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.
Dynamic Gesture Design and Recognition for Human-Robot Collaboration With Convolutional Neural Networks
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.
more » « less- Award ID(s):
- 1646162
- NSF-PAR ID:
- 10212187
- Date Published:
- Journal Name:
- Proceedings of the 2020 International Symposium on Flexible Automation (ISFA 2020)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract -
Abstract As artificial intelligence and industrial automation are developing, human–robot collaboration (HRC) with advanced interaction capabilities has become an increasingly significant area of research. In this paper, we design and develop a real-time, multi-model HRC system using speech and gestures. A set of 16 dynamic gestures is designed for communication from a human to an industrial robot. A data set of dynamic gestures is designed and constructed, and it will be shared with the community. A convolutional neural network is developed to recognize the dynamic gestures in real time using the motion history image and deep learning methods. An improved open-source speech recognizer is used for real-time speech recognition of the human worker. An integration strategy is proposed to integrate the gesture and speech recognition results, and a software interface is designed for system visualization. A multi-threading architecture is constructed for simultaneously operating multiple tasks, including gesture and speech data collection and recognition, data integration, robot control, and software interface operation. The various methods and algorithms are integrated to develop the HRC system, with a platform constructed to demonstrate the system performance. The experimental results validate the feasibility and effectiveness of the proposed algorithms and the HRC system.more » « less
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Human-Robot Collaboration (HRC), which envisions a workspace in which human and robot can dynamically collaborate, has been identified as a key element in smart manufacturing. Human action recognition plays a key role in the realization of HRC as it helps identify current human action and provides the basis for future action prediction and robot planning. Despite recent development of Deep Learning (DL) that has demonstrated great potential in advancing human action recognition, one of the key issues remains as how to effectively leverage the temporal information of human motion to improve the performance of action recognition. Furthermore, large volume of training data is often difficult to obtain due to manufacturing constraints, which poses challenge for the optimization of DL models. This paper presents an integrated method based on optical flow and convolutional neural network (CNN)-based transfer learning to tackle these two issues. First, optical flow images, which encode the temporal information of human motion, are extracted and serve as the input to a two-stream CNN structure for simultaneous parsing of spatial-temporal information of human motion. Then, transfer learning is investigated to transfer the feature extraction capability of a pretrained CNN to manufacturing scenarios. Evaluation using engine block assembly confirmed the effectiveness of the developed method.more » « less
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Human-Robot Collaboration (HRC), which enables a workspace where human and robot can dynamically and safely collaborate for improved operational efficiency, has been identified as a key element in smart manufacturing. Human action recognition plays a key role in the realization ofHRC, as it helps identify current human action and provides the basis for future action prediction and robot planning. While Deep Learning (DL) has demonstrated great potential in advancing human action recognition, effectively leveraging the temporal information of human motions to improve the accuracy and robustness of action recognition has remained as a challenge. Furthermore, it is often difficult to obtain a large volume of data for DL network training and optimization, due to operational constraints in a realistic manufacturing setting. This paper presents an integrated method to address these two challenges, based on the optical flow and convolutional neural network (CNN)based transfer learning. Specifically, optical flow images, which encode the temporal information of human motion, are extracted and serve as the input to a two-stream CNN structure for simultaneous parsing of spatial-temporal information of human motion. Subsequently, transfer learning is investigated to transfer the feature extraction capability of a pre-trained CNN to manufacturing scenarios. Evaluation using engine block assembly confirmed the effectiveness of the developed method.more » « less
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People with hearing and speaking disabilities face significant hurdles in communication. The knowledge of sign language can help mitigate these hurdles, but most people without disabilities, including relatives, friends, and care providers, cannot understand sign language. The availability of automated tools can allow people with disabilities and those around them to communicate ubiquitously and in a variety of situations with non-signers. There are currently two main approaches for recognizing sign language gestures. The first is a hardware-based approach, involving gloves or other hardware to track hand position and determine gestures. The second is a software-based approach, where a video is taken of the hands and gestures are classified using computer vision techniques. However, some hardware, such as a phone's internal sensor or a device worn on the arm to track muscle data, is less accurate, and wearing them can be cumbersome or uncomfortable. The software-based approach, on the other hand, is dependent on the lighting conditions and on the contrast between the hands and the background. We propose a hybrid approach that takes advantage of low-cost sensory hardware and combines it with a smart sign-recognition algorithm with the goal of developing a more efficient gesture recognition system. The Myo band-based approach using the Support Vector Machine method achieves an accuracy of only 49%. The software-based approach uses the Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) methods to train the Myo-based module and achieves an accuracy of over 80% in our experiments. Our method combines the two approaches and shows the potential for improvement. Our experiments are done with a dataset of nine gestures generated from multiple videos, each repeated five times for a total of 45 trials for both the software-based and hardware-based modules. Apart from showing the performance of each approach, our results show that with a more improved hardware module, the accuracy of the combined approach can be significantly improvedmore » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1115/ISFA2020-9609
