Saranga: MilliWatt Ultrasound for Navigation in Visually Degraded Environments on Palm-Sized Aerial Robots

TL;DR

Saranga introduces a low-power ultrasound perception system for palm-sized aerial robots, enabling obstacle detection and navigation in visually degraded environments where traditional sensors fail.

Contribution

The paper presents a novel ultrasound-based perception stack with noise reduction and deep learning denoising, tailored for tiny aerial robots in challenging conditions.

Findings

Successfully navigated in fog, darkness, and snow environments

Achieved obstacle detection with low ultrasound signal-to-noise ratio

Demonstrated real-world applicability with extensive experiments

Abstract

Tiny palm-sized aerial robots possess exceptional agility and cost-effectiveness in navigating confined and cluttered environments. However, their limited payload capacity directly constrains the sensing suite on-board the robot, thereby limiting critical navigational tasks in Global Positioning System (GPS)-denied wild scenes. Common methods for obstacle avoidance use cameras and LIght Detection And Ranging (LIDAR), which become ineffective in visually degraded conditions such as low visibility, dust, fog or darkness. Other sensors, such as RAdio Detection And Ranging (RADAR), have high power consumption, making them unsuitable for tiny aerial robots. Inspired by bats, we propose Saranga, a low-power ultrasound-based perception stack that localizes obstacles using a dual sonar array. We present two key solutions to combat the low Peak Signal-to-Noise Ratio of $-4.9$ decibels: physical noise reduction and a deep learning based denoising method. Firstly, we present a practical way to block propeller induced ultrasound noise on the weak echoes. The second solution is to train a neural network to utilize the \textcolor{black}{long horizon of ultrasound echoes} for finding signal patterns under high amounts of uncorrelated noise where classical methods were insufficient. We generalize to the real world by using a synthetic data generation pipeline and limited real noise data for training. We enable a palm-sized aerial robot to navigate in visually degraded conditions of dense fog, darkness, and snow in a cluttered environment with thin and transparent obstacles using only on-board sensing and computation. We provide extensive real world results to demonstrate the efficacy of our approach.