标题： PX4嵌入式飞行控制器的神经网络模型 显示英文标题

标题： A Neural Network Mode for PX4 on Embedded Flight Controllers

摘要： 本文贡献了一个基于神经网络的控制器框架在PX4堆栈中的开源实现。 我们开发了一个自定义模块用于微控制器上的推理，同时保留了PX4自动驾驶仪的所有功能。 在Aerial Gym仿真器中训练的策略被转换为TensorFlow Lite格式，然后与PX4一起构建，并刷新到飞行控制器。 这些策略替代了PX4中的控制级联，提供了一个端到端的位置设定点跟踪控制器，直接提供归一化的电机RPM设定点。 模拟和现实世界中进行的实验显示了相似的跟踪性能。 因此，我们提供了一个飞行准备好的管道，用于在现实世界中测试神经控制策略。 该管道简化了神经网络在嵌入式飞行控制器硬件上的部署，从而加速了基于学习的控制研究。 Aerial Gym仿真器和PX4模块在<https://github.com/ntnu-arl/aerial\_gym\_simulator> 和 <https://github.com/SindreMHegre/PX4-Autopilot-public/tree/for\_paper> 开源。 视频：<https://youtu.be/lY1OKz\_UOqM?si=VtzL243BAY3lblTJ>. 显示英文摘要

摘要： This paper contributes an open-sourced implementation of a neural-network based controller framework within the PX4 stack. We develop a custom module for inference on the microcontroller while retaining all of the functionality of the PX4 autopilot. Policies trained in the Aerial Gym Simulator are converted to the TensorFlow Lite format and then built together with PX4 and flashed to the flight controller. The policies substitute the control-cascade within PX4 to offer an end-to-end position-setpoint tracking controller directly providing normalized motor RPM setpoints. Experiments conducted in simulation and the real-world show similar tracking performance. We thus provide a flight-ready pipeline for testing neural control policies in the real world. The pipeline simplifies the deployment of neural networks on embedded flight controller hardware thereby accelerating research on learning-based control. Both the Aerial Gym Simulator and the PX4 module are open-sourced at https://github.com/ntnu-arl/aerial_gym_simulator and https://github.com/SindreMHegre/PX4-Autopilot-public/tree/for_paper. Video: https://youtu.be/lY1OKz_UOqM?si=VtzL243BAY3lblTJ.