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arXiv · 2312.08991

A Sim-to-Real Deep Learning-based Framework for Autonomous Nano-drone Racing

Lorenzo Lamberti·Elia Cereda·Gabriele Abbate·Lorenzo Bellone·Victor Javier Kartsch Morinigo·Michał Barcis·Agata Barcis·Alessandro Giusti·Francesco Conti·Daniele Palossi
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Abstract

Autonomous drone racing competitions are a proxy to improve unmanned aerial vehicles' perception, planning, and control skills. The recent emergence of autonomous nano-sized drone racing imposes new challenges, as their ~10cm form factor heavily restricts the resources available onboard, including memory, computation, and sensors. This paper describes the methodology and technical implementation of the system winning the first autonomous nano-drone racing international competition: the IMAV 2022 Nanocopter AI Challenge. We developed a fully onboard deep learning approach for visual navigation trained only on simulation images to achieve this goal. Our approach includes a convolutional neural network for obstacle avoidance, a sim-to-real dataset collection procedure, and a navigation policy that we selected, characterized, and adapted through simulation and actual in-field experiments. Our system ranked 1st among seven competing teams at the competition. In our best attempt, we scored 115m of traveled distance in the allotted 5-minute flight, never crashing while dodging static and dynamic obstacles. Sharing our knowledge with the research community, we aim to provide a solid groundwork to foster future development in this field.

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Lorenzo Lamberti, Elia Cereda, Gabriele Abbate, Lorenzo Bellone, Victor Javier Kartsch Morinigo, Michał Barcis, Agata Barcis, Alessandro Giusti, Francesco Conti, Daniele Palossi. 2023-12-14. A Sim-to-Real Deep Learning-based Framework for Autonomous Nano-drone Racing. https://doi.org/10.1109/lra.2024.3349814

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