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Electrical Engineering and Systems Science > Systems and Control
Title: Fpga-Based Neural Thrust Controller for UAVs
(Submitted on 27 Mar 2024 (this version), latest version 28 Mar 2024 (v2))
Abstract: 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.
Submission history
From: Sharif Azem [view email][v1] Wed, 27 Mar 2024 15:52:54 GMT (1774kb,D)
[v2] Thu, 28 Mar 2024 09:44:06 GMT (1774kb,D)
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