tinyML EMEA 2022 – Daniele Palossi: Autonomous Nano-UAVs: An Extreme Edge Computing Case

tinyML EMEA 2022
Full Stack Solutions session
Autonomous Nano-UAVs: An Extreme Edge Computing Case
Daniele PALOSSI, Postdoctoral Researcher, IDSIA & ETH Zurich

Nano-sized autonomous unmanned aerial vehicles (UAVs), i.e., nano-UAVs, are compelling flying robots characterized by a stringent form factor and payload, such as 10 cm diameter and a few tens of grams in weight. These fundamental traits challenge the onboard sensorial and computational capabilities, allowing only for limited microcontroller-class computational units (MCUs) mapped to a sub-100 mW power envelope. At the same time, making a UAV fully autonomous means to fulfill its mission with the only resources available onboard, i.e., avoiding any external infrastructure or off-board computation.
To some extent, achieving such an ambitious goal of a fully autonomous nano-UAV can be seen as the embodiment of the extreme edge computing paradigm. The computational/memory limitations are exacerbated by the paramount need for real-time mission-critical execution of complex algorithms on a flying cyber-physical system (CPS).
Enabling timely computation on a nano-UAV ultimately would lead to i. new application scenarios, otherwise prevented for bigger UAVs, ii. increased safety in the human-robot interaction, and iii. reduced cost of versatile robotic platforms.
In recent years, many researchers have pushed the state-of-the-art in the onboard intelligence of nano-UAVs by leveraging machine learning and deep learning techniques as an alternative approach to the traditional and computationally expensive, geometrical, and computer vision-based methods. This talk presents our latest research effort and
achievements by delivering holistic and vertical-integrated solutions, which frame energy-efficient ultra-low power MCUs with deep neural network vision-based algorithms, quantization techniques, and data augmentation pipelines. In this presentation, we will follow our two keystone works in the nanorobotics area, i.e., the seminal PULP-Dronet [1,2] and the recent PULP-Frontnet [3] project, as concrete examples to introduce our latest scientific contributions. In detail, we will address several fundamental research questions, such as “how to shrink the number of operations and memory footprint of convolutional neural networks (CNNs) for autonomous navigation” [4], “how to improve the generalization capabilities of tiny CNNs for human-robot interaction” [5] and “how to combine the CPS’ state with vision-based CNNs for enhancing the performance of an autonomous nano-UAV” [6]. Finally, we will support our key findings with thorough in-field evaluations of our methodologies and resulting closed-loop end-to-end robotic demonstrators.

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