arbiter
JournalsPricingAccount

Input-Constrained Visual Servoing Formation Control for Quadrotors Using Off-Policy Reinforcement Learning

Xinning Yi et al.

IEEE Transactions on Cybernetics2026https://doi.org/10.1109/tcyb.2026.3656290article
AJG 3
Weight
0.50

What the paper says

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.

Open paper page →

Cite this paper

https://doi.org/https://doi.org/10.1109/tcyb.2026.3656290

Or copy a formatted citation

@article{xinning2026,
  title        = {{Input-Constrained Visual Servoing Formation Control for Quadrotors Using Off-Policy Reinforcement Learning}},
  author       = {Xinning Yi et al.},
  journal      = {IEEE Transactions on Cybernetics},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1109/tcyb.2026.3656290},
}

Paste directly into BibTeX, Zotero, or your reference manager.

Flag this paper

Input-Constrained Visual Servoing Formation Control for Quadrotors Using Off-Policy Reinforcement Learning

Flags are reviewed by the Arbiter methodology team within 5 business days.

Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.