Shiyu Fang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Shiyu Fang has a total of 7 co-authors. Here are the top co-authors by number of shared publications:
- Degang Xu
School of Automation, Central South University, Changsha 410083, China
2 shared publications - Jiahui Shi
School of Automation, Central South University, Changsha, 410083, China
1 shared publication - Qing Song
School of Automation, Central South University, Changsha 410083, China
1 shared publication - Weihua Gui
Central South University
1 shared publication - Yanrui Guo
College of Automation, Central South University, Changsha 410083, China
1 shared publication - Yizhi Wang
School of Automation, Central South University, Changsha, China
1 shared publication
Latest Publications
- 6 November 2024
- Published by Elsevier in ISA Transactions
- Vol. 156, 579-596
Heuristic dense reward shaping for learning-based map-free navigation of industrial automatic mobile robots
- 6 November 2024
- Published by Elsevier in ISA Transactions
- Vol. 156, 579-596
Heuristic dense reward shaping for learning-based map-free navigation of industrial automatic mobile robots
- Open Access
Sensing Method for Wet Spraying Process of Tunnel Wall Based on the Laser LiDAR in Complex Environment
In tunnel lining construction, the traditional manual wet spraying operation is labor-intensive and can be challenging to ensure consistent quality. To address this, this study proposes a LiDAR-based method for sensing the thickness of tunnel wet spray, which aims to improve efficiency and quality. The proposed method utilizes an adaptive point cloud standardization processing algorithm to address differing point cloud postures and missing data, and the segmented Lamé curve is employed to fit the tunnel design axis using the Gauss–Newton iteration method. This establishes a mathematical model of the tunnel section and enables the analysis and perception of the thickness of the tunnel to be wet sprayed through comparison with the actual inner contour line and the design line of the tunnel. Experimental results show that the proposed method is effective in sensing the thickness of tunnel wet spray, with important implications for promoting intelligent wet spraying operations, improving wet spraying quality, and reducing labor costs in tunnel lining construction.
Open AccessSensing Method for Wet Spraying Process of Tunnel Wall Based on the Laser LiDAR in Complex Environment
In tunnel lining construction, the traditional manual wet spraying operation is labor-intensive and can be challenging to ensure consistent quality. To address this, this study proposes a LiDAR-based method for sensing the thickness of tunnel wet spray, which aims to improve efficiency and quality. The proposed method utilizes an adaptive point cloud standardization processing algorithm to address differing point cloud postures and missing data, and the segmented Lamé curve is employed to fit the tunnel design axis using the Gauss–Newton iteration method. This establishes a mathematical model of the tunnel section and enables the analysis and perception of the thickness of the tunnel to be wet sprayed through comparison with the actual inner contour line and the design line of the tunnel. Experimental results show that the proposed method is effective in sensing the thickness of tunnel wet spray, with important implications for promoting intelligent wet spraying operations, improving wet spraying quality, and reducing labor costs in tunnel lining construction.