An Improved Flocking Model for UAVs in Constrained Environments
Xiaoyu Zhang, Yutong Yuan, Fan Zhang · 2022
The research of UAV flocking is getting more and more popular these days. Some flocking model is established on the commonly used environment and complex environment with narrow paths and broad barriers will reduce the performance of the model. In this paper, we design a complex environment which includes narrow paths and broad barriers. We propose a mission that UAVs move at the start point and move into the expected area after crossing barriers and walls. We propose a evaluation function which contains the mission execution time and collisions. Then we conducted a simulation experiment, using a single-objective genetic algorithm to optimize the parameters of the model. We proof that the performance of the optimized model is superior to the performance of the original model and we get better mission execution time and the number of collisions.