Visual-guided Robotic Object Grasping using Dual Neural Network Controllers

Fang, Wubing, Chao, Fei, Lin, Chih-Min, Zhou, Dajun, Yang, Longzhi, Chang, Xiang, Shen, Qiang and Shang, Changjing (2020) Visual-guided Robotic Object Grasping using Dual Neural Network Controllers. IEEE Transactions on Industrial Informatics. ISSN 1551-3203 (In Press)

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This paper proposes a robotic hand-eye coordination system by simulating the human behavior pattern to achieve a fast and robust reaching ability. This is achieved by two neural-network-based controllers, including a rough reaching movement controller implemented by a pre-trained RBF for rough reaching movements, and a correction movement controller built from a specifically designed Brain Emotional Nesting Network (BENN) for smooth correction movements. The proposed BENN is designed with high nonlinear mapping ability, with its adaptive laws derived from the Lyapunov stability theorem; the stability of the proposed control system are guaranteed by the utilisation of the H ∞ control approach. The method is validated and evaluated by a chaos synchronisation simulation, and the overall control system by object grasping tasks through a physical robotic arm in a real-world environment. The experimental results demonstrate the superiority of the proposed control system in reference to those with single neural networks.

Item Type: Article
Uncontrolled Keywords: Robotic hand-eye coordination, neural networkbased controller, robotic reaching movement
Subjects: G400 Computer Science
G500 Information Systems
G600 Software Engineering
H600 Electronic and Electrical Engineering
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Elena Carlaw
Date Deposited: 21 May 2020 11:53
Last Modified: 17 Sep 2020 08:01

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