A Hybrid RRT*-DLPSO Method for Low-Altitude UAV Reference-Path Planning in Complex Mountainous Environments

Low-altitude unmanned aerial vehicles operating in mountainous environments require continuous spatial reference paths that satisfy terrain-clearance, altitude, pitch, and curvature constraints. Conventional sampling-based planners can efficiently discover collision-free paths but often produce geometrically irregular solutions, whereas swarm-based optimizers are sensitive to the quality and feasibility of their initial populations. This study proposes a feasibility-first hybrid reference-path planner, termed RRT*-DLPSO, which combines Rapidly Exploring Random Tree Star initialization with a particle swarm optimizer incorporating Differential Evolution and Lévy-flight mechanisms. RRT* first supplies a terrain-aware initial polyline, which is compressed into curvature-selected control points and represented by a cubic spline. Because compression and interpolation can invalidate a collision-free polyline, every reconstructed path is checked using the full set of hard constraints. DLPSO then refines the path using time-varying learning factors, stage-adaptive differential trial generation, and stagnation-triggered Lévy perturbations. Feasible paths are ranked using a weighted objective that considers path length, altitude, pitch demand, and horizontal curvature. Experiments were conducted in three DEM-based mountainous scenarios using 50 independent runs per method. The comparison includes a complete RRT*-PSO ablation chain and an RRT*-STOMP baseline. RRT*-DLPSO achieved a 100% continuously certified feasibility rate and the lowest mean final objective value in all three scenarios with respect to the adopted spline and interpolated DEM model. Relative to the strongest competing method in each scenario, it reduced the mean objective by 6.4%, 5.1%, and 3.9%, respectively. Compared with DLPSO without RRT* initialization, the corresponding reductions were 13.0%, 19.6%, and 32.9%. Paired Wilcoxon tests with Holm correction confirmed statistically significant improvements over all major competing methods. The method also reached the scenario-specific objective thresholds in 86%, 100%, and 96% of the runs. Sensitivity analyses repeated in two DEM scenarios supported nine control points and an RRT* step-size and rewiring-radius coefficient pair of (0.020, 0.040) among the tested settings. These results support RRT*-DLPSO as an effective offline spatial reference-path planner for known static mountainous terrain represented by DEM data.