仿鹰隼狩猎的固定翼无人机近地目标优化导引

When performing tasks such as reconnaissance, detection, and grasping, unmanned aerial vehicles (UAVs) are typically required to approach near-ground or ground-based targets with efficient descents from high altitude under safety constraints, and subsequent altitude recovery after task completion. Although fixed-wing UAVs have advantages, including high speed and long endurance, the existing guidance algorithms are only effective for simple curve or straight-line guidance tasks, issues such as large-scale maneuvers or inefficient guidance often arise during the process of switching guidance. Inspired by raptors' predatory trajectory mechanisms, an optimization framework that integrates energy and maneuver constraints is developed to generate composite optimized trajectories for fixed-wing UAVs, emulating the ground-target hunting patterns of raptors, and improve the efficiency of switching task guidance. Meanwhile, to solve the optimization problem, the foraging strategies of Harris hawks are integrated with pigeon-inspired optimization algorithms to develop a Harris hawks-improved pigeon-inspired optimization algorithm. Based on the optimized trajectory, a vector field-based guidance method is designed for fixed-wing UAVs according to actual flight positions, in which collision-avoidance and attraction components are incorporated into the fundamental vector field framework. In contrast to conventional guidance methods, the proposed method features a collision-avoidance design, and the convergence of the algorithm is theoretically analyzed and proven. Simulation experiments are conducted to compare the proposed algorithms with existing methods, and the results show that the proposed method achieves superior convergence speed and trajectory accuracy compared to conventional algorithms, thereby validating its effectiveness in guiding fixed-wing UAVs during near-ground target operations.