基于自适应动态规划的移动机器人视觉伺服跟踪控制

罗彪; 欧阳志华; 易昕宁; 刘德荣

doi:10.16383/j.aas.c211230

基于自适应动态规划的移动机器人视觉伺服跟踪控制

doi: 10.16383/j.aas.c211230

1.
中南大学自动化学院长沙 410083
2.
南方科技大学工学院深圳 518055

基金项目: 国家自然科学基金(62022094, 62373375), 湖南省自然科学基金(2020JJ2049), 之江实验室开放课题(2021NB0AB01), 中南大学创新驱动项目(2020CX032)资助

详细信息

作者简介:
罗彪：中南大学自动化学院教授. 主要研究方向为智能控制, 强化学习, 深度学习和自主决策. 本文通信作者. E-mail: biao.luo@hotmail.com

欧阳志华：中南大学自动化学院硕士研究生. 主要研究方向为移动机器人, 视觉伺服控制和自适应动态规划. E-mail: ouyangzh@csu.edu.cn

易昕宁：中南大学自动化学院硕士研究生. 主要研究方向为四旋翼视觉伺服控制, 自适应动态规划和强化学习. E-mail: xnyi17@foxmail.com

刘德荣：南方科技大学工学院教授. 主要研究方向为智能控制理论及应用, 自适应动态规划, 人工神经网络和计算神经科学. E-mail: derongliu@foxmail.com

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出版历程
- 收稿日期: 2021-12-24
- 录用日期: 2022-10-17
- 网络出版日期: 2022-12-14
- 刊出日期: 2023-11-22

Adaptive Dynamic Programming Based Visual Servoing Tracking Control for Mobile Robots

1.
School of Automation, Central South University, Changsha 410083
2.
School of Engineering, Southern University of Science and Technology, Shenzhen 518055

Funds: Supported by National Natural Science Foundation of China (62022094, 62373375), Hunan Provincial Natural Science Foundation of China (2020JJ2049), Zhejiang Lab (2021NB0AB01), and Innovation-Driven Project of Central South University (2020CX032)

More Information

Author Bio:
LUO Biao　Professor at the School of Automation, Central South University. His research interest covers intelligent control, reinforcement learning, deep learning, and decision-making. Corresponding author of this paper

OUYANG Zhi-Hua　Master student at the School of Automation, Central South University. His research interest covers mobile robot, visual servoing control, and adaptive dynamic programming

YI Xin-Ning　Master student at the School of Automation, Central South University. Her research interest covers visual servoing control of quadrotor, adaptive dynamic programming, and reinforcement learning

LIU De-Rong　Professor at the School of Engineering, Southern University of Science and Technology. His research interest covers intelligent control theory and application, adaptive dynamic programming, artificial neural networks, and computational neuroscience

摘要

摘要: 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.
- 自适应动态规划 /
- 移动机器人 /
- 视觉伺服 /
- 轨迹跟踪 /
- 神经网络控制
Abstract: 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.
- Adaptive dynamic programming (ADP) /
- mobile robot /
- visual servoing /
- trajectory tracking /
- neural network control

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图 1 视觉伺服轨迹跟踪任务描述

Fig. 1 Visual servoing trajectory tracking task

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图 2 系统响应

Fig. 2 System response

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图 3 控制输入

Fig. 3 Control input

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图 4 评价神经网络权值的收敛

Fig. 4 Convergence of critic neural network weights

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图 5 移动机器人期望轨迹速度与实际运动速度

Fig. 5 Desired and real velocities of the mobile robot

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图 6 HJB方程残差

Fig. 6 The residual error of HJB equation

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图 7 利用本文时变权值神经网络结构方法的移动机器人期望轨迹与实际运动轨迹

Fig. 7 Desired and real trajectories of the mobile robot using time-varying weights neural network structure method in this paper

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图 8 利用时变激活函数神经网络结构方法的移动机器人期望轨迹与实际运动轨迹

Fig. 8 Desired and real trajectories of the mobile robot using time-varying activation neural network structure method

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图 9 特征点二维图像轨迹

Fig. 9 2D image trajectories of the feature points

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