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Deep neural network approach in human-like redundancy optimization for anthropomorphic manipulators

Su, Hang; Qi, Wen; Yang, Chenguang; Aliverti, Andrea; Ferrigno, Giancarlo; De Momi, Elena

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Authors

Hang Su

Wen Qi

Andrea Aliverti

Giancarlo Ferrigno

Elena De Momi



Contributors

Abstract

© 2013 IEEE. Human-like behavior has emerged in the robotics area for improving the quality of Human-Robot Interaction (HRI). For the human-like behavior imitation, the kinematic mapping between a human arm and robot manipulator is one of the popular solutions. To fulfill this requirement, a reconstruction method called swivel motion was adopted to achieve human-like imitation. This approach aims at modeling the regression relationship between robot pose and swivel motion angle. Then it reaches the human-like swivel motion using its redundant degrees of the manipulator. This characteristic holds for most of the redundant anthropomorphic robots. Although artificial neural network (ANN) based approaches show moderate robustness, the predictive performance is limited. In this paper, we propose a novel deep convolutional neural network (DCNN) structure for reconstruction enhancement and reducing online prediction time. Finally, we utilized the trained DCNN model for managing redundancy control a 7 DoFs anthropomorphic robot arm (LWR4+, KUKA, Germany) for validation. A demonstration is presented to show the human-like behavior on the anthropomorphic manipulator. The proposed approach can also be applied to control other anthropomorphic robot manipulators in industry area or biomedical engineering.

Citation

Su, H., Qi, W., Yang, C., Aliverti, A., Ferrigno, G., & De Momi, E. (2019). Deep neural network approach in human-like redundancy optimization for anthropomorphic manipulators. IEEE Access, 7, 124207-124216. https://doi.org/10.1109/ACCESS.2019.2937380

Journal Article Type Article
Acceptance Date Aug 20, 2019
Online Publication Date Aug 26, 2019
Publication Date Aug 26, 2019
Deposit Date Aug 27, 2019
Publicly Available Date Mar 29, 2024
Journal IEEE Access
Electronic ISSN 2169-3536
Publisher Institute of Electrical and Electronics Engineers (IEEE)
Peer Reviewed Peer Reviewed
Volume 7
Pages 124207-124216
DOI https://doi.org/10.1109/ACCESS.2019.2937380
Keywords General Engineering; General Materials Science; General Computer Science
Public URL https://uwe-repository.worktribe.com/output/2503774

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