模糊逻辑控制理论与目标跟踪算法相结合的应用在无人驾驶飞行器目标跟踪目标中的应用
Cong Li1, Wenyi Zhao2, Liuxue Zhao3
1State Grid Beijing Economics Research Institute, Beijing, 100055, China.
Scientific reports
|August 9, 2024
概括
这项研究提高了无人机 (UAV) 目标跟踪使用模糊逻辑控制和边缘计算. 新系统显著减少了捕获时间和跟踪错误,在动态环境中展示了卓越的稳定性.
科学领域:
- 机器人和控制系统 机器人和控制系统
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 传统的无人飞行器 (UAV) 目标追踪方法经常与动态环境和实时处理需求作斗争.
- 现有系统在计算效率和适应不可预测的目标移动和环境变化的能力方面面临限制.
研究的目的:
- 通过开发先进的控制模型和边缘计算框架来增强无人机的目标跟踪能力.
- 为了提高基于无人机的目标追踪应用程序的实时性能,准确性和稳定性.
主要方法:
- 实施模糊逻辑控制模型,根据目标运动和环境条件动态调整无人机飞行态度.
- 开发一个边缘计算框架,将目标识别和位置预测计算从无人机的中央单元下载到边缘节点.
- 利用视觉变压器模型进行实时图像分析和粒子过算法进行高精度目标位置估计.
主要成果:
- 与传统的PID方法相比,基于模糊逻辑控制的算法将平均目标捕获时间减少了20% (从5.2秒到4.2秒).
- 与传统的PID相比,平均追踪误差减少了15% (从0.8m降至0.68m).
- 拟议的算法证明了增强的稳定性,跟踪错误波动是传统PID方法的一半,特别是在环境和目标运动变化下.
结论:
- 模糊逻辑控制理论有效应用于无人机目标跟踪,显著提高系统性能.
- 边缘计算和先进的人工智能模型 (如Vision Transformer) 的集成可以提高实时目标识别和预测准确度.
- 开发的框架为UAV在复杂和动态场景中的目标跟踪提供了强大的和高效的解决方案.
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