Flow Shadowing: A Method to Detect Multiple Flow Headings using an Array of Densely Packed Whisker-inspired Sensors

TL;DR

The paper tackles the problem of separating simultaneous airflow sources affecting a drone, such as wind and drone-induced flow. It introduces flow shadowing via a densely packed whisker-inspired sensor array, and demonstrates that upstream flow occlusion reduces downstream sensor signals in a predictable, direction-dependent manner. Using a 2×2 sensor array, the authors show that occlusion-based asymmetry enables estimation of dual-flow headings with reasonable RMSE, validated through wind tunnel and box-fan experiments and supported by an empirical occlusion model. This approach offers a practical step toward nuanced flow understanding for robust drone flight control in gusty, multi-source environments, with clear avenues for denser arrays and velocity estimation enhancements.

Abstract

Understanding airflow around a drone is critical for performing advanced maneuvers while maintaining flight stability. Recent research has worked to understand this flow by employing 2D and 3D flow sensors to measure flow from a single source like wind or the drone's relative motion. Our current work advances flow detection by introducing a strategy to distinguish between two flow sources applied simultaneously from different directions. By densely packing an array of flow sensors (or whiskers), we alter the path of airflow as it moves through the array. We have named this technique ``flow shadowing'' because we take advantage of the fact that a downstream whisker shadowed (or occluded) by an upstream whisker receives less incident flow. We show that this relationship is predictable for two whiskers based on the percent of occlusion. We then show that a 2x2 spatial array of whiskers responds asymmetrically when multiple flow sources from different headings are applied to the array. This asymmetry is direction-dependent, allowing us to predict the headings of flow from two different sources, like wind and a drone's relative motion.

Figures (10)

• Figure 1: Top and side views of two whisker-inspired flow sensors demonstrating flow shadowing. The upstream whisker (green) rotates significantly in response to an incident flow. In contrast, the downstream whisker (purple) in the shadow of the upstream whisker oscillates around its original position.
• Figure 2: Diagram of nomenclature for two flow signals. a) Flow 1: A drone flying at velocity $v_{Drone}$ with a flow sensor onboard measures a flow signal equal to but opposite the drone's velocity ($v_1$, $\varphi_1$). b) Flow 2: The environment in which a drone flies has wind. An onboard flow sensor will respond to the wind flow ($v_2$, $\varphi_2$). c) A flow sensor that responds to both flow types equally will measure a combined signal. d) $\alpha$ is a measure of the difference between the headings of the two flow stimuli ($\varphi_1$ or $\varphi_2$) which are estimated from an array of flow sensors.
• Figure 3: a) 2x2 array design. b) Four whiskers are separated by $s =$ 35mm in both x and y axes in an array. c) The sensors are designed similar to MRLwhisker01. A Hall effect sensor measures the rotations of the whisker drag element suspended by a spring. (Supplementary Video).
• Figure 4: The whisker arrays were tested under two types of airflow. a) One or two fans supplied airflow to a whisker array mounted on an optical table. The optical table and a ThorLabs rotational stage aided repeatability as the headings of flow were varied around the array. b) A wind tunnel was used to apply airflow from a single direction to whisker arrays.
• Figure 5: Comparison of the true heading of flow ($\varphi$) versus the sensor predicted flow heading ($\theta$) for a single sensor with airflow provided by a fan. Each heading includes two trials.