Image-Based Visual Servoing of a 7-DOF Robot Manipulator Using an Adaptive Distributed Fuzzy PD Controller

Indrazno Siradjuddin, Laxmidhar Behera, TM McGinnity, SA Coleman

Research output: Contribution to journalArticle

45 Citations (Scopus)

Abstract

This paper is concerned with the design and implementation of a distributed proportional–derivative (PD) controller of a 7-degrees of freedom (DOF) robot manipulator using the Takagi–Sugeno (T–S) fuzzy framework. Existing machine learning approaches to visual servoing involve system identification of image and kinematic Jacobians. In contrast, the proposed approach actuates a control signal primarily as a function of the error and derivative of the error in the desired visual feature space. This approach leads to a significant reduction in the computational burden as compared to model-based approaches, as well as existing learning approaches to model inverse kinematics. The simplicity of the controller structure will make it attractive in industrial implementations where PD/PID type schemes are in common use.While the initial values of PD gain are learned with the help of model-based controller, an online adaptation scheme has been proposed that is capable of compensating for local uncertainties associated with the system and its environment. Rigorous experiments have been performed to show that visual servoing tasks such as reaching a static target and tracking of a moving target can be achieved using the proposed distributed PD controller. It is shown that the proposed adaptive scheme can dynamically tune the controller parameters during visual servoing, so as to improve its initial performance based on parameters obtained while mimicking the model-based controller. The proposed control scheme is applied and assessed in real-time experiments using an uncalibrated eye-in-hand robotic system with a 7-DOF PowerCube robot manipulator.
LanguageEnglish
Pages512-523
JournalIEEE/ASME Transactions on Mechatronics
Volume19
Issue number2
DOIs
Publication statusPublished - Apr 2014

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Visual servoing
Manipulators
Robots
Derivatives
Controllers
Inverse kinematics
End effectors
Learning systems
Identification (control systems)
Kinematics
Experiments

Cite this

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abstract = "This paper is concerned with the design and implementation of a distributed proportional–derivative (PD) controller of a 7-degrees of freedom (DOF) robot manipulator using the Takagi–Sugeno (T–S) fuzzy framework. Existing machine learning approaches to visual servoing involve system identification of image and kinematic Jacobians. In contrast, the proposed approach actuates a control signal primarily as a function of the error and derivative of the error in the desired visual feature space. This approach leads to a significant reduction in the computational burden as compared to model-based approaches, as well as existing learning approaches to model inverse kinematics. The simplicity of the controller structure will make it attractive in industrial implementations where PD/PID type schemes are in common use.While the initial values of PD gain are learned with the help of model-based controller, an online adaptation scheme has been proposed that is capable of compensating for local uncertainties associated with the system and its environment. Rigorous experiments have been performed to show that visual servoing tasks such as reaching a static target and tracking of a moving target can be achieved using the proposed distributed PD controller. It is shown that the proposed adaptive scheme can dynamically tune the controller parameters during visual servoing, so as to improve its initial performance based on parameters obtained while mimicking the model-based controller. The proposed control scheme is applied and assessed in real-time experiments using an uncalibrated eye-in-hand robotic system with a 7-DOF PowerCube robot manipulator.",
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Image-Based Visual Servoing of a 7-DOF Robot Manipulator Using an Adaptive Distributed Fuzzy PD Controller. / Siradjuddin, Indrazno; Behera, Laxmidhar; McGinnity, TM; Coleman, SA.

Vol. 19, No. 2, 04.2014, p. 512-523.

Research output: Contribution to journalArticle

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