Online Feedback Optimization Using Contraction Estimator for Multi-robot Relative Pose Estimation and Control
Date:
This is a talk for the 22nd Polish Control Conference (PCC) at Poznan, Poland!
We are glad to present our paper, entitled ‘Online Feedback Optimization Using Contraction Estimator for Multi-robot Relative Pose Estimation and Control’ 22nd Polish Control Conference (PCC). The paper can be accessed here.
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 SE(n). 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.