Fpga-Based Neural Thrust Controller for UAVs

arXiv:2403.18703v1 Announce Type: cross
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 …

abstract aerial algorithms art arxiv board complexity computing cost cs.lg cs.sy dynamic eess.sy environmental fields fpga platform state tasks type unmanned aerial vehicles vehicles