Latest Developments of Gesture Recognition for Human–Robot Collaboration

2021 ◽  
pp. 43-68
Author(s):  
Hongyi Liu ◽  
Lihui Wang
Keyword(s):  
Gesture Recognition ◽  
Human Robot Collaboration

scholarly journals Gesture Recognition Using a Depth Camera for Human Robot Collaboration on Assembly Line

2015 ◽  
Vol 3 ◽  
pp. 518-525 ◽  
Author(s):  
Eva Coupeté ◽  
Fabien Moutarde ◽  
Sotiris Manitsaris
Keyword(s):  
Gesture Recognition ◽  
Assembly Line ◽  
Depth Camera ◽  
Human Robot Collaboration

Design of a Real-Time Human-Robot Collaboration System Using Dynamic Gestures

2020 ◽  
Author(s):  
Haodong Chen ◽  
Ming C. Leu ◽  
Wenjin Tao ◽  
Zhaozheng Yin
Keyword(s):  
Real Time ◽  
Gesture Recognition ◽  
Industrial Robot ◽  
Classification Model ◽  
Robotic Arm ◽  
The Real ◽  
Time Motion ◽  
Dynamic Gestures ◽  
Dynamic Gesture Recognition ◽  
Human Robot Collaboration

Abstract With the development of industrial automation and artificial intelligence, robotic systems are developing into an essential part of factory production, and the human-robot collaboration (HRC) becomes a new trend in the industrial field. In our previous work, ten dynamic gestures have been designed for communication between a human worker and a robot in manufacturing scenarios, and a dynamic gesture recognition model based on Convolutional Neural Networks (CNN) has been developed. Based on the model, this study aims to design and develop a new real-time HRC system based on multi-threading method and the CNN. This system enables the real-time interaction between a human worker and a robotic arm based on dynamic gestures. Firstly, a multi-threading architecture is constructed for high-speed operation and fast response while schedule more than one task at the same time. Next, A real-time dynamic gesture recognition algorithm is developed, where a human worker’s behavior and motion are continuously monitored and captured, and motion history images (MHIs) are generated in real-time. The generation of the MHIs and their identification using the classification model are synchronously accomplished. If a designated dynamic gesture is detected, it is immediately transmitted to the robotic arm to conduct a real-time response. A Graphic User Interface (GUI) for the integration of the proposed HRC system is developed for the visualization of the real-time motion history and classification results of the gesture identification. A series of actual collaboration experiments are carried out between a human worker and a six-degree-of-freedom (6 DOF) Comau industrial robot, and the experimental results show the feasibility and robustness of the proposed system.

One-shot gesture recognition with attention-based DTW for human-robot collaboration

2019 ◽  
Vol 40 (1) ◽  
pp. 40-47
Author(s):  
Yiqun Kuang ◽  
Hong Cheng ◽  
Yali Zheng ◽  
Fang Cui ◽  
Rui Huang
Keyword(s):  
Gesture Recognition ◽  
Distance Measure ◽  
Local Features ◽  
Feature Descriptor ◽  
Content Type ◽  
High Efficient ◽  
Region Extraction ◽  
Dynamic Time ◽  
Similar Solutions ◽  
Human Robot Collaboration

Purpose This paper aims to present a one-shot gesture recognition approach which can be a high-efficient communication channel in human–robot collaboration systems. Design/methodology/approach This paper applies dynamic time warping (DTW) to align two gesture sequences in temporal domain with a novel frame-wise distance measure which matches local features in spatial domain. Furthermore, a novel and robust bidirectional attention region extraction method is proposed to retain information in both movement and hold phase of a gesture. Findings The proposed approach is capable of providing efficient one-shot gesture recognition without elaborately designed features. The experiments on a social robot (JiaJia) demonstrate that the proposed approach can be used in a human–robot collaboration system flexibly. Originality/value According to previous literature, there are no similar solutions that can achieve an efficient gesture recognition with simple local feature descriptor and combine the advantages of local features with DTW.

scholarly journals An improved gesture recognition algorithm for human-robot collaboration

2020 ◽  
Vol 1693 ◽  
pp. 012199
Author(s):  
Yiqun Liu ◽  
Xiaogang Wang ◽  
Yang He ◽  
Hua Mu ◽  
Yuewei Bai
Keyword(s):  
Gesture Recognition ◽  
Recognition Algorithm ◽  
Human Robot Collaboration

Dynamic Gesture Design and Recognition for Human-Robot Collaboration With Convolutional Neural Networks

2020 ◽  
Author(s):  
Haodong Chen ◽  
Wenjin Tao ◽  
Ming C. Leu ◽  
Zhaozheng Yin
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Gesture Recognition ◽  
Industrial Robot ◽  
Recognition System ◽  
Dynamic Gestures ◽  
Static Images ◽  
Gesture Design ◽  
Dynamic Gesture Recognition ◽  
Human Robot Collaboration

Abstract Human-robot collaboration (HRC) is a challenging task in modern industry and gesture communication in HRC has attracted much interest. This paper proposes and demonstrates a dynamic gesture recognition system based on Motion History Image (MHI) and Convolutional Neural Networks (CNN). Firstly, ten dynamic gestures are designed for a human worker to communicate with an industrial robot. Secondly, the MHI method is adopted to extract the gesture features from video clips and generate static images of dynamic gestures as inputs to CNN. Finally, a CNN model is constructed for gesture recognition. The experimental results show very promising classification accuracy using this method.

Customized Gesture Recognition for Educational Games

2016 ◽  
Vol 3 (2) ◽  
pp. 1
Author(s):  
Seong Jeong ◽  
HongJun Ju ◽  
Hyo-Rim Choi ◽  
TaeYong Kim
Keyword(s):  
Gesture Recognition ◽  
Educational Games

Mobile Robot Operation by Gesture Recognition using Iterative Motion

2015 ◽  
Vol 135 (8) ◽  
pp. 944-953 ◽  
Author(s):  
Kazuki Tanaka ◽  
Hiroyuki Ukida
Keyword(s):  
Mobile Robot ◽  
Gesture Recognition
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