GA-Optimized Consensus Control and Formation Transition for Multi-UAV Systems

Multi-Unmanned Aerial vehicle (Multi-UAV) formations require flexible formation transition capabilities to adapt to dynamic environments and evolving mission requirements during complex operations. This paper proposes a formation structure model based on a virtual leader and consensus control, achieving target tracking, obstacle avoidance, and inter-UAV coordination through a multi-level behavioral control framework. To address the limitations of traditional formation control methods-such as empirical parameter dependency and low convergence efficiency-a multi-objective fitness function is constructed. This function simultaneously optimizes consensus convergence performance, multi-behavior coordination, and formation transition efficiency, utilizing a Genetic Algorithm (GA) for global optimization. Furthermore, an adaptive transition mechanism between V-formation and linear formation is designed, enabling the formation to dynamically reconfigure based on the distribution of environmental obstacles. Simulation results demonstrate that in sparse obstacle environments, the GA-optimized algorithm improves consensus convergence efficiency by 30.8% and reduces transition time by 22.7%. In continuously constrained environments, consensus convergence efficiency is enhanced by 25.6%, with transition time shortened by 29.6%. The results confirm that the proposed method improves both convergence speed and formation transition efficiency for multi-UAV formations in complex environments, validating the superiority of the joint optimization strategy.