Fully end-to-end deep learning policies for vision-based autonomous racing on ultra-low-power nano-drones
Florentin-Cristian Udrea
Fully end-to-end deep learning policies for vision-based autonomous racing on ultra-low-power nano-drones.
Rel. Daniele Jahier Pagliari, Alessio Burrello. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2024
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Abstract
In recent years, drones have found applications in a variety of domains, such as aerial surveillance and rescue missions to precision agriculture and filmmaking. Advancements in drone miniaturization led to nano-drones: very compact drones with only 10 cm in diameter and a few tens of grams in weight, which have advantages, such as being highly maneuverable in confined areas and safe to operate around people, but also have limitations, such as battery lifetime lasting only a few minutes and a microcontroller unit (MCU) limited to under 100 mW of power, which restricts computational capacity. Over the past decade, autonomous drone racing (ADR) has become increasingly popular.
In ADR competitions drones must autonomously navigate through gates and avoid obstacles at high speeds, which needs reactive perception and precise control
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