| 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. |
- Iakovidis, Dimitris K., et al.
(author)
-
Roadmap on signal processing for next generation measurement systems
- 2022
-
In: Measurement Science and Technology. - : IOP Publishing. - 0957-0233 .- 1361-6501. ; 33:1
-
Research review (peer-reviewed)abstract
- Signal processing is a fundamental component of almost any sensor-enabled system, with a wide range of applications across different scientific disciplines. Time series data, images, and video sequences comprise representative forms of signals that can be enhanced and analysed for information extraction and quantification. The recent advances in artificial intelligence and machine learning are shifting the research attention towards intelligent, data-driven, signal processing. This roadmap presents a critical overview of the state-of-the-art methods and applications aiming to highlight future challenges and research opportunities towards next generation measurement systems. It covers a broad spectrum of topics ranging from basic to industrial research, organized in concise thematic sections that reflect the trends and the impacts of current and future developments per research field. Furthermore, it offers guidance to researchers and funding agencies in identifying new prospects.
|
|
| 3. |
|
|
| 4. |
- Zhang, Jianjing, et al.
(author)
-
Neural rendering-enabled 3D modeling for rapid digitization of in-service products
- 2023
-
In: CIRP annals. - : Elsevier BV. - 0007-8506 .- 1726-0604. ; 72:1, s. 93-96
-
Journal article (peer-reviewed)abstract
- Rapid digitization of physical objects enables monitoring, analysis, and maintenance of in-service products, of which an up-to-date CAD model is not available. It provides designers with the products' actual response to the real-world usage, which provides a reference base for design optimization. This paper presents neural rendering as a novel method for rapid digital model building. It learns a radiance field from RGB images to determine the characteristics of the physical object. Textured mesh can be generated from the learned radi-ance field for efficient 3D modeling. The effectiveness of the method is demonstrated by an engine component.
|
|
