de Croon Guido C H E
Micro Air Vehicle Laboratory, Control and Simulation, Faculty of Aerospace Engineering, TU Delft, the Netherlands.
Bioinspir Biomim. 2016 Jan 7;11(1):016004. doi: 10.1088/1748-3190/11/1/016004.
The visual cue of optical flow plays an important role in the navigation of flying insects, and is increasingly studied for use by small flying robots as well. A major problem is that successful optical flow control seems to require distance estimates, while optical flow is known to provide only the ratio of velocity to distance. In this article, a novel, stability-based strategy is proposed for monocular distance estimation, relying on optical flow maneuvers and knowledge of the control inputs (efference copies). It is shown analytically that given a fixed control gain, the stability of a constant divergence control loop only depends on the distance to the approached surface. At close distances, the control loop starts to exhibit self-induced oscillations. The robot can detect these oscillations and hence be aware of the distance to the surface. The proposed stability-based strategy for estimating distances has two main attractive characteristics. First, self-induced oscillations can be detected robustly by the robot and are hardly influenced by wind. Second, the distance can be estimated during a zero divergence maneuver, i.e., around hover. The stability-based strategy is implemented and tested both in simulation and on board a Parrot AR drone 2.0. It is shown that the strategy can be used to: (1) trigger a final approach response during a constant divergence landing with fixed gain, (2) estimate the distance in hover, and (3) estimate distances during an entire landing if the robot uses adaptive gain control to continuously stay on the 'edge of oscillation.'
光流的视觉线索在飞虫导航中起着重要作用，目前也越来越多地被用于小型飞行机器人的研究。一个主要问题是，成功的光流控制似乎需要距离估计，而众所周知，光流仅能提供速度与距离的比值。在本文中，我们提出了一种基于稳定性的单目距离估计新策略，该策略依赖于光流操纵和控制输入（传出副本）的知识。通过分析表明，在给定固定控制增益的情况下，恒定散度控制回路的稳定性仅取决于到接近表面的距离。在近距离时，控制回路开始出现自激振荡。机器人可以检测到这些振荡，从而感知到到表面的距离。所提出的基于稳定性的距离估计策略具有两个主要吸引人的特点。首先，机器人可以可靠地检测到自激振荡，并且几乎不受风的影响。其次，可以在零散度操纵期间，即在悬停附近估计距离。基于稳定性的策略在模拟和Parrot AR无人机2.0上都进行了实现和测试。结果表明，该策略可用于：（1）在固定增益的恒定散度着陆期间触发最终接近响应，（2）在悬停时估计距离，以及（3）如果机器人使用自适应增益控制以持续保持在“振荡边缘”，则在整个着陆过程中估计距离。
