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Motion recognition using deep convolutional neural network for Kinect-based NAO teleoperation

Published online by Cambridge University Press:  28 February 2022

Archana Balmik Open the ORCID record for Archana Balmik [Opens in a new window] ,
Arnab Paikaray ,
Mrityunjay Jha  and
Anup Nandy
Archana Balmik*
Affiliation:
Department of Computer Science and Engineering, NIT Rourkela, Rourkela, India
Arnab Paikaray
Affiliation:
Department of Computer Science and Engineering, NIT Rourkela, Rourkela, India
Mrityunjay Jha
Affiliation:
Department of Computer Science and Engineering, NIT Rourkela, Rourkela, India
Anup Nandy
Affiliation:
Department of Computer Science and Engineering, NIT Rourkela, Rourkela, India
*
*Corresponding author. E-mail: [email protected]
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Abstract

The capabilities of teleoperated robots can be enhanced with the ability to recognise and reproduce human-like behaviour. The proposed framework presents motion recognition for a Kinect-based NAO teleoperation. It allows the NAO robot to recognise the human motions and act as a human motion imitator. A stable whole-body imitation is still a challenging issue because of the difficulty in dynamic balancing of centre of mass (CoM). In this paper, a novel adaptive balancing technique for NAO (ABTN) is proposed to control the whole body in single as well as double supporting phases. It targets dynamic balancing of the humanoid robot by solving forward kinematics and applying a weighted average of mass with the CoMs of individual links with respect to the previous joint frames, which provides us with the dynamic CoM of the whole body. Our novel approach uses this dynamic CoM and calculates joint angles using proposed pitch and roll control algorithm to keep the dynamic CoM inside the stable region. Additionally, the NAO robot is capable of recognising human motions using the proposed 7-layer one-dimensional convolutional neural network (1D-CNN). To solve the problem of variable length of time sequences, Zero padding is introduced with 1D-CNN. It attains a recognition accuracy of 95% as compared to the hidden Markov model and neural network. The experimental results demonstrate that the developed teleoperation framework is robust and serves as potential support for the development and application of teleoperated robots.

Keywords

teleoperationhumanoid robot NAOKinect v2human motion recognitionbalance control strategyconvolutional neural network
Type
Research Article
Information
Robotica , Volume 40 , Issue 9 , September 2022 , pp. 3233 - 3253
Copyright
© The Author(s), 2022. Published by Cambridge University Press

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