| 1. |
- Gao, Robert X., et al.
(author)
-
Human motion recognition and prediction for robot control
- 2021
-
In: Advanced Human-Robot Collaboration in Manufacturing. - Cham : Springer Nature. ; , s. 261-282
-
Book chapter (other academic/artistic)abstract
- The ever-increasing demand for higher productivity, lower cost and improved safety continues to drive the advancement of manufacturing technologies. As one of the key elements, human-robot collaboration (HRC) envisions a workspace where humans and robots can dynamically collaborate for improved operational efficiency while maintaining safety. As the effectiveness of HRC is affected by a robot's ability to sense, understand and forecast the state of the collaborating human worker, human action recognition and motion trajectory prediction have become a crucial part in realising HRC. In this chapter, deep-learning-based methods for accomplishing this goal, based on the in-situ sensing data from the workspace are presented. Specifically, to account for the variability and heterogeneity of human workers during assembly, a context-aware deep convolutional neural network (DCNN) has been developed to identify the task-associated context for inferencing human actions. To improve the accuracy and reliability of human motion trajectory prediction, a functional unit-incorporated recurrent neural network (RNN) has been developed to parse worker's motion patterns and forecast worker's future motion trajectories. Collectively, these techniques allow the robot to answer the question: "which tool or part should be delivered to which location next?", and enable online robot action planning and execution for the collaborative assembly operation. The methods developed are experimentally evaluated, with the collaborative assembly of an automotive engine as a case study.
|
|
| 2. |
- Xu, X., et al.
(author)
-
Manufacturing Systems
- 2019
-
In: Handbook of Manufacturing. - : World Scientific Pub Co Pte Ltd. ; , s. 609-708
-
Book chapter (other academic/artistic)
|
|
| 3. |
|
|
| 4. |
- Yang, Jikuang, 1948, et al.
(author)
-
Finite element analysis of thorax responses under quasi-static and dynamic loading
- 2013
-
In: Computational Biomechanics for Medicine: Models, Algorithms and Implementation. - New York, NY : Springer New York. - 9781461463511 ; , s. 197-211
-
Book chapter (other academic/artistic)abstract
- This study aimed at investigation of the response of the human thorax under various loading conditions. For this purpose an FE thorax model was developed based on the human anatomical structures. The human thorax consists of ribs, thoracic vertebrae and intervertebral discs, a sternum, costal cartilages and internal organs. Material properties used in this study were based on the published literature. The FE model was used to simulate the phenomenon of thorax compression. The simulations were carried out in different configurations, including the three-point bending of single rib and frontal impacting with a cylinder to a complete thorax at low speed. The results from simulations were compared with the impact responses obtained from biological tests, such as 3-point bending tests and rib structural tests. The entire thorax model was then tested by simulation of volunteer test. The responses predicted by the simulation showed a good biofidelity.
|
|
| 5. |
|
|
