircraft during the transit. Algorithms for autonomous navigation of UAVs require access to accurate information about the state of the environment in order to perform well. However, this information is often uncertain and dynamically changing. In this paper, a Grey Wolf Optimization (GWO) based algorithm is proposed to find the optimal UAV trajectory in presence of moving obstacles, referred to as Intruder Aircraft (lAs), with unknown trajectories. The solution uses an efficient Bayesian formalism with a notion of cell weighting based on Distance Based Value Function (DBVF). The assumption is that the UAV is equipped with the Automatic Dependent Surveillance-Broadcast (ADS-B) and is provided with the position of lAs either via the ADS-B or ground based radar. However, future trajectories of the lAs are unknown to the UAV. The proposed method is verified using simulations performed on multiple scenarios. The results demonstrate the effectiveness of the proposed method in solving the trajectory planning problem of the UAVs. (C) 2018 Elsevier Masson SAS. All rights reserved.
机译：无人驾驶飞行器（UAV）在军事领域非常流行，由于其潜在的民用应用，最近引起了许多研究人员的关注。但是，在无人机能够在民用领空飞行之前，它们需要能够安全导航至目标，同时在运输过程中保持与其他有人和无人飞机的分离。无人机自主导航算法要求访问有关环境状态的准确信息，以使其性能良好。但是，此信息通常是不确定的并且会动态变化。在本文中，提出了一种基于灰狼优化（GWO）的算法，用于在存在未知轨迹的移动障碍物（称为“入侵者飞机”（IAS））存在的情况下找到最佳的无人机轨迹。该解决方案使用有效的贝叶斯形式主义，并基于基于距离的值函数（DBVF）进行单元加权。假定无人机配备了自动相关监视广播（ADS-B），并通过ADS-B或基于地面的雷达提供了lAs的位置。但是，无人机的未来轨迹是未知的。使用在多种情况下执行的仿真验证了所提出的方法。结果证明了该方法在解决无人机航迹规划问题上的有效性。 （C）2018 Elsevier Masson SAS。版权所有。

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