Implementation of Modified Ant-Colony Optimization Algorithm using Crazyflie Nano Quadcopters

Multi-UAV systems have been widely used in various fields, including agriculture. Effective coordination and task allocation are essential components of these systems. This study focuses on optimizing the coordination of multi-UAVs by adapting Modified Ant Colony Optimization (M-ACO) and K-Means Algorithms. Prior to implementing these algorithms, it is necessary to configure a multi/swarm-UAV system. In this study, we have configured the Crazyflie UAVs and Crazyswarm software for a laboratory experiment. Subsequently, the configured swarm-UAV system undergoes testing using the M-ACO algorithm, and the results are compared with the actual positions in real experiments. The experiments revealed that the average position errors for the UAVs are as follows: -0.084 ± 0.033 cm for the x-axis, -0.251 ± 0.2 cm for the y-axis, and 0.01 ± 0.09 cm for the z-axis. These results indicate that, in general, the UAVs can achieve their target positions with a relatively low margin of error.