Paper Info
Title
Ultra Low Power Deep-Learning-powered Autonomous Nano Drones.
Abstract
Flying in dynamic, urban, highly-populated environments represents an open problem in robotics. State-of-the-art (SoA) autonomous Unmanned Aerial Vehicles (UAVs) employ advanced computer vision techniques based on computationally expensive algorithms, such as Simultaneous Localization and Mapping (SLAM) or Convolutional Neural Networks (CNNs) to navigate in such environments. In the Internet-of-Things (IoT) era, nano-size UAVs capable of autonomous navigation would be extremely desirable as self-aware mobile IoT nodes. However, autonomous flight is considered unaffordable in the context of nano-scale UAVs, where the ultra-constrained power envelopes of tiny rotor-crafts limit the on-board computational capabilities to low-power microcontrollers. In this work, we present the first vertically integrated system for fully autonomous deep neural network-based navigation on nano-size UAVs. Our system is based on GAP8, a novel parallel ultra-low-power computing platform, and deployed on a 27 g commercial, open-source CrazyFlie 2.0 nano-quadrotor. We discuss a methodology and software mapping tools that enable the SoA CNN presented in [1] to be fully executed on-board within a strict 12 fps real-time constraint with no compromise in terms of flight results, while all processing is done with only 94 mW on average - 1% of the power envelope of the deployed nano-aircraft.
Year
Venue
Field
2018
arXiv: Robotics
Convolutional neural network,Control engineering,Real-time computing,Software,Artificial intelligence,Microcontroller,Drone,Engineering,Deep learning,Artificial neural network,Simultaneous localization and mapping,Robotics
DocType
Volume
Citations 
Journal
abs/1805.01831
3
PageRank 
References 
Authors
0.37
25
6
Name
Order
Citations
PageRank
Daniele Palossi1416.12
Antonio Loquercio251.09
Francesco Conti 0001312518.24
Eric Flamand428714.07
Davide Scaramuzza52704154.51
Luca Benini6131161188.49