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

Hangkong bingqi

Chen Yu, Zhang Gongping, Song Tao, Wen Xinling, Ma Zhengxiang, Liu Zhaoyu, Qin Yuxin¹ 

[1] 1. Zhengzhou Institute of Aeronautics, Zhengzhou 450046, China;

关键词: |air-to-surface weapon|collaborative optimization|task assignment|genetic algorithm|sparse a * algorithm;   

DOI  :  10.12132/ISSN.1673-5048.2020.0020

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown