Self-Organizing Neural Network based Mission Planning for Space Unmanned System

Hongxing Zheng; Jifeng Guo; Chengchao Bai

doi:10.1109/ICUS48101.2019.8996054

Self-Organizing Neural Network based Mission Planning for Space Unmanned System

School of Astronautics, Harbin Institute of Technology

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

1 Scopus citations

Abstract

In this paper, the task planning problem of multi-mission location information collection for space unmanned system with long-range detection sensors is considered and modeled as a NP hard combinatorial optimization problem. An algorithm for solving planning problems based on self-organizing neural network is proposed to construct a zero-distance information collection path. On this basis, the effective detection areas of space unmanned system at each mission point are sampled. Four search operators are designed and the appropriate detection points are searched by local search algorithm. In addition, the mission scenario is constructed by random initial multi-mission sites. The simulation and analysis of the algorithm verify the validity of the proposed algorithm for the problem of space unmanned system information collection mission planning.

Original language	English
Title of host publication	Proceedings of the 2019 IEEE International Conference on Unmanned Systems, ICUS 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	114-118
Number of pages	5
ISBN (Electronic)	9781728137926
DOIs	https://doi.org/10.1109/ICUS48101.2019.8996054
State	Published - Oct 2019
Externally published	Yes
Event	2019 IEEE International Conference on Unmanned Systems, ICUS 2019 - Beijing, China Duration: 17 Oct 2019 → 19 Oct 2019

Publication series

Name	Proceedings of the 2019 IEEE International Conference on Unmanned Systems, ICUS 2019

Conference

Conference	2019 IEEE International Conference on Unmanned Systems, ICUS 2019
Country/Territory	China
City	Beijing
Period	17/10/19 → 19/10/19

Keywords

information collecting
local search
mission planning
self-organizing neural network
space unmanned system

Access to Document

10.1109/ICUS48101.2019.8996054

Cite this

Zheng, H., Guo, J., & Bai, C. (2019). Self-Organizing Neural Network based Mission Planning for Space Unmanned System. In Proceedings of the 2019 IEEE International Conference on Unmanned Systems, ICUS 2019 (pp. 114-118). Article 8996054 (Proceedings of the 2019 IEEE International Conference on Unmanned Systems, ICUS 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICUS48101.2019.8996054

Zheng, Hongxing ; Guo, Jifeng ; Bai, Chengchao. / Self-Organizing Neural Network based Mission Planning for Space Unmanned System. Proceedings of the 2019 IEEE International Conference on Unmanned Systems, ICUS 2019. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 114-118 (Proceedings of the 2019 IEEE International Conference on Unmanned Systems, ICUS 2019).

@inproceedings{9bb30814e01c4592aff8678954e2c591,

title = "Self-Organizing Neural Network based Mission Planning for Space Unmanned System",

abstract = "In this paper, the task planning problem of multi-mission location information collection for space unmanned system with long-range detection sensors is considered and modeled as a NP hard combinatorial optimization problem. An algorithm for solving planning problems based on self-organizing neural network is proposed to construct a zero-distance information collection path. On this basis, the effective detection areas of space unmanned system at each mission point are sampled. Four search operators are designed and the appropriate detection points are searched by local search algorithm. In addition, the mission scenario is constructed by random initial multi-mission sites. The simulation and analysis of the algorithm verify the validity of the proposed algorithm for the problem of space unmanned system information collection mission planning.",

keywords = "information collecting, local search, mission planning, self-organizing neural network, space unmanned system",

author = "Hongxing Zheng and Jifeng Guo and Chengchao Bai",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE International Conference on Unmanned Systems, ICUS 2019 ; Conference date: 17-10-2019 Through 19-10-2019",

year = "2019",

month = oct,

doi = "10.1109/ICUS48101.2019.8996054",

language = "英语",

series = "Proceedings of the 2019 IEEE International Conference on Unmanned Systems, ICUS 2019",

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

pages = "114--118",

booktitle = "Proceedings of the 2019 IEEE International Conference on Unmanned Systems, ICUS 2019",

address = "美国",

}

Zheng, H , Guo, J & Bai, C 2019, Self-Organizing Neural Network based Mission Planning for Space Unmanned System. in Proceedings of the 2019 IEEE International Conference on Unmanned Systems, ICUS 2019., 8996054, Proceedings of the 2019 IEEE International Conference on Unmanned Systems, ICUS 2019, Institute of Electrical and Electronics Engineers Inc., pp. 114-118, 2019 IEEE International Conference on Unmanned Systems, ICUS 2019, Beijing, China, 17/10/19. https://doi.org/10.1109/ICUS48101.2019.8996054

Self-Organizing Neural Network based Mission Planning for Space Unmanned System. / Zheng, Hongxing ; Guo, Jifeng ; Bai, Chengchao.
Proceedings of the 2019 IEEE International Conference on Unmanned Systems, ICUS 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 114-118 8996054 (Proceedings of the 2019 IEEE International Conference on Unmanned Systems, ICUS 2019).

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

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N2 - In this paper, the task planning problem of multi-mission location information collection for space unmanned system with long-range detection sensors is considered and modeled as a NP hard combinatorial optimization problem. An algorithm for solving planning problems based on self-organizing neural network is proposed to construct a zero-distance information collection path. On this basis, the effective detection areas of space unmanned system at each mission point are sampled. Four search operators are designed and the appropriate detection points are searched by local search algorithm. In addition, the mission scenario is constructed by random initial multi-mission sites. The simulation and analysis of the algorithm verify the validity of the proposed algorithm for the problem of space unmanned system information collection mission planning.

AB - In this paper, the task planning problem of multi-mission location information collection for space unmanned system with long-range detection sensors is considered and modeled as a NP hard combinatorial optimization problem. An algorithm for solving planning problems based on self-organizing neural network is proposed to construct a zero-distance information collection path. On this basis, the effective detection areas of space unmanned system at each mission point are sampled. Four search operators are designed and the appropriate detection points are searched by local search algorithm. In addition, the mission scenario is constructed by random initial multi-mission sites. The simulation and analysis of the algorithm verify the validity of the proposed algorithm for the problem of space unmanned system information collection mission planning.

KW - information collecting

KW - local search

KW - mission planning

KW - self-organizing neural network

KW - space unmanned system

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Zheng H , Guo J , Bai C. Self-Organizing Neural Network based Mission Planning for Space Unmanned System. In Proceedings of the 2019 IEEE International Conference on Unmanned Systems, ICUS 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 114-118. 8996054. (Proceedings of the 2019 IEEE International Conference on Unmanned Systems, ICUS 2019). doi: 10.1109/ICUS48101.2019.8996054

Self-Organizing Neural Network based Mission Planning for Space Unmanned System

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