基于动态划分-均衡任务模型的无人机任务规划研究
Research on UAV mission planning based on dynamic partitioning-load balancing task model
摘要
针对执行海上侦察任务的多无人机任务规划问题,提出一种基于动态划分区域思想的均衡任务模型.基于海上侦察资源分配与任务调度复杂性高的特点,构建动态划分区域模型,结合蚁群算法提出一种考虑兵力优化的无人机任务规划方案,减少了所需无人机的数量;为解决无人机侦察任务负载不均衡的问题,提出一种均衡任务模型,对各无人机的任务量进行了均衡分配.仿真结果表明,通过动态划分区域有效减少了无人机数量、缩短了路径长度.进一步结合均衡模型后,总侦察耗时相比均衡任务前缩短了15％,且显著缩小了不同无人机任务量之间的差异.
Aiming at the multi-UAV mission planning problem for maritime reconnaissance missions,an balanced task model based on the idea of dynamically partitioning zones is proposed.Firstly,considering the high complexity of resource allocation and task scheduling in maritime reconnaissance,a dynamically partitioned zone model is constructed.Combined with the ant colony algorithm,a UAV mission planning scheme that considers force optimization is proposed,reducing the required number of UAVs.Secondly,to address the issue of unbalanced task loads among UAVs,a balanced task model is introduced to ensure an equitable distribution of tasks among the UAVs.Simulation results show that dynamically partitioning zones effectively reduces the number of UAVs and shortens the path length.Furthermore,with the integration of the balanced model,the total reconnaissance time is shortened by 15％compared to the pre-balanced task approach,and the disparity in task loads among different UAVs is significantly reduced.
年，卷(期)：
2026, (2)
分类号：
V279
机标分类号：
TP391.41
TN925.93
V279
在线出版日期：
2026年5月28日
基金项目：
2008085QF332:安徽省自然基金项目PFXY230101003:陆军炮兵防空兵学院自主项目，
