A Framework for Real-Time Physical Human-Robot Interaction using Hand Gestures

Osama Mazhar, S. Ramdani, B. Navarro, R. Passama
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引用次数: 3

Abstract

A physical Human-Robot Interaction (pHRI) framework is proposed using vision and force sensors for a two-way object hand-over task. Kinect v2 is integrated with the state-of-the-art 2D skeleton extraction library namely Openpose to obtain a 3D skeleton of the human operator. A robust and rotation invariant (in the coronal plane) hand gesture recognition system is developed by exploiting a convolutional neural network. This network is trained such that the gestures can be recognized without the need to pre-process the RGB hand images at run time. This work establishes a firm basis for the robot control using hand-gestures. This will be extended for the development of intelligent human intention detection in pHRI scenarios to efficiently recognize a variety of static as well as dynamic gestures.
使用手势的实时物理人机交互框架
提出了一种基于视觉和力传感器的人机交互(pHRI)框架。Kinect v2集成了最先进的2D骨骼提取库,即Openpose,以获得人类操作员的3D骨骼。利用卷积神经网络,开发了一种鲁棒的旋转不变性(冠状面)手势识别系统。该网络经过训练,可以在不需要在运行时预处理RGB手部图像的情况下识别手势。这项工作为机器人手势控制奠定了坚实的基础。这将扩展到pHRI场景中智能人类意图检测的开发,以有效识别各种静态和动态手势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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