Daniele Palossi: PULP-DroNet: Open Source and Open Hardware Artificial Intelligence for Fully Autonomous Navigation on Nano-UAVs
07 June 2019
Manno, Galleria 1, 2nd floor @ 12:00
Nano-size unmanned aerial vehicles (UAVs), with few centimeters of
diameter and sub-10 Watts of total power budget, have so far been
considered incapable of running sophisticated visual-based autonomous
navigation software without external aid from base-stations, ad-hoc
local positioning infrastructure, and powerful external computation
In this talk, we present what is, to the best of our knowledge, the
first 27g nano-UAV system able to run aboard an end-to-end,
closed-loop visual pipeline for autonomous navigation based on a
state-of-the-art deep-learning algorithm, built upon the open-source
Crazyflie 2.0 nano-quadrotor. Our visual navigation engine is enabled
by the combination of an ultra-low power computing device (the GAP8
system-on-chip) with a novel methodology for the deployment of deep
convolutional neural networks (CNNs). We enable onboard real-time
execution of the DroNet state-of-the-art deep CNN at 6
frame-per-second within 64mW and up to 18fps while still consuming on
average just 3.5% of the power envelope of the deployed nano-aircraft.
Field experiments demonstrate that the system's high responsiveness
prevents collisions with unexpected dynamic obstacles up to a flight
speed of 1.5m/s. In addition, we also demonstrate the capability of
our visual navigation engine of fully autonomous indoor navigation on
a 113m previously unseen path.