Ground-based radar monitoring of low Earth orbit (LEO) satellites is constrained by terrain occlusion and short visibility windows. Deploying UAVs as mobile platforms can extend coverage, but beam alignment, obstacle avoidance, and kinematic limits have not been jointly addressed. This paper proposes a UAV path planning framework that treats the ground station–UAV–satellite (G-U-S) collinear relationship as a planning prior. Ideal UAV positions are first computed from satellite visible arcs. In obstacle-free airspace, these positions directly define the flight path. When no-fly zones intersect the ideal trajectory, a geometry-guided RRT* planner generates collision-free detours by favoring sampling near the beam axis. The beam safety corridor is then embedded with kinematic limits as a conservative hard constraint within a Minimum Snap Bézier QP model, whose convex hull property bounds the trajectory within the coverage region. Simulations over a representative visible arc of the target satellite (approximately 3.3 min) achieve 100% beam coverage in the obstacle-free scenario and 95.2% with three no-fly zones placed along the ideal trajectory. Visible arc duration varies with satellite orbital trajectory; the framework is applicable to any arc whose geometry satisfies the kinematic constraints of the UAV platform. An ablation experiment confirms that the geometry-guided sampling strategy is essential: replacing it with uniform random sampling reduces coverage by 9.5 percentage points and triggers a diagnostic fallback mechanism. Edge case stress testing further identifies the conditions under which beam containment and obstacle avoidance come into tension. The proposed framework provides a simulation-validated trajectory planning approach for UAV-assisted LEO satellite monitoring in complex airspace.
Li et al. (Wed,) studied this question.