Adaptive Gain Control Strategy for Constant Optical Flow Divergence Landing

Abstract

Bio-inspired methods can provide efficient solutions to perform autonomous landing for Micro Air Vehicles (MAVs). Flying insects such as honeybees perform vertical landings by keeping flow divergence constant. This leads to an exponential decay of both height and vertical velocity, and allows for smooth and safe landings. However, the presence of noise and delay in obtaining flow divergence estimates will cause instability of the landing when the control gains are not adapted to the height. In this paper, we propose a strategy that deals with this fundamental problem of optical flow control. The key to the strategy lies in the use of a recent theory that allows the MAV to see distance by means of its control instability. At the start of a landing, the MAV detects the height by means of an oscillating movement and sets the control gains accordingly. Then, during descent, the gains are reduced exponentially, with mechanisms in place to reduce or increase the gains if the actual trajectory deviates too much from an ideal constant divergence landing. Real-world experiments demonstrate stable landings of the MAV in both indoor and windy outdoor environments. Index Terms-Biologically-inspired robots, aerial robotics, visual servoing, optical flow, autonomous landing.H. W. Ho is with the Micro Air Vehicle laboratory

Vertical landing for micro air vehicles using event‐based optical flow

“…Additionally, unlike Ref. , the error seen with our estimator is not Gaussian but is fit better by a log‐normal error distribution as shown in Figure (c) with .…”

“…The dashed black line shows the 25%, 50%, and 75% percentiles of the data. For comparison, the model obtained for the frame‐based size divergence estimator is shown as well. (b) Normalized error distribution of .…”

“…In Ref. , an extensive characterization of two frame‐based visual estimators for is performed, which includes an assessment of their absolute error distribution up to (Fig. in that paper).…”

“…This was used in Ref. to develop an adaptive gain controller, which detects the height at the start of a landing maneuver, sets initial controller gains based on the height, and lands while exponentially reducing the gains. Although the landings performed were quite fast compared to landings in the literature [ compared to a typical (Ref.…”

“…)], the speed of the landings in Ref. is still quite limited by the standard cameras available on the used AR drone 2.0 quadrotor MAV.…”