Model-based path prediction for fixed-wing unmanned aircraft using pose estimates
Model-based path prediction for fixed-wing unmanned aircraft using pose estimates
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DOI：
10.1016/j.ast.2020.106030
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发表时间：
2020-10-01
影响因子：
5.6
通讯作者：
Woolsey, Craig A.
中科院分区：
文献类型：
--
作者：
Kang, Changkoo;Woolsey, Craig A.
随着小型无人机系统(SUA)的迅速普及，为了避免碰撞，这些飞机越来越需要检测和预测彼此的运动。除了探测和避免有人驾驶飞机的既定需要之外，这一关切也出现了。这两个威胁构成了截然不同的挑战。例如，虽然有人驾驶的飞机通常比SUAS飞行得更快，但只需使用位置测量和运动学粒子模型，就可以在中等时间间隔内准确预测其路径。由于SUA的可操作性更强，探测范围可能更短，因此需要更复杂的预测方法。提高精度的一种方法是基于完整的姿态(位置和姿态)和更高保真度的威胁飞机动力学模型进行预测。作为初步演示，我们提出了一种算法来预测小型固定翼无人驾驶飞机的路径，该算法使用对这种威胁飞机的姿态的估计，就像使用视觉传感器可能获得的那样。为了评估算法的性能，在一个大的实验数据集上，将使用该算法的预测与仅基于位置数据的预测进行了比较。实验结果表明，该算法的预测性能优于纯位置预测方法。(C)2020年爱思唯尔·马森公司。版权所有。
With the rapid proliferation of small unmanned aircraft systems (sUAS), there is an increasing need for these aircraft to detect and predict each other's motion in order to avoid collisions. This concern arises in addition to the well-established need to detect and avoid manned aircraft. The two threats pose distinct challenges. For example, while a manned aircraft typically travels quite fast compared with a sUAS, its path can be accurately predicted over moderate time intervals using only position measurements and a kinematic particle model. Because sUAS are more maneuverable, and detection horizons can be much shorter, there is a need for more sophisticated prediction methods. One way to improve accuracy is to base predictions on the complete pose (position and attitude) and a higher fidelity model of the threat aircraft's dynamics. As an initial demonstration, we propose an algorithm to predict the path of a small, fixed-wing unmanned aircraft using estimates of this threat aircraft's pose, as might be obtained using visual sensors. To assess the algorithm's performance, predictions using the proposed algorithm are compared with predictions based solely on position data for a large experimental data set. The results indicate that the proposed algorithm outperforms the position-only prediction method. (C) 2020 Elsevier Masson SAS. All rights reserved.