Research on trajectory optimization for morphing unmanned aerial-underwater vehicles based on improved grey wolf optimizer

To address the strong nonlinearity and dynamic coupling in the trans-media maneuver process for morphing unmanned aerial-underwater vehicles, a trajectory optimization method based on an improved Grey Wolf Optimizer (IGWO) is presented. First, the optimal control model was formulated and transformed. Subsequently, the cubic chaotic operator and opposition-based learning method were utilized to process the initial population. Control parameters ɑ were redesigned using cosine and cubic functions to balance global exploration and local exploitation. The guiding weight of elite wolves was adaptively adjusted based on fitness values, increasing the influence of elite wolves on the population. Additionally, a scout method was proposed to address the blind-following behavior. The performance of the IGWO was validated on 11 benchmark functions and compared with four other algorithms. Finally, the IGWO was applied to solve the optimal trajectory, the distance-optimal trajectories and motion parameters were calculated, and the effects of initial speed and altitude on the optimal trajectory were analyzed. Simulation results demonstrate that the IGWO significantly improves the efficiency and accuracy of solving complex trajectory optimization problems. The method can rapidly generate optimal trajectories that satisfy specified constraints and safety requirements, thus validating the effectiveness and applicability.