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Neural-Learning-Based Telerobot Control With Guaranteed Performance

Chenguang Yang, Xinyu Wang, Long Cheng, Hongbin Ma

IEEE Transactions on Cybernetics, Volume: 47, Issue: 10, Pages: 3148 - 3159

Swansea University Author: Chenguang Yang

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Abstract

We developed a neural networks (NN) enhanced telerobot control system and tested it on a Baxter robot. Obstacle avoidance at kinematic level enables the human operator only concentrate on motion of robot's end-effector without concern on possible collision. A posture restoration scheme ensures...

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Published in: IEEE Transactions on Cybernetics
ISSN: 2168-2267 2168-2275
Published: 2017
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa28343
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Abstract: We developed a neural networks (NN) enhanced telerobot control system and tested it on a Baxter robot. Obstacle avoidance at kinematic level enables the human operator only concentrate on motion of robot's end-effector without concern on possible collision. A posture restoration scheme ensures the manipulator restore back to the natural posture in the absence of obstacles. Neural networks (NN) enhanced controller at dynamic level compensate for the effect caused by the internal and external uncertainties, e.g., unknown payload. Both the steady state and the transient performance are guaranteed to satisfy a prescribed performance requirement. Comparative experiments have been performed to test the effectiveness and to demonstrate the performance of the proposed methods.
Keywords: Telerobot Control; Neural Networks; Collision Avoidance; Guaranteed Performance
College: Faculty of Science and Engineering
Issue: 10
Start Page: 3148
End Page: 3159