Name
Abstract | ||
|---|---|---|
This paper discusses automatic path planning for tower crane lifting in highly complex environments to be digitized using point cloud representation. A mathematical optimization technique is developed to identify the lifting path with GPU accelerated massively parallel genetic algorithm. A continuous collision detection method is designed for real time application of collision avoidance during the crane lifting process. |
Year | Venue | Field |
|---|---|---|
2018 | CGI | Motion planning,Computer vision,Collision detection,Massively parallel,Computer science,Collision,Real-time computing,Artificial intelligence,Depth map,Tower crane,Point cloud,Genetic algorithm |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
1 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lihui Huang | 1 | 3 | 1.13 |
| Yuzhe Zhang | 2 | 8 | 2.19 |
| jianmin zheng | 3 | 1024 | 99.03 |
| Panpan Cai | 4 | 0 | 0.34 |
| Souravik Dutta | 5 | 0 | 0.68 |
| Yufeng Yue | 6 | 8 | 5.73 |
| Nadia Magnenat-Thalmann | 7 | 5119 | 659.15 |
| Yiyu Cai | 8 | 202 | 36.94 |