Resilient multi-objective mission planning for UAV formation: A unified framework integrating task pre- and re-assignment

IF 5.1 2区 工程技术 Q1 Engineering Defence Technology Pub Date : 2024-08-13 DOI:10.1016/j.dt.2024.08.002
Xinwei Wang, Xiaohua Gao, Lei Wang, Xichao Su, Junhong Jin, Xuanbo Liu, Zhilong Deng
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Abstract

Combat effectiveness of unmanned aerial vehicle (UAV) formations can be severely affected by the mission execution reliability. During the practical execution phase, there are inevitable risks where UAVs being destroyed or targets failed to be executed. To improve the mission reliability, a resilient mission planning framework integrates task pre- and re-assignment modules is developed in this paper. In the task pre-assignment phase, to guarantee the mission reliability, probability constraints regarding the minimum mission success rate are imposed to establish a multi-objective optimization model. And an improved genetic algorithm with the multi-population mechanism and specifically designed evolutionary operators is used for efficient solution. As in the task-reassignment phase, possible trigger events are first analyzed. A real-time contract net protocol-based algorithm is then proposed to address the corresponding emergency scenario. And the dual objective used in the former phase is adapted into a single objective to keep a consistent combat intention. Three cases of different scales demonstrate that the two modules cooperate well with each other. On the one hand, the pre-assignment module can generate high-reliability mission schedules as an elaborate mathematical model is introduced. On the other hand, the re-assignment module can efficiently respond to various emergencies and adjust the original schedule within a millisecond. The corresponding animation is accessible at bilibili.com/video/BV12t421w7EE for better illustration.
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无人机编队的弹性多目标任务规划:整合任务预分配和重新分配的统一框架
任务执行的可靠性会严重影响无人机编队的战斗力。在实际执行阶段,不可避免地存在无人机被摧毁或目标执行失败的风险。为了提高任务的可靠性,本文开发了一种集成了任务预分配和重新分配模块的弹性任务规划框架。在任务预分配阶段,为保证任务的可靠性,对任务最小成功率施加概率约束,建立多目标优化模型。并采用改进的遗传算法,通过多群体机制和专门设计的进化算子来实现高效求解。与任务重新分配阶段一样,首先要分析可能的触发事件。然后提出一种基于实时契约网协议的算法来解决相应的紧急场景。并将前一阶段使用的双目标调整为单一目标,以保持作战意图的一致性。三个不同规模的案例表明,这两个模块之间配合默契。一方面,由于引入了精细的数学模型,预分配模块可以生成高可靠性的任务时间表。另一方面,重新分配模块可以有效地应对各种紧急情况,并在毫秒级的时间内调整原计划。为了更好地说明问题,可在 bilibili.com/video/BV12t421w7EE 上观看相应的动画。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Defence Technology
Defence Technology Engineering-Computational Mechanics
CiteScore
7.50
自引率
7.80%
发文量
1248
审稿时长
22 weeks
期刊介绍: Defence Technology, sponsored by China Ordnance Society, is published quarterly and aims to become one of the well-known comprehensive journals in the world, which reports on the breakthroughs in defence technology by building up an international academic exchange platform for the defence technology related research. It publishes original research papers having direct bearing on defence, with a balanced coverage on analytical, experimental, numerical simulation and applied investigations. It covers various disciplines of science, technology and engineering.
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