桥梁直觉和数据:一个统一的贝叶斯框架,以优化无人机飞行器群的性能
Ruiguo Zhong1,2, Zidong Wang3, Hao Wang4
1School of Electronics and Information, Northwestern Polytechnical University, Xi'an 710129, China.
Entropy (Basel, Switzerland)
|September 27, 2025
概括
工程经理现在可以使用新的贝叶斯网络 (BN) 框架优化无人机 (UAV) 群运作. 该工具集成了专家知识和实时数据,以更好地评估绩效和决策.
科学领域:
- 工程管理工程管理工程管理
- 人工智能的人工智能
- 机器人技术 机器人技术 机器人技术
背景情况:
- 低海拔经济生态系统和无人机群应用的快速扩张需要先进的性能评估和运营优化策略.
- 现有的评估方法往往不足以应对无人机群的动态和复杂性质,缺乏有效地整合各种性能标准的能力.
研究的目的:
- 引入一种基于贝叶斯网络 (BN) 的多标准决策框架,用于评估和优化无人机群性能.
- 在无人机群管理中弥合主观专家见解和客观实时数据之间的差距.
主要方法:
- 开发一个贝叶斯网络 (BN) 框架,整合专家直觉和实时数据,用于多标准决策.
- 利用差异分解来建立专家权重和网络概率参数之间的双向映射.
- 通过全面测试进行验证,以评估框架在确定关键绩效驱动因素方面的有效性.
主要成果:
- 拟议的BN框架成功地将专家知识和客观数据集成到一个统一的模型中.
- 该框架有效地确定了无人机群的关键性能驱动因素,例如环境意识,沟通和协作决策.
- 验证证实了框架能够为工程经理提供透明和可操作的见解的能力.
结论:
- 开发的BN框架为监督无人机群系统的工程经理提供了一个透明和适应性的工具.
- 该框架有助于明智地分配资源,采用技术,并提高复杂无人机群的整体运营效率.
- 这种方法为不断增长的无人机群应用所带来的性能评估和优化挑战提供了强大的解决方案.
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