Cooperative search of UAV swarm based on improved ant colony algorithm in uncertain environment

Fan Yang; Xiuling Ji; Chengwei Yang; Jie Li; Bing Li

doi:10.1109/ICUS.2017.8278346

Cooperative search of UAV swarm based on improved ant colony algorithm in uncertain environment

Fan Yang, Xiuling Ji, Chengwei Yang^*, Jie Li, Bing Li

^*Corresponding author for this work

School of Mechatronical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

28 Citations (Scopus)

Abstract

Considering the situation that the regional environment is completely unknown and is dynamically changing, a novel method for multi-UAV (Unmanned Aerial Vehicle) conducting cooperative area search based on Ant Colony (AC) Theory is proposed. Firstly, the regional environment model and the UAV dynamic model were established. Secondly, the transfer rules of the waypoints were determined for UAVs by improving the behavior criterion of the ant colony algorithm and the updating principle of the pheromone map. Finally, Compared with the conventional search methods, the simulation results with higher coverage rate and search efficiency demonstrated the improved ant colony algorithm was rationality and validity.

Original language	English
Title of host publication	Proceedings of 2017 IEEE International Conference on Unmanned Systems, ICUS 2017
Editors	Xin Xu
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	231-236
Number of pages	6
ISBN (Electronic)	9781538631065
DOIs	https://doi.org/10.1109/ICUS.2017.8278346
Publication status	Published - 2 Jul 2017
Event	2017 IEEE International Conference on Unmanned Systems, ICUS 2017 - Beijing, China Duration: 27 Oct 2017 → 29 Oct 2017

Publication series

Name	Proceedings of 2017 IEEE International Conference on Unmanned Systems, ICUS 2017
Volume	2018-January

Conference

Conference	2017 IEEE International Conference on Unmanned Systems, ICUS 2017
Country/Territory	China
City	Beijing
Period	27/10/17 → 29/10/17

Keywords

cooperative area search
improved ant colony algorithm
pheromone map
swarm
unmanned aerial vehicle (UAV)

Access to Document

10.1109/ICUS.2017.8278346

Cite this

Yang, F., Ji, X., Yang, C., Li, J., & Li, B. (2017). Cooperative search of UAV swarm based on improved ant colony algorithm in uncertain environment. In X. Xu (Ed.), Proceedings of 2017 IEEE International Conference on Unmanned Systems, ICUS 2017 (pp. 231-236). (Proceedings of 2017 IEEE International Conference on Unmanned Systems, ICUS 2017; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICUS.2017.8278346

Yang, Fan ; Ji, Xiuling ; Yang, Chengwei et al. / Cooperative search of UAV swarm based on improved ant colony algorithm in uncertain environment. Proceedings of 2017 IEEE International Conference on Unmanned Systems, ICUS 2017. editor / Xin Xu. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 231-236 (Proceedings of 2017 IEEE International Conference on Unmanned Systems, ICUS 2017).

@inproceedings{5a0923efff0d4b02afff13a2b5e9123f,

title = "Cooperative search of UAV swarm based on improved ant colony algorithm in uncertain environment",

abstract = "Considering the situation that the regional environment is completely unknown and is dynamically changing, a novel method for multi-UAV (Unmanned Aerial Vehicle) conducting cooperative area search based on Ant Colony (AC) Theory is proposed. Firstly, the regional environment model and the UAV dynamic model were established. Secondly, the transfer rules of the waypoints were determined for UAVs by improving the behavior criterion of the ant colony algorithm and the updating principle of the pheromone map. Finally, Compared with the conventional search methods, the simulation results with higher coverage rate and search efficiency demonstrated the improved ant colony algorithm was rationality and validity.",

keywords = "cooperative area search, improved ant colony algorithm, pheromone map, swarm, unmanned aerial vehicle (UAV)",

author = "Fan Yang and Xiuling Ji and Chengwei Yang and Jie Li and Bing Li",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE International Conference on Unmanned Systems, ICUS 2017 ; Conference date: 27-10-2017 Through 29-10-2017",

year = "2017",

month = jul,

day = "2",

doi = "10.1109/ICUS.2017.8278346",

language = "English",

series = "Proceedings of 2017 IEEE International Conference on Unmanned Systems, ICUS 2017",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "231--236",

editor = "Xin Xu",

booktitle = "Proceedings of 2017 IEEE International Conference on Unmanned Systems, ICUS 2017",

address = "United States",

}

Yang, F, Ji, X, Yang, C , Li, J & Li, B 2017, Cooperative search of UAV swarm based on improved ant colony algorithm in uncertain environment. in X Xu (ed.), Proceedings of 2017 IEEE International Conference on Unmanned Systems, ICUS 2017. Proceedings of 2017 IEEE International Conference on Unmanned Systems, ICUS 2017, vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 231-236, 2017 IEEE International Conference on Unmanned Systems, ICUS 2017, Beijing, China, 27/10/17. https://doi.org/10.1109/ICUS.2017.8278346

Cooperative search of UAV swarm based on improved ant colony algorithm in uncertain environment. / Yang, Fan; Ji, Xiuling; Yang, Chengwei et al.
Proceedings of 2017 IEEE International Conference on Unmanned Systems, ICUS 2017. ed. / Xin Xu. Institute of Electrical and Electronics Engineers Inc., 2017. p. 231-236 (Proceedings of 2017 IEEE International Conference on Unmanned Systems, ICUS 2017; Vol. 2018-January).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Cooperative search of UAV swarm based on improved ant colony algorithm in uncertain environment

AU - Yang, Fan

AU - Ji, Xiuling

AU - Yang, Chengwei

AU - Li, Jie

AU - Li, Bing

PY - 2017/7/2

Y1 - 2017/7/2

N2 - Considering the situation that the regional environment is completely unknown and is dynamically changing, a novel method for multi-UAV (Unmanned Aerial Vehicle) conducting cooperative area search based on Ant Colony (AC) Theory is proposed. Firstly, the regional environment model and the UAV dynamic model were established. Secondly, the transfer rules of the waypoints were determined for UAVs by improving the behavior criterion of the ant colony algorithm and the updating principle of the pheromone map. Finally, Compared with the conventional search methods, the simulation results with higher coverage rate and search efficiency demonstrated the improved ant colony algorithm was rationality and validity.

AB - Considering the situation that the regional environment is completely unknown and is dynamically changing, a novel method for multi-UAV (Unmanned Aerial Vehicle) conducting cooperative area search based on Ant Colony (AC) Theory is proposed. Firstly, the regional environment model and the UAV dynamic model were established. Secondly, the transfer rules of the waypoints were determined for UAVs by improving the behavior criterion of the ant colony algorithm and the updating principle of the pheromone map. Finally, Compared with the conventional search methods, the simulation results with higher coverage rate and search efficiency demonstrated the improved ant colony algorithm was rationality and validity.

KW - cooperative area search

KW - improved ant colony algorithm

KW - pheromone map

KW - swarm

KW - unmanned aerial vehicle (UAV)

UR - http://www.scopus.com/inward/record.url?scp=85046680533&partnerID=8YFLogxK

U2 - 10.1109/ICUS.2017.8278346

DO - 10.1109/ICUS.2017.8278346

M3 - Conference contribution

AN - SCOPUS:85046680533

T3 - Proceedings of 2017 IEEE International Conference on Unmanned Systems, ICUS 2017

SP - 231

EP - 236

BT - Proceedings of 2017 IEEE International Conference on Unmanned Systems, ICUS 2017

A2 - Xu, Xin

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2017 IEEE International Conference on Unmanned Systems, ICUS 2017

Y2 - 27 October 2017 through 29 October 2017

ER -

Yang F, Ji X, Yang C , Li J, Li B. Cooperative search of UAV swarm based on improved ant colony algorithm in uncertain environment. In Xu X, editor, Proceedings of 2017 IEEE International Conference on Unmanned Systems, ICUS 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 231-236. (Proceedings of 2017 IEEE International Conference on Unmanned Systems, ICUS 2017). doi: 10.1109/ICUS.2017.8278346

Cooperative search of UAV swarm based on improved ant colony algorithm in uncertain environment

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