基于2D三焦点张量的移动机器人视觉伺服镇定控制

李宝全; 方勇纯; 张雪波

doi:10.3724/SP.J.1004.2014.02706

基于2D三焦点张量的移动机器人视觉伺服镇定控制

doi: 10.3724/SP.J.1004.2014.02706

李宝全^1,2,
方勇纯^1,2, ,,
张雪波^1,2

1.
南开大学机器人与信息自动化研究所天津 300071;
2.
南开大学天津市智能机器人技术重点实验室天津 300071

基金项目:

国家自然科学基金(61203333),教育部高等学校博士学科点专项科研基金项目(20120031120040),天津市应用基础与前沿技术研究计划(13JCQNJC03200)资助

详细信息

作者简介:
李宝全南开大学机器人与信息自动化研究所博士研究生. 2010 年于南开大学获得工学学士学位. 2013 年至2014 年于新加坡南洋理工大学公派联合培养.主要研究方向为计算机视觉, 视觉伺服和移动机器人.E-mail: libq@robot.nankai.edu.cn

通讯作者:
方勇纯南开大学机器人与信息自动化研究所教授. 2002 年于美国克莱姆森大学获得博士学位. 主要研究方向为视觉伺服, 微纳米控制系统, 非线性控制以及欠驱动系统控制. 本文通信作者.E-mail: yfang@robot.nankai.edu.cn

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出版历程
- 收稿日期: 2013-05-08
- 修回日期: 2014-06-10
- 刊出日期: 2014-12-20

2D Trifocal Tensor Based Visual Servo Regulation of Nonholonomic Mobile Robots

1.
Institute of Robotics and Automatic Information System, Nankai University, Tianjin 300071;
2.
Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin 300071

Funds:

Supported by National Natural Science Foundation of China (61203333), Specialized Research Fund for the Doctoral Program of Higher Education of China (20120031120040), and Tianjin Research Program of Application Foundation and Advanced Technology (13JCQNJC03200)

摘要

摘要: 针对单目视觉移动机器人系统, 本文提出了一种基于二维三焦点张量(2D trifocal tensor, 2DTT)的视觉伺服镇定控制方法. 具体而言, 首先描述了2D三焦点张量的导出过程, 并给出了基于图像特征点的估计方法. 在此基础上根据2D三焦点张量的元素, 设计了一种反馈线性化控制器以实现机器人的位置镇定, 以及一种比例控制器来实现姿态镇定, 因而在场景信息与平移信息均未知情况下完成了移动机器人的视觉镇定控制. 通过理论分析证明了本文设计的镇定控制算法具有指数收敛性能. 相比现有方法, 这种基于2D 三焦点张量的方法在图像特征识别方面具有更强的鲁棒性, 并且在平面场景与立体场景情况下均适用. 最后利用仿真与实验结果验证了本文提出的视觉伺服方法的优良性能.
- 非完整移动机器人 /
- 镇定控制 /
- 视觉伺服 /
- 2D三焦点张量
Abstract: For a nonholonomic mobile robot system equipped with a fixed camera, we propose a two-dimensional trifocal tensor (2DTT) based visual servo regulation strategy. Firstly, the derivation of the 2DTT is described and then estimated by an image feature based algorithm. Subsequently, by utilizing the entities of the 2DTT, a feedback linearizing position controller is designed to regulate the position errors of the robot, and then a proportion controller is designed to regulate the rotation error. The regulation task is successfully implemented even in the situation that the scene information and the translation parameter are unknown. The exponentially convergent rate of closed-loop systems is proven mathematically. Compared with the existing methods, the 2DTT based strategy is more robust in image feature recognition, and it can be applied to both planar and nonplanar scenes. Simulation and experimental results show the feasibility of the proposed approach.
- Nonholonomic mobile robots /
- regulation control /
- visual servoing /
- 2D trifocal tensor (2DTT)

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