The advent of unmanned aerial vehicles (UAVs) has improved a variety of
fields by providing a versatile, cost-effective and accessible platform for
implementing state-of-the-art algorithms. To accomplish a broader range of
tasks, there is a growing need for enhanced on-board computing to cope with
increasing complexity and dynamic environmental conditions. Recent advances
have seen the application of Deep Neural Networks (DNNs), particularly in
combination with Reinforcement Learning (RL), to improve the adaptability and
performance of UAVs, especially in unknown environments. However, the
computational requirements of DNNs pose a challenge to the limited computing
resources available on many UAVs. This work explores the use of Field
Programmable Gate Arrays (FPGAs) as a viable solution to this challenge,
offering flexibility, high performance, energy and time efficiency. We propose
a novel hardware board equipped with an Artix-7 FPGA for a popular open-source
micro-UAV platform. We successfully validate its functionality by implementing
an RL-based low-level controller using real-world experiments.

使用 FPGA 硬件板验证了应用于开源微型无人机平台的 RL 低层控制器的功能。