在一个随时间变化的通信网络下,在一群无人机上进行定向同步的最佳k-最近邻居
Rigoberto Martínez-Clark1, Javier Pliego-Jimenez2, Juan Francisco Flores-Resendiz1
1Faculty of Engineering, Administrative, and Social Sciences, Autonomous University of Baja California, Tecate 21460, BC, Mexico.
Entropy (Basel, Switzerland)
|June 28, 2023
概括
这项研究通过分析最佳邻里大小来优化无人机 (UAV) 群体的方向同步. 研究结果表明,一个简单的控制算法可以有效地实现群体行为,减少群体机器人的计算负载.
科学领域:
- 机器人和控制系统 机器人和控制系统
- 群集情报 群集情报 群集情报
- 计算动力学是一种计算动力学.
背景情况:
- 定位同步对于集体行为至关重要,比如无人机 (UAV) 群集,使通用导航成为可能.
- 以自然群体为灵感的k-最近邻近算法,由于动态通信网络,对于大型无人机群体来说,计算成本昂贵.
研究的目的:
- 进行统计分析,以确定最佳的邻里大小,以在无人机中进行标点同步,在无人机群中最多有100个代理.
- 通过识别群体机器人应用程序的高效社区大小,减少能力有限的无人机的计算负载.
主要方法:
- 在模拟无人机群体中,对邻里大小对航线同步的影响进行统计分析.
- 评估两种方法:在100个无人机群中保持最佳邻居百分比,并在各种群体大小 (高达100个无人机) 中保持7个邻居.
主要成果:
- 模拟结果和统计分析表明,减少邻里大小可以有效地实现方向同步.
- 这些发现支持一种简单的P类控制算法的有效性,用于模仿自然的群体行为,类似于鸟群.
结论:
- 对于无人机群体来说,可以确定一个最佳的社区大小,以有效地实现方向同步.
- 这项研究有助于为群体机器人开发计算上更轻的控制算法,提高资源有限的环境中的可扩展性和性能.
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