Adaptive Dynamic Programming Based Visual Servoing Tracking Control for Mobile Robots

In this paper, a visual servoing approach based on adaptive dynamic programming (ADP) is developed for the trajectory tracking control of mobile robots. First, according to the current image, the desired image and reference image sequence of coplanar feature points are captured by the on-board camera. The current pose information and desired pose information of the mobile robot can be reconstructed by homography technology. Then, the open-loop error model of the system is obtained by the difference between the current translation and rotation and the desired. In order to design the optimal controller for this system, the appropriate control input transformation is adopted. Therefore, a visual servoing approach based on ADP is proposed to achieve the trajectory tracking task for the mobile robot. A critic neural network structure is used to learn the time-varying solution, namely the optimal value function, of the Hamilton-Jacobi-Bellman (HJB) equation. Owing to the existence of time-varying terms, which is different from many existing works, the HJB equation is time-varying. Therefore, a neural network with a time-varying weight structure is designed to approximate the time-dependent value function of the HJB equation. Finally, it is proved that the approach proposed in this paper guarantees the closed-loop system is uniformly ultimately bounded.