在嵌入式飞控上实现端到端神经网络控制，直接输出电机转速指令。
A Neural Network Mode for PX4 on Embedded Flight Controllers
- 用TensorFlow Lite将仿真训练的策略部署到飞控微控制器
- 真实飞行与仿真中位置追踪性能相当
- 开源全流程工具链，加速学习型控制研究
本文在PX4自动驾驶系统中开源实现了基于神经网络的控制器框架。我们在微控制器上开发了专用推理模块，同时保持PX4自动驾驶仪全部功能。在Aerial Gym仿真器中训练的控制策略被转换为TensorFlow Lite格式，与PX4一同编译并烧录至飞行控制器。这些策略替代了PX4中的控制级联，直接输出归一化的电机转速设定值，构成端到端的位置设定点跟踪控制器。仿真与真实飞行实验均表明性能相近。因此，我们提供了一条可直接用于真实世界测试神经控制策略的完整流程。该流程简化了神经网络在嵌入式飞控硬件上的部署，从而加速基于学习的控制研究。Aerial Gym仿真器与PX4模块均已开源，地址分别为https://github.com/ntnu-arl/aerial_gym_simulator 和 https://github.com/SindreMHegre/PX4-Autopilot-public/tree/for_paper。视频演示：https://youtu.be/lY1OKz_UOqM?si=VtzL243BAY3lblTJ。
原文摘要 · Abstract (English)
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.
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