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Designing Human-like Behaviors for Anthropomorphic Arm in Humanoid Robot NAO

Published online by Cambridge University Press:  30 September 2019

Yuan Wei Open the ORCID record for Yuan Wei [Opens in a new window]  and
Jing Zhao
Yuan Wei*
Affiliation:
Mechanical Engineering & Applied Electronics Technology, Beijing University of Technology, Beijing, China. E-mail: zhaojing@bjut.edu.cn Vehicle & Transportation Engineering Institute, Henan University of Science and Technology, Luoyang Shi, China
Jing Zhao
Affiliation:
Mechanical Engineering & Applied Electronics Technology, Beijing University of Technology, Beijing, China. E-mail: zhaojing@bjut.edu.cn
*
*Corresponding author. E-mail: tsubasafx@foxmail.com
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Summary

Human-like motion of robots can improve human–robot interaction and increase the efficiency. In this paper, a novel human-like motion planning strategy is proposed to help anthropomorphic arms generate human-like movements accurately. The strategy consists of three parts: movement primitives, Bayesian network (BN), and a novel coupling neural network (CPNN). The movement primitives are used to decouple the human arm movements. The classification of arm movements improves the accuracy of human-like movements. The motion-decision algorithm based on BN is able to predict occurrence probabilities of the motions and choose appropriate mode of motion. Then, a novel CPNN is proposed to solve the inverse kinematics problems of anthropomorphic arms. The CPNN integrates different models into a single network and reflects the features of these models by changing the network structure. Through the strategy, the anthropomorphic arms can generate various human-like movements with satisfactory accuracy. Finally, the availability of the proposed strategy is verified by simulations for the general motion of humanoid NAO.

Keywords

Anthropomorphic armMotion planningHuman-like movementsBayesian networkNeural network
Type
Articles
Information
Robotica , Volume 38 , Issue 7 , July 2020 , pp. 1205 - 1226
Copyright
© Cambridge University Press 2019

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