FPGA-Based Neural Thrust Controller for UAVs
This addresses the problem of computational constraints for UAVs, enabling more complex algorithms on-board, though it is incremental as it applies existing FPGA technology to a specific domain.
This work tackled the challenge of implementing deep neural networks on UAVs with limited computing resources by proposing an FPGA-based hardware board, and validated it with real-world experiments for an RL-based controller.
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.