基于自适应动态规划的移动机器人视觉伺服跟踪控制

针对移动机器人视觉伺服跟踪控制问题, 提出一种基于自适应动态规划(Adaptive dynamic programming, ADP) 的控制方法. 通过移动机器人上的相机拍摄共面特征点的当前图像、期望图像以及参考图像, 利用单应性技术得到移动机器人当前的位姿信息与期望的位姿信息(即平移量与旋转角度), 从而通过当前与期望的平移旋转之间差值得到系统的开环误差模型. 进而, 针对此系统设计最优控制器, 同时做合适的控制输入变换. 在此基础上设计一个基于ADP的视觉伺服控制方法以保证移动机器人完成轨迹跟踪任务. 为求出最优控制输入, 采用一个评价神经网络近似值函数, 通过不断学习逼近哈密顿−雅可比−贝尔曼(Hamilton-Jacobi-Bellman, HJB)方程的解. 与以往不同的是, 由于系统存在时变项, 导致HJB方程也含有时变项, 因此需要设计具有时变权值结构的神经网络近似值函数. 最终证明在所设计的控制方法作用下, 闭环系统是一致最终有界的.

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.