Input-Constrained Visual Servoing Formation Control for Quadrotors Using Off-Policy Reinforcement Learning
提出一种无需机间通信或相对位置测量的四旋翼视觉伺服编队控制器，利用离策略强化学习处理输入和可见性约束，适用于通信受限环境下的无人机编队控制。
In this article, an input-constrained visual servoing formation controller is proposed for multiple quadrotor systems operating without intervehicle communication or relative position measurements. The aerial formation control is achieved by formulating image-based leader-follower dynamics using a virtual camera framework and sphere-based image moments. An adaptive velocity observer is developed for the follower quadrotor to estimate the relative velocity with respect to the leader quadrotor in communication-free environments. Input-constrained visual servoing and attitude controllers are proposed using an off-policy reinforcement learning (RL) algorithm to handle visibility and attitude constraints, without relying on accurate system model parameters. The stability of the closed-loop system is theoretically analyzed, and the effectiveness of the proposed controller is demonstrated through case studies.