Online Feedback Optimization Using Contraction Estimator for Multi-robot Relative Pose Estimation and Control
Published in 22nd Polish Control Conference (PCC), 2026
Recommended citation: X. Liu, Q. Zhang, and M. Cao, "Online Feedback Optimization Using Contraction Estimator for Multi-robot Relative Pose Estimation and Control," In: Michałek, M.M., Pazderski, D., Bartoszewicz, A., Kacprzyk, J. (eds) Advances of Control and Automation. PCC 2026. Lecture Notes in Networks and Systems, vol 2072. Springer, Cham. https://doi.org/10.1007/978-3-032-32216-6_2 https://link.springer.com/chapter/10.1007/978-3-032-32216-6_2
In this paper, we propose an online feedback contraction-based method for relative pose estimation and control of teams of robots. To control the robot motions and regulate their pose relative to other robots toward a desired configuration, we develop an end-to-end pipeline that integrates feature-based pose estimation, contraction-driven online feedback optimization, and pose regulation to achieve relative pose convergence of a pair of rigid-body robots evolving on a smooth manifold. The relative pose estimation and control problem is formulated in Lie groups . We use the feature observations to calculate the relative pose between robots and construct the control objective with respect to the relative pose. A contraction-based optimization and control strategy is then employed to steer the robots and ensure relative pose convergence to the target configuration. The stability analysis of the system is presented, and the online feedback contraction-based approach is validated through numerical simulations.