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arXiv 2012.08230math.OC
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航迹预测不确定性下的鲁棒飞行器冲突消解

Robust aircraft conflict resolution under trajectory prediction uncertainty

Fernando H C Dias, David Rey

摘要:

研究了航迹预测不确定条件下的飞行器冲突消解问题。我们认为,飞机的速度矢量可能会受到扰动,由于天气的影响,如风,或测量误差。在空中交通管制中,飞行器轨迹预测对保证飞行安全起着关键作用。我们的目标是解决飞机的冲突解决问题,在存在这样的扰动,并保证飞机分离的任何实现的不确定数据。我们提出了一个不确定性模型,其中飞机速度表示为随机变量和不确定性集被假定为多面体。我们考虑一个强大的优化方法,并嵌入在国家的最先进的数学规划配方的飞机冲突解决建议的不确定性模型。然后,我们采用Bertsimas和Sim(2004)的方法来制定鲁棒的对应。我们使用Dias等人(2020 a)提出的复数重构和约束生成算法来解决由此产生的非凸最优化问题。我们的数值实验表明,扰动的顺序$\pm 5\%$飞机速度可以占没有显着影响的目标函数相比,确定性的情况下。我们的测试还表明,对于更大程度的不确定性,一些情况下不承认无冲突的解决方案,从而突出了飞机冲突解决方案中现有的风险因素。我们试图通过进一步分析优化前和优化后的飞机轨迹来解释这种行为。我们的研究结果表明,大多数不可行的情况下,有一个相对较低的总飞机成对的最小距离和大量的冲突。

英文摘要:

We address the aircraft conflict resolution problem under trajectory prediction uncertainty. We consider that aircraft velocity vectors may be perturbed due to weather effects, such as wind, or measurement errors. Such perturbations may affect aircraft trajectory prediction which plays a key role in ensuring flight safety in air traffic control. Our goal is to solve the aircraft conflict resolution problem in the presence of such perturbations and guarantee that aircraft are separated for any realization of the uncertain data. We propose an uncertainty model wherein aircraft velocities are represented as random variables and the uncertainty set is assumed to be polyhedral. We consider a robust optimization approach and embed the proposed uncertainty model within state-of-the-art mathematical programming formulations for aircraft conflict resolution. We then adopt the approach of Bertsimas and Sim (2004) to formulate the robust counterpart. We use the complex number reformulation and the constraint generation algorithm proposed by Dias et al. (2020a) to solve the resulting nonconvex optimization problem on benchmarking instances of the literature. Our numerical experiments reveal that perturbations of the order of $\pm 5\%$ on aircraft velocities can be accounted for without significantly impacting the objective function compared to the deterministic case. Our tests also show that for greater levels of uncertainty, several instances fail to admit conflict-free solutions, thus highlighting existing risk factors in aircraft conflict resolution. We attempt to explain this behavior by further analyzing pre- and post-optimization aircraft trajectories. Our findings show that most infeasible instances have both a relatively low total aircraft pairwise minimal distance and a high number of conflicts.

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