Input-Constrained Visual Servoing Formation Control for Quadrotors Using Off-Policy Reinforcement Learning
Abstract
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.
Fields of Study & Subjects
Distributed Control Multi-Agent Systems
Robotic Path Planning Algorithms
Adaptive Control of Nonlinear Systems
Publication details
- Journal / source
- IEEE Transactions on Cybernetics
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Volume
- 56
- Issue
- 6
- Pages
- 3540–3550
- Publication date
- 6 February 2026
- Language
- EN