Constrained Visual Servoing of Quadrotors Based on Model Predictive Control

Abstract

One of the main issues of visual servoing schemes occurs when the target objects leave out the field of view (FOV) of the camera, which causes failure or poor performance of the controller. Solving this problem can be a challenge due to traditional controllers cannot include system's constraints. This work presents model predictive control (MPC) for constrained image-based visual servoing (IBVS) applied in quadrotors, considering constraints in FOV and restrictions in the control actions. To handle with image constraints the MPC considers: (1) the target objects stay only in camera's FOV and this work converts these restrictions in state constraints, (2) merge image instantaneous kinematics and the dynamic of commercial quadrotors (Mavic Pro 2) in a general mathematical model in order to satisfy the bounded control actions and image constraints. Due to commercial quadrotors allow velocities like control inputs, this work considers the reduced dynamic model in general velocities space and it was identified using Dynamic Mode Decomposition with control (DMDc) algorithm. This work uses Webots to evaluate the performance of the proposed controller. Finally the controller is compared with a classical IBVS scheme in order to verify the efficacy of the proposed controller and systematically evaluate the performance considering the system constraints.