在无人机群中,可解释的机器学习模型可以解释无人机群中的混乱
Marta Gackowska-Kątek1, Piotr Cofta2
1Faculty of Telecommunications, Computer Science and Electrical Engineering, Bydgoszcz University of Science and Technology, Al. prof. S. Kaliskiego 7, 85-796, Bydgoszcz, Poland. marta.gackowska-katek@pbs.edu.pl.
Scientific reports
|September 28, 2024
概括
管理无人机舰队需要稳定的组成,特别是在入侵期间. 这项研究开发了一个基于形成参数预测干扰的模型,CatBoost机器学习优于其他模型.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
- 控制系统 控制系统
背景情况:
- 管理无人驾驶飞行器 (UAV) 机队在维持阵营稳定性和最大限度地减少混乱方面存在挑战,特别是在入侵期间.
- 任务规划需要平衡运营需求与潜在的入侵者引起的混乱.
研究的目的:
- 开发一个预测模型,以预测无人机阵列中的干扰,作为阵列参数的函数.
- 将六种机器学习方法的有效性与预测形成干扰的基线进行比较.
- 为无人机运营商提供使用可解释性方法的一般性建议.
主要方法:
- 模拟数据被用来训练和评估六种机器学习方法.
- 每种方法的性能与先前确定的基线进行了比较.
- 为了模型的可解释性,使用了SHAP (沙普利增量解释).
主要成果:
- CatBoost (分类提升) 证明了最高的有效性,达到83.3%的确定系数 ().
- 这代表了比基线方法有80%的改进.
- SHAP方法使模型的洞察力从特定预测扩展到一般运营商建议.
结论:
- 开发的预测模型,特别是使用CatBoost,显著改善了无人机形成稳定性对抗干扰的管理.
- 通过SHAP的可解释性为操作人员提供了可操作的见解,以优化任务成功的形成参数.
- 这项研究为提高自主无人机机队运营的弹性和效率提供了一个强大的框架.
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