Research on Multi-Mission Airborne Weapon Multi-Target Cooperative Optimization Planning Algorithm

In a complex battlefield environment, there may be multiple dynamic threats, how to quickly plan the optimal flight path is the key to the successful execution of the attack mission. Under the cultural algorithm framework, a kind of dynamic flight path planning algorithm used in the multi-mission ai...

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Auteur principal:	Chen Yu, Zhang Gongping, Song Tao, Wen Xinling, Ma Zhengxiang, Liu Zhaoyu, Qin Yuxin
Format:	article
Langue:	ZH
Publié:	Editorial Office of Aero Weaponry 2021
Sujets:	\|air-to-surface weapon\|collaborative optimization\|task assignment\|genetic algorithm\|sparse a * algorithm Motor vehicles. Aeronautics. Astronautics TL1-4050
Accès en ligne:	https://doaj.org/article/bcaf998bc06f46eeb3cb92bcd58046d4
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id	oai:doaj.org-article:bcaf998bc06f46eeb3cb92bcd58046d4
record_format	dspace
spelling	oai:doaj.org-article:bcaf998bc06f46eeb3cb92bcd58046d42021-11-30T00:13:49ZResearch on Multi-Mission Airborne Weapon Multi-Target Cooperative Optimization Planning Algorithm1673-504810.12132/ISSN.1673-5048.2020.0020https://doaj.org/article/bcaf998bc06f46eeb3cb92bcd58046d42021-04-01T00:00:00Zhttps://www.aeroweaponry.avic.com/fileup/1673-5048/PDF/2020-00020.pdfhttps://doaj.org/toc/1673-5048In a complex battlefield environment, there may be multiple dynamic threats, how to quickly plan the optimal flight path is the key to the successful execution of the attack mission. Under the cultural algorithm framework, a kind of dynamic flight path planning algorithm used in the multi-mission airborne weapon multi-target cooperative optimization planning combining sparse A * algorithm with Genetic Algorithm (GA) algorithm is proposed. Based on the cultural algorithm framework, the sparse A * algorithm is used to quickly obtain information of initial flight path and navigation nodes, and the acquired information is sent to the belief space as knowledge for storage, so as to guide the population space to achieve the optimal target task allocation and route acquisition by GA algorithm within the effective range. Simulation results show that the algorithm can effectively avoid threats, reduce the overall flight distance of flight path, and complete the multi-mission airborne weapon multi-target collaborative planning.Chen Yu, Zhang Gongping, Song Tao, Wen Xinling, Ma Zhengxiang, Liu Zhaoyu, Qin YuxinEditorial Office of Aero Weaponryarticle\|air-to-surface weapon\|collaborative optimization\|task assignment\|genetic algorithm\|sparse a * algorithmMotor vehicles. Aeronautics. AstronauticsTL1-4050ZHHangkong bingqi, Vol 28, Iss 2, Pp 62-68 (2021)
institution	DOAJ
collection	DOAJ
language	ZH
topic	\|air-to-surface weapon\|collaborative optimization\|task assignment\|genetic algorithm\|sparse a * algorithm Motor vehicles. Aeronautics. Astronautics TL1-4050
spellingShingle	\|air-to-surface weapon\|collaborative optimization\|task assignment\|genetic algorithm\|sparse a * algorithm Motor vehicles. Aeronautics. Astronautics TL1-4050 Chen Yu, Zhang Gongping, Song Tao, Wen Xinling, Ma Zhengxiang, Liu Zhaoyu, Qin Yuxin Research on Multi-Mission Airborne Weapon Multi-Target Cooperative Optimization Planning Algorithm
description	In a complex battlefield environment, there may be multiple dynamic threats, how to quickly plan the optimal flight path is the key to the successful execution of the attack mission. Under the cultural algorithm framework, a kind of dynamic flight path planning algorithm used in the multi-mission airborne weapon multi-target cooperative optimization planning combining sparse A * algorithm with Genetic Algorithm (GA) algorithm is proposed. Based on the cultural algorithm framework, the sparse A * algorithm is used to quickly obtain information of initial flight path and navigation nodes, and the acquired information is sent to the belief space as knowledge for storage, so as to guide the population space to achieve the optimal target task allocation and route acquisition by GA algorithm within the effective range. Simulation results show that the algorithm can effectively avoid threats, reduce the overall flight distance of flight path, and complete the multi-mission airborne weapon multi-target collaborative planning.
format	article
author	Chen Yu, Zhang Gongping, Song Tao, Wen Xinling, Ma Zhengxiang, Liu Zhaoyu, Qin Yuxin
author_facet	Chen Yu, Zhang Gongping, Song Tao, Wen Xinling, Ma Zhengxiang, Liu Zhaoyu, Qin Yuxin
author_sort	Chen Yu, Zhang Gongping, Song Tao, Wen Xinling, Ma Zhengxiang, Liu Zhaoyu, Qin Yuxin
title	Research on Multi-Mission Airborne Weapon Multi-Target Cooperative Optimization Planning Algorithm
title_short	Research on Multi-Mission Airborne Weapon Multi-Target Cooperative Optimization Planning Algorithm
title_full	Research on Multi-Mission Airborne Weapon Multi-Target Cooperative Optimization Planning Algorithm
title_fullStr	Research on Multi-Mission Airborne Weapon Multi-Target Cooperative Optimization Planning Algorithm
title_full_unstemmed	Research on Multi-Mission Airborne Weapon Multi-Target Cooperative Optimization Planning Algorithm
title_sort	research on multi-mission airborne weapon multi-target cooperative optimization planning algorithm
publisher	Editorial Office of Aero Weaponry
publishDate	2021
url	https://doaj.org/article/bcaf998bc06f46eeb3cb92bcd58046d4
work_keys_str_mv	AT chenyuzhanggongpingsongtaowenxinlingmazhengxiangliuzhaoyuqinyuxin researchonmultimissionairborneweaponmultitargetcooperativeoptimizationplanningalgorithm
_version_	1718406886985826304

Research on Multi-Mission Airborne Weapon Multi-Target Cooperative Optimization Planning Algorithm

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