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Characterization of Flow Field Divergence for MAVs Vertical Control Landing

机译：MAVs垂直控制着陆的流场散布特征。

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摘要

Application of vision sensors to Micro Air Vehicles (MAVs) systems has gained a lot of attention in recent years. This sensor is widely used for interacting with the objects in the environment to perform tasks such as obstacle avoidance, 3D reconstruction, autonomous landing, aerial docking, etc. Many recent studies focus on bio-inspired methods using time-to-contact and flow divergence to visually guide and control the MAVs. However, this visual information measured from the vision system in the MAVs is not well understood. In this paper, we characterize flow divergence or visual looming estimated from a single camera mounted on a MAV during a vertically controlled maneuver. This is done by first estimating the delay of measurement and then using the delay-corrected data to find the noise model. We demonstrate that the output from the noise model is close to the actual measurement. Additionally, we investigate the feasibility of using divergence based control in the presence of the estimated delay and noise in simulation. Results from the closed-loop control analysis show that with either delay or noise alone the non-linear controller is able to perform gap closure task with some practical solutions at the end of maneuver. However, in the presence of both delay and noise, the maneuver is unstable when close to the ground.
机译：近年来，视觉传感器在微型飞行器（MAV）系统上的应用引起了很多关注。该传感器被广泛用于与环境中的物体进行交互，以执行诸如避障，3D重建，自主着陆，空中对接等任务。许多近期的研究集中于利用受时间接触和流量发散的生物启发方法在视觉上指导和控制MAV。但是，从MAV的视觉系统测得的视觉信息尚不十分清楚。在本文中，我们描述了在垂直控制操纵过程中从安装在MAV上的单个摄像机估计的流量发散或视觉隐约性。通过首先估计测量延迟，然后使用经过延迟校正的数据来找到噪声模型，可以完成此操作。我们证明了噪声模型的输出接近于实际测量。此外，我们研究了在估计的延迟和噪声存在的情况下使用基于发散控制的可行性。闭环控制分析的结果表明，仅在延迟或噪声的情况下，非线性控制器就可以在操纵结束时通过一些实用的解决方案来执行间隙闭合任务。但是，在同时存在延迟和噪音的情况下，靠近地面时操纵不稳定。

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