Recurrent neural network for motion trajectory prediction in human-robot collaborative assembly

Zhang, Jianjing; Liu, Hongyi; Chang, Qing; Wang, Lihui; Gao, Robert X.

doi:10.1016/j.cirp.2020.04.077

Cited by 119 publications

(55 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recurrent neural networks have also been proposed for predicting future human postures [14], [15]. One of the main challenges of these methods is to encode the multi-value behaviour of the human, coming by its redundant structure, and to evaluate the different solutions [16].…”

Section: B Human Posture Predictionmentioning

confidence: 99%

Human Posture Prediction During Physical Human-Robot Interaction

Vianello

Mouret

Dalin

et al. 2021

IEEE Robot. Autom. Lett.

View full text Add to dashboard Cite

Section: B Human Posture Predictionmentioning

confidence: 99%

Human Posture Prediction During Physical Human-Robot Interaction

Vianello

Mouret

Dalin

et al. 2021

IEEE Robot. Autom. Lett.

View full text Add to dashboard Cite

“…The motion data, after preprocessing and recognition, are stored in the database in the data calculation layer. The preprocessed data is stored in the database in the form of a data stream, but it is difficult to obtain useful information from this type of data; as such, the frequency domain features of the data need to be extracted [28]. Sliding window is adopted to segment the time series data to produce a series of motion signal segments.…”

Section: Data Calculation Layermentioning

confidence: 99%

Optimization of a Sports Activity Development Model Using Artificial Intelligence under New Curriculum Reform

Liu

Wilczyńska

Lipowski

et al. 2021

IJERPH

View full text Add to dashboard Cite

The recent curriculum reform in China puts forward higher requirements for the development of physical education. In order to further improve students’ physical quality and motor skills, the traditional model was improved to address the lack of accuracy in motion recognition and detection of physical condition so as to assist teachers to improve students’ physical quality. First, the physical education teaching activities required by the new curriculum reform were studied with regard to the actual needs of China’s current social, political, and economic development; next, the application of artificial intelligence technology to physical education teaching activities was proposed; and finally, deep learning technology was studied and a human movement recognition model based on a long short-term memory (LSTM) neural network was established to identify the movement state of students in physical education teaching activities. The designed model includes three components: data acquisition, data calculation, and data visualization. The functions of each layer were introduced; then, the intelligent wearable system was adopted to detect the status of students and a feedback system was established to assist teaching; and finally, the dataset was constructed to train and test the designed model. The experimental results demonstrate that the recognition accuracy and loss value of the training model meet the practical requirements; in the algorithm test, the motion recognition accuracy of the designed model for different subjects was greater than 97.5%. Compared with the traditional human motion recognition algorithm, the designed model had a better recognition effect. Hence, the designed model can meet the actual needs of physical education. This exploration provides a new perspective for promoting the intelligent development of physical education.

show abstract

“…In Ref. [92], an RNN-based human motion trajectory predictive model parses the interaction among human body parts for more accurate trajectory prediction. Further, Monte-Carlo dropout was investigated to measure the prediction uncertainty and improve the model robustness.…”

Section: Learnmentioning

confidence: 99%

Artificial Intelligence in Advanced Manufacturing: Current Status and Future Outlook

Arinez

Chang

Gao

et al. 2020

Journal of Manufacturing Science and Engineering

Self Cite

236

View full text Add to dashboard Cite

Today's manufacturing systems are becoming increasingly complex, dynamic and connected. The factory operation faces challenges of highly nonlinear and stochastic activity due to the countless uncertainties and interdependencies that exist. Recent developments in Artificial Intelligence (AI), especially Machine Learning (ML) have shown great potential to transform the manufacturing domain through advanced analytics tools for processing the vast amounts of manufacturing data generated, known as Big Data. The focus of this paper is threefold: (1) Review the State-of-the-Art applications of AI to representative manufacturing problems, (2) Provide a systematic view for analyzing data and process dependencies at multiple levels that AI must comprehend, and (3) Identify challenges and opportunities to not only further leverage AI for manufacturing, but also influence the future development of AI to better meet the needs of manufacturing. To satisfy these objectives, the paper adopts the hierarchical organization widely practiced in manufacturing plants in examining the interdependencies from the overall system level to the more detailed granular level of incoming material process streams. In doing so, the paper considers a wide range of topics from throughput and quality, supervisory control in human robotic collaboration, process monitoring, diagnosis and prognosis, finally to advances in materials engineering to achieve desired material property in process modeling and control.

show abstract

Recurrent neural network for motion trajectory prediction in human-robot collaborative assembly

Cited by 119 publications

References 9 publications

Human Posture Prediction During Physical Human-Robot Interaction

Human Posture Prediction During Physical Human-Robot Interaction

Optimization of a Sports Activity Development Model Using Artificial Intelligence under New Curriculum Reform

Artificial Intelligence in Advanced Manufacturing: Current Status and Future Outlook

Contact Info

Product

Resources

About