论文标题
$（3）$的模型预测性球形图像的视觉宣传
Model Predictive Spherical Image-Based Visual Servoing On $SO(3)$ for Aggressive Aerial Tracking
论文作者
论文摘要
本文提出了一种基于图像的视觉伺服控制（IBVS）方法，用于进行第一人称视图（FPV）四极管，以进行积极的空中跟踪。使用IBV操纵不足的车辆面临三个主要挑战：（i）找到对大型旋转且适合优化变量的视觉特征表示形式； （ii）在不牺牲机器人敏捷性的情况下保持目标可见； （iii）补偿检测到的特征中的旋转效应。我们建议一个完整的设计框架来解决这些问题。首先，我们在$ SO（3）$上使用旋转来代表$ S^{2} $上的球形图像功能，以获得无奇异性和二阶可区分属性。为了确保目标可见性，我们将IBV作为非线性模型预测控制（NMPC）问题，并考虑了三个约束：机器人的物理限制，目标可见性和碰撞时间（TTC）。此外，我们提出了一种新颖的态度补偿方案，以启用在实际图像平面中的可见性约束，而不是虚拟固定取向图像平面。它保证可见性限制在大旋转下是有效的。广泛的实验结果表明，我们的方法可以在没有定位系统的情况下稳定而积极地跟踪快速移动的目标。
This paper presents an image-based visual servo control (IBVS) method for a first-person-view (FPV) quadrotor to conduct aggressive aerial tracking. There are three major challenges to maneuvering an underactuated vehicle using IBVS: (i) finding a visual feature representation that is robust to large rotations and is suited to be an optimization variable; (ii) keeping the target visible without sacrificing the robot's agility; and (iii) compensating for the rotational effects in the detected features. We propose a complete design framework to address these problems. First, we employ a rotation on $SO(3)$ to represent a spherical image feature on $S^{2}$ to gain singularity-free and second-order differentiable properties. To ensure target visibility, we formulate the IBVS as a nonlinear model predictive control (NMPC) problem with three constraints taken into account: the robot's physical limits, target visibility, and time-to-collision (TTC). Furthermore, we propose a novel attitude-compensation scheme to enable formulating the visibility constraint in the actual image plane instead of a virtual fix-orientation image plane. It guarantees that the visibility constraint is valid under large rotations. Extensive experimental results show that our method can track a fast-moving target stably and aggressively without the aid of a localization system.
