We present a perception constrained visual predictive control (PCVPC) algorithm for quadrotors to enable aggressive flights without using any position information. Our framework leverages nonlinear model predictive control (NMPC) to formulate a constrained image-based visual servoing (IBVS) problem. The quadrotor dynamics, image dynamics, actuation constraints, and visibility constraints are taken into account to handle quadrotor maneuvers with high agility. Two main challenges of applying IBVS to agile drones are considered: (i) high sensitivity of depths to intense orientation changes, and (ii) conflict between the visual servoing objective and action objective due to the underactuated nature. To deal with the first challenge, we parameterize a visual feature by a bearing vector and a distance, by which the depth will no longer be involved in the image dynamics. Meanwhile, we settle the conflict problem by compensating for the rotation in the future visual servoing cost using the predicted orientations of the quadrotor. Our approach in simulation shows that (i) it can work without any position information, (ii) it can achieve a maximum referebce speed of 9 m/s in trajectory tracking without losing the target, and (iii) it can reach a landmark, e.g., a gate in drone racing, from varied initial configurations.

中文翻译：

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PCVPC：敏捷四旋翼的感知约束视觉预测控制

我们提出了一种用于四旋翼飞行器的感知约束视觉预测控制 (PCVPC) 算法，以在不使用任何位置信息的情况下实现激进的飞行。我们的框架利用非线性模型预测控制 (NMPC) 来制定受约束的基于图像的视觉伺服 (IBVS) 问题。考虑到四旋翼飞行器动力学、图像动力学、驱动约束和可见性约束，以高敏捷性处理四旋翼飞行器机动。考虑将 IBVS 应用于敏捷无人机的两个主要挑战：（i）深度对强烈方向变化的高度敏感性，以及（ii）由于欠驱动性质导致的视觉伺服目标和动作目标之间的冲突。为了应对第一个挑战，我们通过方位向量和距离参数化视觉特征，深度将不再参与图像动态。同时，我们通过使用四旋翼飞行器的预测方向补偿未来视觉伺服成本中的旋转来解决冲突问题。我们的模拟方法表明：（i）它可以在没有任何位置信息的情况下工作，（ii）它可以在不丢失目标的情况下在轨迹跟踪中达到 9 m/s 的最大参考速度，以及（iii）它可以到达一个地标，例如，无人机竞赛中的一扇门，来自不同的初始配置。