November, Friday 15th at 10.30 am in room C005, Daniele Palossi from the ETH Zurich will give a talk for the in joint D3-D5 seminar. The general topic will be how to run a convolutionnal neural network on a tiny quadcopter (10cm / 27g) using a new kind of CPU architecture (PULP). The exact title of her talk is : PULP-DroNet: Open Source and Open Hardware Artificial Intelligence for Fully Autonomous Navigation on Nano-UAVs
After the seminar, Daniele will try to setup a demo in the “creativ’lab” arena (bat. C).
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 servers.
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.