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UAV编队控制算法实现

本项目是对论文[Flocking for Multi-Agent Dynamic Systems: Algorithms and Theory]中提出的多智能体编队控制算法的实现。

本项目基于MSN-Flocking-Formation-Control，并进行了一系列改进和优化。

效果展示

编队过程

轨迹图

项目改进

相比原始项目，本实现做了以下改进：

1. 可视化增强：

   • 统一了图形尺寸为正方形，提高了可视化效果
   • 添加了字体大小设置，使图表更加清晰
   • 改进了图例显示和标签设置

2. 轨迹跟踪优化：

   • 添加了轨迹图生成功能，可以观察UAV从起点到当前位置的完整移动路径
   • 实现了动态绘图范围计算，确保所有UAV和目标点都在视图内

3. 障碍物避障增强：

   • 优化了障碍物避障算法的实现
   • 改进了障碍物的可视化表示

4. 代码结构优化：

   • 模块化设计，将不同功能分离为独立函数
   • 添加了详细的注释，提高代码可读性

开始使用

环境要求

• Python 3.7+
• NumPy
• Matplotlib
• PIL (用于图像拼接)

安装依赖

pip install numpy matplotlib pillow
python Flocking_Formation_Control.py

项目参数

• UAV节点数量: N = 50
• 空间维度: M = 2
• 节点间期望距离: D = 15
• 缩放因子: K = 1.2
• 交互范围 R = K*D
• 时间步长: DELTA_T = 0.009
• 迭代次数: ITERATION = 1000

变量命名

为了便于理解变量命名与论文中的公式保持一致。例如：

• C1_ALPHA 对应论文中的 c₁ᵅ
• phi_alpha 对应论文中的 φᵅ
• 等等

功能说明

本项目实现了带有障碍物避障的UAV编队控制算法主要功能包括：

1. 编队形成：

   • 随机初始化UAV位置
   • 实现基于人工势场的编队控制

2. 目标点跟踪：

   • UAV群体可以跟踪指定的目标点

3. 障碍物避障：

   • 实现了基于β-代理的障碍物避障算法
   • 支持圆形障碍物

4. 可视化与分析：

   • 生成编队快照
   • 绘制UAV轨迹图
   • 分析速度变化
   • 监测系统连通性
   • 跟踪质心轨迹

输出结果

运行程序后将在snapshots目录下生成以下图像：

1. 编队快照：记录不同时刻的UAV编队状态
2. 轨迹图：展示UAV的移动轨迹
3. 速度图：分析UAV速度随时间的变化
4. 连通性图：展示系统连通性随时间的变化
5. 质心轨迹图：展示整个系统质心的移动轨迹

辅助工具

项目包含两个辅助工具用于图像处理：

1. create_montage.py：将多个快照拼接成一个大图
2. create_montage2.py：更灵活的图像拼接工具，支持指定图像前缀

致谢

本项目基于MSN-Flocking-Formation-Control，感谢原作者的工作。

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🇨🇳 中文文档 | 🇺🇸 English

UAV Flocking Formation Control Algorithm

This project implements the multi-agent flocking formation control algorithm proposed in the paper [Flocking for Multi-Agent Dynamic Systems: Algorithms and Theory].

This project is based on MSN-Flocking-Formation-Control with a series of improvements and optimizations.

Results Display

Formation Process

Trajectory Plot

Improvements

Compared to the original project, this implementation has made the following improvements:

1. Enhanced Visualization:

   • Unified figure size to square format for better visual effect
   • Added font size settings for clearer charts
   • Improved legend display and label settings

2. Trajectory Tracking Optimization:

   • Added trajectory plotting functionality to observe the complete movement path of UAVs from start to current position
   • Implemented dynamic plotting range calculation to ensure all UAVs and target points are within view

3. Obstacle Avoidance Enhancement:

   • Optimized the implementation of obstacle avoidance algorithm
   • Improved the visual representation of obstacles

4. Code Structure Optimization:

   • Modular design, separating different functionalities into independent functions
   • Added detailed comments to improve code readability

Getting Started

Requirements

• Python 3.7+
• NumPy
• Matplotlib
• PIL (for image montage)

Installation

pip install numpy matplotlib pillow
python Flocking_Formation_Control.py

Project Parameters

• Number of UAV nodes: N = 50
• Space dimensions: M = 2
• Desired distance between nodes: D = 15
• Scaling factor: K = 1.2
• Interaction range: R = K*D
• Time step: DELTA_T = 0.009
• Number of iterations: ITERATION = 1000

Variable Naming

For easier understanding, variable names are consistent with the formulas in the paper. For example:

• C1_ALPHA corresponds to c₁ᵅ in the paper
• phi_alpha corresponds to φᵅ in the paper
• etc.

Features

This project implements UAV flocking formation control with obstacle avoidance. Main features include:

1. Formation Control:

   • Random initialization of UAV positions
   • Implementation of artificial potential field-based formation control

2. Target Tracking:

   • UAV swarm can track specified target points

3. Obstacle Avoidance:

   • Implementation of β-agent based obstacle avoidance algorithm
   • Support for circular obstacles

4. Visualization and Analysis:

   • Generation of formation snapshots
   • Plotting of UAV trajectories
   • Analysis of velocity changes
   • Monitoring of system connectivity
   • Tracking of center of mass trajectory

Output Results

After running the program, the following images will be generated in the snapshots directory:

1. Formation snapshots: Recording UAV formation states at different times
2. Trajectory plots: Showing UAV movement paths
3. Velocity plots: Analyzing UAV velocity changes over time
4. Connectivity plots: Showing system connectivity changes over time
5. Center of mass trajectory plot: Showing the movement trajectory of the entire system's center of mass

Utility Tools

The project includes two utility tools for image processing:

1. create_montage.py: Combines multiple snapshots into one large image
2. create_montage2.py: More flexible image montage tool, supporting specified image prefixes

Acknowledgements

This project is based on MSN-Flocking-Formation-Control. Thanks to the original author for their work.