Improving Autonomous Nano-drones Performance via Automated End-to-End Optimization and Deployment of DNNs

Abstract

The evolution of energy-efficient ultra-low-power (ULP) parallel processors and the diffusion of convolutional neural networks (CNNs) are fueling the advent of autonomous driving nano-sized unmanned aerial vehicles (UAVs). These sub-10 cm robotic platforms are envisioned as next-generation ubiquitous smart-sensors and unobtrusive robotic-helpers. However, the limited computational/memory resources available aboard nano-UAVs introduce the challenge of minimizing and optimizing vision-based CNNs -- which to date require error-prone, labor-intensive iterative development flows. This work explores methodologies and software tools to streamline and automate all the deployment of vision-based CNN navigation on a ULP multicore system-on-chip acting as a mission computer on a Crazyflie 2.1 nano-UAV. We focus on the deployment of PULP-Dronet, a state-of-the-art CNN for autonomous navigation of nano-UAVs, from the initial training to the final closed-loop evaluation. Compared to the original hand-crafted CNN, our results show a 2x reduction of memory footprint and a speedup of 1.6x in inference time while guaranteeing the same prediction accuracy and significantly improving the behavior in the field, achieving: i) obstacle avoidance with a peak braking-speed of 1.65 m/s and improving the speed/braking-space ratio of the baseline, ii) free flight in a familiar environment up to 1.96 m/s (0.5 m/s for the baseline), and iii) lane following on a path featuring a 90 deg turn -- all while using for computation less than 1.6% of the drone's power budget. To foster new applications and future research, we open-source all the software design in a ready-to-run project compatible with the Crazyflie 2.1

16 pages, 8 figures, 5 tables. This paper has been accepted for publication in the IEEE Journal on Emerging and Selected Topics in Circuits and Systems (JETCAS) copyright 2021 IEEE

References in corpus (5)

• A 64mW DNN-based Visual Navigation Engine for Autonomous Nano-Drones
• PULP-NN: Accelerating Quantized Neural Networks on Parallel Ultra-Low-Power RISC-V Processors
• DORY: Automatic End-to-End Deployment of Real-World DNNs on Low-Cost IoT MCUs
• An Open Source and Open Hardware Deep Learning-powered Visual Navigation Engine for Autonomous Nano-UAVs
• Learning-based Bias Correction for Ultra-wideband Localization of Resource-constrained Mobile Robots

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• Fully Onboard SLAM for Distributed Mapping with a Swarm of Nano-Drones
• A Sim-to-Real Deep Learning-based Framework for Autonomous Nano-drone Racing
• Ultra-low Power Deep Learning-based Monocular Relative Localization Onboard Nano-quadrotors