Robust Vision-Based Tube Model Predictive Control of Multiple Mobile Robots for Leader–Follower Formation

Abstract

Generally, vision-based controls use various camera sensors and require camera calibration, while the control performance would degrade due to inaccuracy calibration. Therefore, in this paper, the proposed controller only makes use of the image information from an un-calibrated perspective camera mounted at the follower robot without relative position measurement or any communication among the robots. First, the nominal visual formation kinematic model is developed using the camera models. Then it is redescribed as a quadratic programming (QP) with the specified constraints. A neurodynamic optimization based on primal-dual neural network is utilized to ensure the QP being converged to the exact optimal values. Through two-time-scale neuro-dynamical optimization, the gain scheduling of the ancillary state feedback can be realized so that the state variables are constrained within an invariant designed tube. The experiment results provide the verification for the effectiveness of the proposed approach.