Visual Servoing of Constrained Mobile Robots Based on Model Predictive Control

This paper develops an image-based visual servoing (IBVS) control strategy using model predictive control (MPC) to stabilize a physically constrained mobile robot. In IBVS strategy, ambiguity, and degeneracy problems of the homography and fundamental matrix-based algorithms can be avoided. Moreover,...

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Bibliographic Details
Published in:IEEE transactions on systems, man, and cybernetics. Systems Vol. 47; no. 7; pp. 1428 - 1438
Main Authors: Ke, Fan, Li, Zhijun, Xiao, Hanzhen, Zhang, Xuebo
Format: Journal Article
Language:English
Published: New York IEEE 01.07.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Ambiguity
Cameras
Constraints
Control systems
Control theory
Cost function
Decay
Image-based visual servoing (IBVS)
Kinematics
Mathematical analysis
Mathematical models
Matrices (mathematics)
Matrix methods
Mobile robots
model predictive control (MPC)
Neural networks
neural-dynamic optimization
Optimization
Predictive control
Quadratic programming
Robot kinematics
Servocontrol
Visual control
Visual servoing
ISSN:2168-2216, 2168-2232
Online Access:Get full text
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Summary:This paper develops an image-based visual servoing (IBVS) control strategy using model predictive control (MPC) to stabilize a physically constrained mobile robot. In IBVS strategy, ambiguity, and degeneracy problems of the homography and fundamental matrix-based algorithms can be avoided. Moreover, a synthetic error vector incorporating the advantages of IBVS and position-based visual servoing is defined that includes both the robot angle and image coordinates. By using linear system control theory, the kinematics of nonholonomic chained robotic systems can be transformed into a skew-symmetric form, and through introducing an exponential decay phase, the uncontrollable problem can be solved. Then, an MPC strategy is developed and, thereafter, iteratively transformed into a constrained quadratic programming (QP) problem. Subsequently, we utilize a primal-dual neural network (PDNN) to solve this QP problem. By using PDNN optimization, the cost function of MPC effectively converges to the exact optimal values. Finally, experimental studies on the actual robotic systems have been conducted to demonstrate the performance of the proposed approach.
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ISSN:2168-2216
2168-2232
DOI:10.1109/TSMC.2016.2616486