This paper presents an aggressive trajectory tracking method for a small lightweight nano-quadrotor using nonlinear model predictive control (NMPC) based on acados. Controlling a nano quadrotor for accurate trajectory tracking at high speed in dynamic environments is challenging due to complex aerodynamic forces that introduce significant disturbances and large positional tracking errors. These aerodynamic effects are difficult to be identified and require feedback control that compensates for them in real time. NMPC allows the nano-quadrotor to control its motion in real time based on onboard sensor measurements, making it well-suited for tasks such as aggressive maneuvers and navigation in complex and dynamic environments. The software package acados enables the implementation of the NMPC algorithm on embedded systems, which is particularly important for nano-quadrotor due to its limited computational resources. Our autonomous navigation system is developed based on an AI-deck that is a GAP8-based parallel ultra-low power computing platform with onboard sensors of a multi-ranger deck and a flow deck. The proposed method of NMPC-based trajectory tracking control is tested in simulation and the results demonstrate its effectiveness in trajectory tracking while considering the dynamic environments. It is also tested on a real nano quadrotor hardware, 27-g Crazyflie 2.1, with a customized MCU running embedded NMPC, in which accurate trajectory tracking results are achieved in dynamic real-world environments.

翻译：本文提出了一种基于acados的非线性模型预测控制（NMPC）方法，用于实现小型轻量级纳米四旋翼无人机的激进轨迹跟踪。在动态环境中，由于复杂空气动力效应会引入显著干扰和较大位置跟踪误差，控制纳米四旋翼无人机实现高速精确轨迹跟踪具有挑战性。这些空气动力学效应难以辨识，需要能够实时补偿的反馈控制。NMPC使纳米四旋翼无人机能够基于机载传感器测量实时控制其运动，非常适用于复杂动态环境中的激进机动和导航等任务。acados软件包支持在嵌入式系统上实现NMPC算法，这对于计算资源有限的纳米四旋翼无人机尤为重要。我们的自主导航系统基于AI-deck开发，该平台采用GAP8并行超低功耗计算架构，并配备多测距仪与光流传感器的机载模块。所提出的NMPC轨迹跟踪控制方法在仿真中进行了测试，结果证明了其在考虑动态环境时轨迹跟踪的有效性。该方法还在实际纳米四旋翼无人机硬件（27克Crazyflie 2.1，搭载运行嵌入式NMPC的定制微控制器）上进行了测试，在真实动态环境中实现了精确的轨迹跟踪结果。