Saranga: milliWatt Ultrasound Navigation On Palm-Sized Aerial Robots for Visually Degraded Scenes
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 GPS-denied wild scenes. Common methods for obstacle avoidance use RGB cameras and LiDAR become ineffective in visually degraded conditions such as low visibility, dust, fog or complete darkness. Other sensors, such as 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 ideas to combat the low signal to noise ratio: Physical noise reduction and a deep learning based denoising method. In the first idea, we find an optimal and practical way to block propeller induced ultrasound noise on the weak echoes. In the second idea, we generate and train a denoising neural network to utilize long-horizon for finding signal patterns under extreme amounts of uncorrelated noise. We generalize to the real-world with no real data for training. For the first time ever, we enable a palm-sized aerial robot to navigate in visually degraded conditions of smoke, 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.
Advisor: Professor Nitin Sanket (WPI)
Committee: Professor Kevin Leahy (WPI) and Professor Guanrui Li (WPI)