Issue |
JNWPU
Volume 39, Number 3, June 2021
|
|
---|---|---|
Page(s) | 617 - 623 | |
DOI | https://doi.org/10.1051/jnwpu/20213930617 | |
Published online | 09 August 2021 |
Multi-UAVs task assignment method considering expected destruction probability of target
考虑目标期望摧毁概率的多无人机任务分配方法
1
Research and Development Institute of Northwestern Polytechnical University in Shenzhen, Shenzhen 518057, China
2
School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
3
School of Geological Engineering and Geomatics, Chang'an University, Xi'an 710054, China
Received:
21
September
2020
To solve the combat task assignment of reconnaissance unmanned aerial vehicle (RUAV)/unmanned combat aerial vehicle(UCAV), this paper proposed an efficient task assignment method that takes into account the expected destruction probability of target. This method improves the utility function and constraint of the model that based on the goal of destroying the total sum of the target value. The adjustment factor is added to the model to achieve a balanced distribution of RUAVs/UCAVs resources; the expected destruction probability of target is introduced as a constraint to prevent the excessive distribution of RUAVs/UCAVs resources. Subsequently, a greedy algorithm based on maximizing marginal-return is designed to solve the proposed model. The simulation results show that the improved algorithm not only meets the combat effectiveness but also improves the economic performance on the basis of real-time task allocation.
摘要
针对侦查无人机(reconnaissance unmanned aerial vehicle,RUAV)/攻击型无人机(unmanned combat aerial vehicle,UCAV)对目标作战的任务分配问题,提出了一种考虑目标期望摧毁概率的高效分配方法。该方法在以摧毁目标价值总和最大为目标的基础上,改进设计了模型的收益函数以及约束条件。模型中加入调节因子实现资源的均衡分配;引入目标期望摧毁概率作为约束条件,防止资源的过度分配。随后,设计了基于边缘受益最大化的贪婪算法对所提模型进行求解。仿真结果表明,改进后的模型算法在实现实时性任务分配的基础上,既满足作战效能又提高了经济效能。
Key words: multiple UAVs / expected destruction probability / task allocating quality / marginal return
关键字 : 多无人机 / 期望摧毁概率 / 任务分配 / 边缘受益
© 2021 Journal of Northwestern Polytechnical University. All rights reserved.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.