多无人机集群编队飞行（8架无人机）

# MSN Flocking Formation Control-Python Version This is a Python implementation of "Flocking for multi-agent dynamic systems: Algorithms and theory" by Olfati-Saber, Reza. You can find the article using these links: [IEEE](https://ieeexplore.ieee.org/abstract/document/1605401), [PDF](https://sci-hub.yncjkj.com/10.1109/TAC.2005.864190). ## Getting started ```shell $ git clone $ cd $ pip install ``` Run the python files for each of the five cases explained below. ```shell $ python src/msn_1.py ``` ## Project parameters: - Number of sensor nodes: n =100. - Space dimensions: m = 2. - Desired distance among sensor node: d = 15. - Scaling factor: k = 1.2 and interaction range r = k*d. - Epsilon = 0.1 and Delta_t = 0.009 (These two parameters are optional and you can change them). ## Variable names For easier understanding the variables are named similar to the equations described below. For example, $c_{1}^{a}$ is denoted as `C1_ALPHA` (all capital as it's a constant), $p_{i, k}$ is denoted as `p_i_k`, and so on. ## Cases ### Case 1 - MSN Fragmentation **Filename: msn_1.py** - Randomly generates a connected network of 100 nodes in the area of 50x50. - Plots the initial deployment of the MSN of 100 nodes. - Links the neighboring nodes together by a line. #### Plots - Plots the fragmentation of the nodes. - Plots the velocity. - Plots the connectivity. - Plots the trajectory. #### Algorithm 1: <img src='alg_img\alg1.jpg' width=500px> <img src='alg_img\alg11.jpg' width=500px> <img src='alg_img\alg12.jpg' width=500px> <img src='alg_img\alg13.jpg' width=500px> #### Result <img src='alg_img\msn_1.png' width=500px> ### Case 2 - Implements MSN Quasi-Lattice Formation with **static target** **Filename: msn_2.py** - Randomly generates a connected network of 100 nodes in the area of 50x50. - Sets up a target (gamma agent) as static point with its coordinate (x = 150, y =150). - Implements flocking behavior of the MSN. #### Algorithm 2 for Case 2: <img src='alg_img\alg2.jpg' width=500px> #### Plots - Plots the flocking of the nodes. - Plots the velocity. - Plots the connectivity. - Plots the trajectory. #### Result <img src='alg_img\msn_2.png' width=500px> ### Case 3 - Implements MSN Quasi-Lattice Formation with **dynamic target (Sine wave trajectory)** **Filename: msn_3.py** - Randomly generates a connected network of 100 nodes in the area of 150x150. - Sets up a target (gamma agent) moving in a **sine wave trajectory.** - Implements flocking behavior of the MSN. #### Algorithm 2 for Case 3: <img src='alg_img\alg21.jpg' width=500px> #### Plots - Plots the flocking of the nodes following the target (Sine wave trajectory). - Plots the velocity. - Plots the connectivity. - Plots the trajectory. - Plots the center of mass and target trajectory. #### Result <img src='alg_img\msn_3.png' width=500px> ### Case 4 - Implements MSN Quasi-Lattice Formation with **dynamic target (Circular trajectory)** **Filename: msn_4.py** - Randomly generates a connected network of 100 nodes in the area of 150x150. In this case you - Sets up a target (gamma agent) moving in a **circular trajectory.** - Implements flocking behavior of the MSN. #### Algorithm 2 for Case 3 (Same equation, change the values to make it a circular trajectory): <img src='alg_img\alg21.jpg' width=500px> #### Plots - Plots the flocking of the nodes following the target (Circular trajectory). - Plots the velocity. - Plots the connectivity. - Plots the trajectory. - Plots the center of mass and target trajectory. #### Result <img src='alg_img\msn_4.png' width=500px> ### Case 5 - Implements MSN Quasi-Lattice Formation with **obstacle avoidance** **Filename: msn_5.py** - Randomly generates a connected network of 100 nodes in the area of 50x50. - Sets up a target (gamma agent) at the location of (200, 25). - Sets up an obstacle, circular in shape with a radius of 15 and its center location at (100,25). - Implements flocking behavior of the MSN avoiding obstacles. #### Algorithm 3: <img src='alg_img\alg3.jpg' width=500px> <img src='alg_img\alg31.jpg' width=500px> <img src='alg_img\alg32.jpg' width=500px> #### Plots - Plots the flocking of the nodes following the target, avoiding obstacles. - Plots the velocity. - Plots the connectivity. - Plots the trajectory. - Plots the center of mass and target trajectory. #### Result <img src='alg_img\msn_5.png' width=500px> ### Case 6 - Implements MSN Quasi-Lattice Formation **Filename: msn_6.py** The purpose of the algorithm is to provide a common control law to form agents in a triangular shape. **The agent model** is a double integrator: ```math p' = q q' = u ``` ```p: position, q: velocity``` #### Results  --- **Credit**: [paul-shuvo](https://github.com/paul-shuvo/MSN-Flocking-Formation-Control) and [amirhosseinh77](https://github.com/amirhosseinh77/Flocking-Multi-Agent)