Seeing with sound; surface detection and avoidance by sensing self-generated noise

Abstract

Here, we demonstrate obstacle and secondary drone avoidance capability by quadcopter drones that can perceive and react to modulation of their self-generated acoustic environment when in proximity to surfaces. A ground truth for the interpretation of self-noise was established by measuring the intrinsic, three-dimensional, acoustic signature of a drone in an anechoic chamber. This was used to design sensor arrangements and machine learning algorithms to estimate the position of external features, obstacles or another drone, within the environment. Our machine learning approach took short segments of recorded sound and their Fourier transforms, fed these into a convolutional neural network, and output the location of an obstacle or secondary drone in the environment. The convolutional layers were constructed with a suitable topology that matched the physical arrangement of the sensors. Our surface detection and avoidance algorithms were refined during tethered flight within an anechoic chamber, followed by an exercise in free flight without obstacle avoidance, and finally free flight obstacle detection and avoidance. Our acoustic sense-and-avoid capability extends to vertical and horizontal planar surfaces and tethered secondary drones.

Developing an expansion‐based obstacle detection using panoptic segmentation

“…| INTRODUCTIONObstacle detection is essential for self-driving Micro Aerial Vehicles (MAVs). Detection of obstacles can be categorized into image-based(Aharchi & Kbir, 2022;Shi et al, 2023), sensor-based(Goodin et al, 2021;Wilshin et al, 2023), and hybrid(Beul et al, 2017;Hu et al, 2020). Image-based approaches use image information, such as gray levels(Mashaly et al, 2016), points(Aguilar et al, 2017;Al-Kaff et al, 2017), edges(Mashaly et al, 2016), and regions(Badrloo et al, 2022b).…”