As an example of drone formation transformation discussed in the previous article, the following code demonstrates the application of Hungarian Algorithm to determine the position allocation in formation transformation.  

#! /usr/bin/python 

# pip install hungarian-algorithm 
 

from hungarian_algorithm import algorithm 
import numpy as np 
 
# Source: drones in a 3×3 grid on Z=0 plane 
source_positions = [ 
    (x, y, 0) 
    for y in range(3) 
    for x in range(3) 
] 
 
# Target: drones in a single horizontal line (linear flight path), spaced 10 units apart 
target_positions = [ 
    (i * 10, 0, 0) for i in range(9) 
] 
 
# Compute cost matrix (Euclidean distance) 
cost_matrix = [ 
    [ 
        np.linalg.norm(np.array(src) - np.array(dst)) 
        for dst in target_positions 
    ] 
    for src in source_positions 
] 
 
# Run Hungarian Algorithm to get minimum-cost assignment 
assignment = algorithm.find_matching(cost_matrix, matching_type='min') 
 
# Report matched pairs 
for src_idx, dst_idx in enumerate(assignment): 
    print(f"Drone {src_idx} → Target Position {dst_idx}: {target_positions[dst_idx]}") 

The above does not take velocity and heading into consideration but that can be adjusted as per trajectory.

#Codingexercise: https://1drv.ms/w/c/d609fb70e39b65c8/EYzGgu5Fc4dEoCUHWQYxMbUBSfvC36iKh8ESBaLtozvdqA?e=VfWGog