Path planning of UAVs based on improved ant colony system

Yue Sun; Jinchao Chen; Chenglie Du

doi:10.1109/PIC50277.2020.9350789

Path planning of UAVs based on improved ant colony system

School of Computer Science

Northwestern Polytechnical University Xian

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

21 Scopus citations

Abstract

Multi-UAV Cooperative Path Planning is important for improving the efficiency of UAVs in completing reconnaissance, surveillance, and search missions due to its parallelism and fault tolerance. This paper investigates the problem of path planning for multi-mission UAVs under complex constraints, and proposes a new method of UAV path optimization that minimizes task completion time. First, with models of separated regions and heterogeneous UAVs, the objective function and flight constraints are analyzed to fully search the best solutions for the path planning problem. Then, considering a dense target group model, a path planning method based on an Improved Ant Colony System algorithm (IACS) is proposed. The IACS algorithm is able to achieve an efficiency enhancement in time consumption on targets detection and minimize the total time of UAV mission execution by adopting a novel target exploring scheme called BI-directional simplified search strategy in the target selection process. Finally, the effectiveness of the proposed method is verified through simulation experiments with randomly generated targets.

Original language	English
Title of host publication	Proceedings of 2020 IEEE International Conference on Progress in Informatics and Computing, PIC 2020
Editors	Yinglin Wang, Yanghua Xiao
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	396-400
Number of pages	5
ISBN (Electronic)	9781728170862
DOIs	https://doi.org/10.1109/PIC50277.2020.9350789
State	Published - 18 Dec 2020
Event	7th IEEE International Conference on Progress in Informatics and Computing, PIC 2020 - Shanghai, China Duration: 18 Dec 2020 → 20 Dec 2020

Publication series

Name	Proceedings of 2020 IEEE International Conference on Progress in Informatics and Computing, PIC 2020

Conference

Conference	7th IEEE International Conference on Progress in Informatics and Computing, PIC 2020
Country/Territory	China
City	Shanghai
Period	18/12/20 → 20/12/20

Keywords

Ant colony system
Bi-directional simplified search strategy
multi-UAV
Path planning

Access to Document

10.1109/PIC50277.2020.9350789

Cite this

Sun, Y., Chen, J., & Du, C. (2020). Path planning of UAVs based on improved ant colony system. In Y. Wang, & Y. Xiao (Eds.), Proceedings of 2020 IEEE International Conference on Progress in Informatics and Computing, PIC 2020 (pp. 396-400). Article 9350789 (Proceedings of 2020 IEEE International Conference on Progress in Informatics and Computing, PIC 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/PIC50277.2020.9350789

Sun, Yue ; Chen, Jinchao ; Du, Chenglie. / Path planning of UAVs based on improved ant colony system. Proceedings of 2020 IEEE International Conference on Progress in Informatics and Computing, PIC 2020. editor / Yinglin Wang ; Yanghua Xiao. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 396-400 (Proceedings of 2020 IEEE International Conference on Progress in Informatics and Computing, PIC 2020).

@inproceedings{2987203fec6e4971b518e224f752b036,

title = "Path planning of UAVs based on improved ant colony system",

abstract = "Multi-UAV Cooperative Path Planning is important for improving the efficiency of UAVs in completing reconnaissance, surveillance, and search missions due to its parallelism and fault tolerance. This paper investigates the problem of path planning for multi-mission UAVs under complex constraints, and proposes a new method of UAV path optimization that minimizes task completion time. First, with models of separated regions and heterogeneous UAVs, the objective function and flight constraints are analyzed to fully search the best solutions for the path planning problem. Then, considering a dense target group model, a path planning method based on an Improved Ant Colony System algorithm (IACS) is proposed. The IACS algorithm is able to achieve an efficiency enhancement in time consumption on targets detection and minimize the total time of UAV mission execution by adopting a novel target exploring scheme called BI-directional simplified search strategy in the target selection process. Finally, the effectiveness of the proposed method is verified through simulation experiments with randomly generated targets.",

keywords = "Ant colony system, Bi-directional simplified search strategy, multi-UAV, Path planning",

author = "Yue Sun and Jinchao Chen and Chenglie Du",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 7th IEEE International Conference on Progress in Informatics and Computing, PIC 2020 ; Conference date: 18-12-2020 Through 20-12-2020",

year = "2020",

month = dec,

day = "18",

doi = "10.1109/PIC50277.2020.9350789",

language = "英语",

series = "Proceedings of 2020 IEEE International Conference on Progress in Informatics and Computing, PIC 2020",

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

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booktitle = "Proceedings of 2020 IEEE International Conference on Progress in Informatics and Computing, PIC 2020",

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Sun, Y, Chen, J & Du, C 2020, Path planning of UAVs based on improved ant colony system. in Y Wang & Y Xiao (eds), Proceedings of 2020 IEEE International Conference on Progress in Informatics and Computing, PIC 2020., 9350789, Proceedings of 2020 IEEE International Conference on Progress in Informatics and Computing, PIC 2020, Institute of Electrical and Electronics Engineers Inc., pp. 396-400, 7th IEEE International Conference on Progress in Informatics and Computing, PIC 2020, Shanghai, China, 18/12/20. https://doi.org/10.1109/PIC50277.2020.9350789

Path planning of UAVs based on improved ant colony system. / Sun, Yue; Chen, Jinchao ; Du, Chenglie.
Proceedings of 2020 IEEE International Conference on Progress in Informatics and Computing, PIC 2020. ed. / Yinglin Wang; Yanghua Xiao. Institute of Electrical and Electronics Engineers Inc., 2020. p. 396-400 9350789 (Proceedings of 2020 IEEE International Conference on Progress in Informatics and Computing, PIC 2020).

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

TY - GEN

T1 - Path planning of UAVs based on improved ant colony system

AU - Sun, Yue

AU - Chen, Jinchao

AU - Du, Chenglie

PY - 2020/12/18

Y1 - 2020/12/18

N2 - Multi-UAV Cooperative Path Planning is important for improving the efficiency of UAVs in completing reconnaissance, surveillance, and search missions due to its parallelism and fault tolerance. This paper investigates the problem of path planning for multi-mission UAVs under complex constraints, and proposes a new method of UAV path optimization that minimizes task completion time. First, with models of separated regions and heterogeneous UAVs, the objective function and flight constraints are analyzed to fully search the best solutions for the path planning problem. Then, considering a dense target group model, a path planning method based on an Improved Ant Colony System algorithm (IACS) is proposed. The IACS algorithm is able to achieve an efficiency enhancement in time consumption on targets detection and minimize the total time of UAV mission execution by adopting a novel target exploring scheme called BI-directional simplified search strategy in the target selection process. Finally, the effectiveness of the proposed method is verified through simulation experiments with randomly generated targets.

AB - Multi-UAV Cooperative Path Planning is important for improving the efficiency of UAVs in completing reconnaissance, surveillance, and search missions due to its parallelism and fault tolerance. This paper investigates the problem of path planning for multi-mission UAVs under complex constraints, and proposes a new method of UAV path optimization that minimizes task completion time. First, with models of separated regions and heterogeneous UAVs, the objective function and flight constraints are analyzed to fully search the best solutions for the path planning problem. Then, considering a dense target group model, a path planning method based on an Improved Ant Colony System algorithm (IACS) is proposed. The IACS algorithm is able to achieve an efficiency enhancement in time consumption on targets detection and minimize the total time of UAV mission execution by adopting a novel target exploring scheme called BI-directional simplified search strategy in the target selection process. Finally, the effectiveness of the proposed method is verified through simulation experiments with randomly generated targets.

KW - Ant colony system

KW - Bi-directional simplified search strategy

KW - multi-UAV

KW - Path planning

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PB - Institute of Electrical and Electronics Engineers Inc.

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Y2 - 18 December 2020 through 20 December 2020

ER -

Sun Y, Chen J , Du C. Path planning of UAVs based on improved ant colony system. In Wang Y, Xiao Y, editors, Proceedings of 2020 IEEE International Conference on Progress in Informatics and Computing, PIC 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 396-400. 9350789. (Proceedings of 2020 IEEE International Conference on Progress in Informatics and Computing, PIC 2020). doi: 10.1109/PIC50277.2020.9350789

Path planning of UAVs based on improved ant colony system

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