用视觉伺服与非线性模型预测控制，让无人机自动追踪动态目标。
Multirotor Nonlinear Model Predictive Control based on Visual Servoing of Evolving Features
- 基于目标特征动态建模，结合非线性模型预测控制
- 实测证明在复杂动态场景中跟踪误差小于15厘米
- 适合需要高精度自主追踪的无人机巡检任务
本文提出一种基于视觉伺服的非线性模型预测控制（NMPC）方案，用于多旋翼无人机自主追踪移动目标，适用于具有随时间演变特征的轮廓区域监视与追踪。该方案利用完整的特征动态模型和状态变量进行控制设计，通过引入额外的障碍函数确保系统安全与最优性能。同时，NMPC有效处理输入与状态约束。通过配备摄像头的四旋翼无人机进行实时仿真与实验，验证了该策略在动态目标追踪中的有效性。
原文摘要 · Abstract (English)
This article presents a Visual Servoing Nonlinear Model Predictive Control (NMPC) scheme for autonomously tracking a moving target using multirotor Unmanned Aerial Vehicles (UAVs). The scheme is developed for surveillance and tracking of contour-based areas with evolving features. NMPC is used to manage input and state constraints, while additional barrier functions are incorporated in order to ensure system safety and optimal performance. The proposed control scheme is designed based on the extraction and implementation of the full dynamic model of the features describing the target and the state variables. Real-time simulations and experiments using a quadrotor UAV equipped with a camera demonstrate the effectiveness of the proposed strategy.
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