This is an extension of use cases for a drone management software platform in the cloud. The DFCS

Autonomous drone fleet movement has been discussed with the help of self-organizing maps as implemented in https://github.com/raja0034/som4drones but sometimes the fleet movement must be controlled precisely especially at high velocity and large number or high density when the potential for congestion or collision is high. For example, when the velocity is high, drones and missiles might fly similarly. A centralized algorithm to control their movement for safe arrival of all units might make it more convenient and cheaper for the units if there is continuous connectivity during flight. One such algorithm is the virtual structure method.

 The virtual structure method for UAV swarm movement is a control strategy where the swarm is organized into a predefined formation, often resembling a geometric shape. Instead of relying on a single leader UAV, the swarm is controlled as if it were a single rigid body or virtual structure. Each UAV maintains its position relative to this virtual structure, ensuring cohesive and coordinated movement. The steps include:

Virtual Structure Definition: A virtual structure, such as a line, triangle, or more complex shape, is defined. This structure acts as a reference for the UAVs' positions.

Relative Positioning: Each UAV maintains its position relative to the virtual structure, rather than following a specific leader UAV. This means that if one UAV moves, the others adjust their positions to maintain the formation.

Coordination and Control: The UAVs use local communication and control algorithms to ensure they stay in their designated positions within the virtual structure. This can involve adjusting speed, direction, and altitude based on the positions of neighboring UAVs.

Fault Tolerance: Since the control does not rely on a single leader, the swarm can be more resilient to failures. If one UAV fails, the others can still maintain the formation.

 A sample implementation where each UAV follows a leader to maintain a formation might appear as follows:

import numpy as np

class UAV:

    def __init__(self, position):

        self.position = np.array(position)

class Swarm:

    def __init__(self, num_uavs, leader_position, formation_pattern):

        self.leader = UAV(leader_position)

        self.uavs = [UAV(leader_position + offset) for offset in formation_pattern]

    def update_leader_position(self, new_position):

        self.leader.position = np.array(new_position)

        self.update_uav_positions()

    def update_uav_positions(self):

        for i, uav in enumerate(self.uavs):

            uav.position = self.leader.position + formation_pattern[i]

    def get_positions(self):

        return [uav.position for uav in self.uavs]

# Example usage

num_uavs = 5

leader_position = [0, 0, 0] # Initial leader position

formation_pattern = [np.array([i*2, 0, 0]) for i in range(num_uavs)] # Line formation

swarm = Swarm(num_uavs, leader_position, formation_pattern)

# Update leader position

new_leader_position = [5, 5, 5]

swarm.update_leader_position(new_leader_position)

# Get updated positions of all UAVs

positions = swarm.get_positions()

print("Updated UAV positions:")

for pos in positions:

    print(pos)