关键词: 巡飞弹, 航迹规划, 优化算法, 最优成本

Abstract:

In view of the problem of obstacle avoidance and cost in loitering munition trajectory planning within complex three-dimensional environments, an improved red kite optimization algorithm （IROA） based on multidimensional cooperative guidance is proposed. The algorithm integrates Latin hypercube sampling, a hierarchical guidance strategy, and nonlinear adaptive parameter adjustment, combined with Lévy flight perturbation and a forced elite retention mechanism, thereby effectively enhancing both its global exploration and local exploitation capabilities. Benchmark function tests demonstrate that the proposed algorithm significantly outperforms the traditional red kite optimization algorithm （ROA） and several advanced metaheuristics in optimization performance. For the loitering munition trajectory planning problem, a multi-factor constrained flight cost function is designed, and a corresponding trajectory planning model is constructed. By incorporating three-dimensional terrain data in a simulated threat environment, the trajectory planning experiment results show that the IROA-based planning scheme can rapidly converge to the optimal trajectory, with a markedly lower flight cost function fitness compared with the traditional ROA. These results verify the feasibility and engineering applicability of the proposed algorithm for loitering munition trajectory planning.

Key words: loitering munition, trajectory planning, optimization algorithm, optimal cost