Point cloud based path planning for tower crane lifting

Lihui Huang; Yuzhe Zhang; Jianmin Zheng; Panpan Cai; Souravik Dutta; Yufeng Yue; Nadia Thalmann; Yiyu Cai

doi:10.1145/3208159.3208186

Point cloud based path planning for tower crane lifting

Lihui Huang, Yuzhe Zhang, Jianmin Zheng, Panpan Cai, Souravik Dutta, Yufeng Yue, Nadia Thalmann, Yiyu Cai

Nanyang Technological University

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

9 引用（Scopus）

摘要

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.

源语言	英语
主期刊名	Proceedings of Computer Graphics International, CGI 2018
出版商	Association for Computing Machinery
页	211-215
页数	5
ISBN（电子版）	1595930361, 9781450364010
DOI	https://doi.org/10.1145/3208159.3208186
出版状态	已出版 - 11 6月 2018
已对外发布	是
活动	2018 Computer Graphics International Conference, CGI 2018 - Bintan, 印度尼西亚期限: 11 6月 2018 → 14 6月 2018

出版系列

姓名	ACM International Conference Proceeding Series

会议

会议	2018 Computer Graphics International Conference, CGI 2018
国家/地区	印度尼西亚
市	Bintan
时期	11/06/18 → 14/06/18

访问文件

10.1145/3208159.3208186

其它文件与链接

链接到 Scopus 的出版物

引用此

Huang, L., Zhang, Y., Zheng, J., Cai, P., Dutta, S., Yue, Y., Thalmann, N., & Cai, Y. (2018). Point cloud based path planning for tower crane lifting. 在 Proceedings of Computer Graphics International, CGI 2018 (页码 211-215). (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3208159.3208186

@inproceedings{f9f855d2fac440659c1fa2b75d08cdc2,

title = "Point cloud based path planning for tower crane lifting",

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.",

keywords = "Automatic lifting path planning, Collision check, Complex environment, Depth map, Genetic algorithm, Point cloud, Rasterization, Tower crane",

author = "Lihui Huang and Yuzhe Zhang and Jianmin Zheng and Panpan Cai and Souravik Dutta and Yufeng Yue and Nadia Thalmann and Yiyu Cai",

note = "Publisher Copyright: {\textcopyright} 2018 ACM.; 2018 Computer Graphics International Conference, CGI 2018 ; Conference date: 11-06-2018 Through 14-06-2018",

year = "2018",

month = jun,

day = "11",

doi = "10.1145/3208159.3208186",

language = "English",

series = "ACM International Conference Proceeding Series",

publisher = "Association for Computing Machinery",

pages = "211--215",

booktitle = "Proceedings of Computer Graphics International, CGI 2018",

}

Huang, L, Zhang, Y, Zheng, J, Cai, P, Dutta, S, Yue, Y, Thalmann, N & Cai, Y 2018, Point cloud based path planning for tower crane lifting. 在 Proceedings of Computer Graphics International, CGI 2018. ACM International Conference Proceeding Series, Association for Computing Machinery, 页码 211-215, 2018 Computer Graphics International Conference, CGI 2018, Bintan, 印度尼西亚, 11/06/18. https://doi.org/10.1145/3208159.3208186

TY - GEN

T1 - Point cloud based path planning for tower crane lifting

AU - Huang, Lihui

AU - Zhang, Yuzhe

AU - Zheng, Jianmin

AU - Cai, Panpan

AU - Dutta, Souravik

AU - Yue, Yufeng

AU - Thalmann, Nadia

AU - Cai, Yiyu

PY - 2018/6/11

Y1 - 2018/6/11

N2 - 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.

AB - 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.

KW - Automatic lifting path planning

KW - Collision check

KW - Complex environment

KW - Depth map

KW - Genetic algorithm

KW - Point cloud

KW - Rasterization

KW - Tower crane

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U2 - 10.1145/3208159.3208186

DO - 10.1145/3208159.3208186

M3 - Conference contribution

AN - SCOPUS:85062835974

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BT - Proceedings of Computer Graphics International, CGI 2018

PB - Association for Computing Machinery

T2 - 2018 Computer Graphics International Conference, CGI 2018

Y2 - 11 June 2018 through 14 June 2018

ER -

Point cloud based path planning for tower crane lifting

摘要

出版系列

会议

访问文件

其它文件与链接

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引用此