Accurate position control of a flapping-wing robot enabling free-flight flow visualisation in a wind tunnel

Abstract

Flow visualisations are essential to better understand the unsteady aerodynamics of flapping wing flight. The issues inherent to animal experiments, such as poor controllability and unnatural flapping when tethered, can be avoided by using robotic flyers that promise for a more systematic and repeatable methodology. Here, we present a new flapping-wing micro air vehicle (FWMAV)-specific control approach that, by employing an external motion tracking system, achieved autonomous wind tunnel flight with a maximum root-mean-square position error of 28 mm at low speeds (0.8-1.2 m/s) and 75 mm at high speeds (2-2.4 m/s). This allowed the first free-flight flow visualisation experiments to be conducted with an FWMAV. Time-resolved stereoscopic particle image velocimetry was used to reconstruct the three-dimensional flow patterns of the FWMAV wake. A good qualitative match was found in comparison to a tethered configuration at similar conditions, suggesting that the obtained free-flight measurements are reliable and meaningful.

Large-scale volumetric flow visualization of the unsteady wake of a flapping-wing micro air vehicle

“…However, the use of Taylor's hypothesis fails to represent the wake at more realistic flight conditions, where the angle of attack is quite high and the interaction between the velocities induced by the wings and the free stream is too complex to be accounted for by a simple uniform convection model. Free-flight visualizations of the DelFly (del Estal Herrero et al 2018;Karasek et al 2019) give, furthermore, clear evidence that in slow forward flight, the wake is predominantly aligned along the body axis direction, making the assumption of convection in the free stream direction unrealistic.…”

“…However, larger differences were found in the wing stroke direction, which were attributed to structural vibrations of the MAV body, which affect the force balance measurements depending on the clamping position. For the free-flight measurements in the current study, the same procedure is followed as for the preliminary free-flight visualization experiments described in (Karasek et al 2019), by combining the PIV visualization with a position-control system for autonomous flight in the wind tunnel environment.…”

“…4). The DelFly position is controlled by an on-board autopilot, which steers the vehicle based on feedback from the on-board IMU (used for attitude estimation) and from an external motion tracking system that provides position and heading information with respect to ground (Karasek et al 2019). The external motion tracking system employs 12 OptiTrack Flex 13 cameras (Natu-ralPoint, Inc., resolution 1280 px × 1024 px, 120 fps).…”

“…This provides the information on the flapping phase of the wings required in the phaseaveraging procedure while also allowing to apply corrections on the measured data to account for the dynamic motion of the DelFly. More detailed information on the control system and the data synchronization with the PIV data set can be found in (Karasek et al 2019).…”

“…This allows valuable information to be obtained on the relation between flow behaviour and resulting force generation. When possible, one may alternatively approach the natural free-flight condition more closely by training/controlling the animal/ MAV to remain suspended without physical restraint in the test section (Spedding et al 2003;Hedenström et al 2006Hedenström et al , 2009Johansson and Hedenström 2009;Muijres, et al 2011;Karasek et al 2019).…”