Robust Online Model Predictive Control for a Constrained Image-Based Visual Servoing

被引：111
作者：
Hajiloo, Amir [1 ]
Keshmiri, Mohammad [2 ]
Xie, Wen-Fang [1 ]
Wang, Ting-Ting [3 ]
机构：
[1] Concordia Univ, Dept Mech & Ind Engn, Montreal, PQ H3G 1M8, Canada
[2] McGill Univ, Dept Comp Sci, Montreal, PQ H3A 0G4, Canada
[3] Hohai Univ, Dept Mech & Elect Engn, Changzhou 213022, Peoples R China
来源：
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS | 2016年 / 63卷 / 04期
基金：
中国国家自然科学基金; 加拿大自然科学与工程研究理事会;
关键词：
Image-based visual servoing (IBVS); model predictive controller (MPC); robotic; visual servoing; STABILITY; SYSTEMS;
D O I：
10.1109/TIE.2015.2510505
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper presents an online image-based visual servoing (IBVS) controller for a 6-degrees-of-freedom (DOF) robotic system based on the robust model predictive control (RMPC) method. The controller is designed considering the robotic visual servoing system's input and output constraints, such as robot physical limitations and visibility constraints. The proposed IBVS controller avoids the inverse of the image Jacobian matrix and hence can solve the intractable problems for the classical IBVS controller, such as large displacements between the initial and the desired positions of the camera. To verify the effectiveness of the proposed algorithm, real-time experimental results on a 6-DOF robot manipulator with eye-in-hand configuration are presented and discussed.
引用
收藏
页码：2242 / 2250
页数：9
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