Deep learning-based human motion recognition for predictive context-aware human-robot collaboration

Wang, Peng; Liu, Hongyi; Wang, Lihui; Gao, Robert X.

doi:10.1016/j.cirp.2018.04.066

Cited by 177 publications

(69 citation statements)

References 7 publications

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“…Contact-less sensing has a wide range of applications in HRC. Wang et al [254] analysed the sequence of traditional 2D images of human motion in assembly for context-aware recognition. A method to monitor the degradation of industrial robots was introduced by Qiao and Weiss [205], by coordinating different cameras to measure 7D information (time, X, Y, Z, roll, pitch, and yaw).…”

Section: Contact-less Sensingmentioning

confidence: 99%

“…16. Deep learning for human motion recognition and prediction [254]. Within the context, an efficient HRC system should be able to understand a human operator's intention and assist the operator during assembly [208].…”

Section: Convolutional Layers Input Imagementioning

confidence: 99%

“…Visual observation of humans' motion provides informative clues about the specific tasks to be performed, thus can be used to establish reliable context awareness. Wang et al [254] investigated deep learning as a data-driven technique for continuous human motion analysis and prediction, leading to improved robot planning and control in accomplishing a shared task. Fig.…”

Section: Convolutional Layers Input Imagementioning

confidence: 99%

“…Sample video frames (top); sequence of recognised human motions (middle); sequence of identified objects (bottom)[254].…”

mentioning

confidence: 99%

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Symbiotic human-robot collaborative assembly

et al. 2019

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In human-robot collaborative assembly, robots are often required to dynamically change their pre-planned tasks to collaborate with human operators in a shared workspace. However, the robots used today are controlled by pre-generated rigid codes that cannot support effective human-robot collaboration. In response to this need, multi-modal yet symbiotic communication and control methods have been a focus in recent years. These methods include voice processing, gesture recognition, haptic interaction, and brainwave perception. Deep learning is used for classification, recognition and context awareness identification. Within this context, this keynote provides an overview of symbiotic human-robot collaborative assembly and highlights future research directions.

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Section: Contact-less Sensingmentioning

confidence: 99%

Section: Convolutional Layers Input Imagementioning

confidence: 99%

Section: Convolutional Layers Input Imagementioning

confidence: 99%

“…Sample video frames (top); sequence of recognised human motions (middle); sequence of identified objects (bottom)[254].…”

mentioning

confidence: 99%

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Symbiotic human-robot collaborative assembly

et al. 2019

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“…The related methods to realize this technology have been stuck in the manual stage, which is not only a large workload but also inefficient. Human posture and specific movements are a way for people to interact with surrounding information or creatures [2]. The research around motion recognition can be traced back to the 1980s in the world.…”

Section: Introductionmentioning

confidence: 99%

Kinect-Based Motion Recognition Tracking Robotic Arm Platform

Gao¹,

Chen²,

Li³

2019

ICA

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The development of artificial intelligence technology has promoted the rapid improvement of human-computer interaction. This system uses the Kinect visual image sensor to identify human bone data and complete the recognition of the operator's movements. Through the filtering process of real-time data by the host computer platform with computer software as the core, the algorithm is programmed to realize the conversion from data to control signals. The system transmits the signal to the lower computer platform with Arduino as the core through the transmission mode of the serial communication, thereby completing the control of the steering gear. In order to verify the feasibility of the theory, the team built a 4-DOF robotic arm control system and completed software development. It can display other functions such as the current bone angle and motion status in real time on the computer operation interface. The experimental data shows that the Kinect-based motion recognition method can effectively complete the tracking of the expected motion and complete the grasping and transfer of the specified objects, which has extremely high operability.

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