使用动态无人机集群进行高效的多目标定位
Wei Gong1,2, Shuhan Lou1, Liyuan Deng1
1Department of Control Science and Engineering, Tongji University, Shanghai 201804, China.
Sensors (Basel, Switzerland)
|May 14, 2025
概括
本研究介绍了一种动态无人机集群模型,以提高3D环境中的多目标定位精度. 这种新的算法提高了协作本地化性能,特别是在复杂的动态场景中.
科学领域:
- 机器人和控制系统 机器人和控制系统
- 无线通信和网络无线通信和网络.
- 优化算法 优化算法
背景情况:
- 使用无人机 (UAV) 在复杂的3D环境中准确地定位多目标,由于动态环境和有限的资源,这是一个挑战.
- 现有的协作本地化方法经常与移动意识的集群形成和处理测量/运动不确定性作斗争.
研究的目的:
- 提出一个动态无人机集群模型,以提高复杂3D环境中的多目标定位精度.
- 开发一个强大的算法,用于移动意识的集群形成,以改善协作本地化.
- 通过克拉梅尔-拉奥下界 (CRLB) 分析局部化性能,考虑测量和运动诱导的不确定性.
主要方法:
- 一个动态的无人机集群模型,集成移动意识的集群形成,以提高协作本地化准确性.
- 在不确定性下进行性能分析的克拉梅尔-拉奥下限 (CRLB) 的推导.
- 用自适应模拟化 (MDQPSO-ASA) 算法开发多群体离散量子灵感粒子群体优化,包括对约束的修复机制.
主要成果:
- 与基线方法相比,MDQPSO-ASA算法显示出更高的本地化准确性.
- 拟议的模型显示了增强的计算效率和适应不同无人机和目标尺度的适应性.
- 模拟结果验证了移动意识集群方法的有效性.
结论:
- 动态无人机集群模型有效地提高了复杂的3D环境中的多目标定位精度.
- MDQPSO-ASA算法为资源受限的协作本地化提供了一个高效和可适应的解决方案.
- 这项工作为现实世界无人机基于本地化应用提供了实用方法.
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