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Neural-Learning-Based Telerobot Control With Guaranteed Performance
IEEE Transactions on Cybernetics, Volume: 47, Issue: 10, Pages: 3148 - 3159
Swansea University Author: Chenguang Yang
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DOI (Published version): 10.1109/tcyb.2016.2573837
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...
| Published in: | IEEE Transactions on Cybernetics |
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| ISSN: | 2168-2267 2168-2275 |
| Published: |
2017
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| Online Access: |
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| 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. |
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| Keywords: |
Telerobot Control; Neural Networks; Collision Avoidance; Guaranteed Performance |
| College: |
Faculty of Science and Engineering |
| Issue: |
10 |
| Start Page: |
3148 |
| End Page: |
3159 |