Hierarchical path planning for low-altitude logistics UAVs in complex urban environments: an enhanced weighted A*...

Autonomous logistics UAVs operating in low-altitude urban airspace face a fundamentally distinct set of challenges from those in high-altitude flight-extreme obstacle density, regulatory geo-fencing, and unpredictable urban canyon wind shear demand path planners that are simultaneously fast, kinematically smooth, and safety-aware.

Existing grid-based planners (e.g., standard A) generate geometrically suboptimal paths cluttered with sharp 45°/90° turns that waste energy and increase turning workload, while bio-inspired and sampling-based alternatives suffer from stochastic execution times and high hyperparameter sensitivity that preclude deterministic delivery scheduling. Critically, no prior deterministic planner simultaneously minimizes path length, cumulative turning angle, and obstacle proximity risk within a single unified cost function actually enforced during search. Methodology: We propose a hierarchical "Search-and-Smooth" framework comprising: (1) an Enhanced Weighted A algorithm whose edge-cost function directly encodes all three objectives-path length (Jlen), turning cost (Jturn), and spatial risk (Jrisk) - and whose inflated heuristic (w = 1.5) provides a provable ε-suboptimality bound while reducing node expansions; and (2) a greedy Line-of-Sight (LOS) post-processor that removes residual grid-discretization artifacts.

Monte Carlo simulations (N = 50 randomized urban scenarios, obstacle density ρobs = 20\%) demonstrate statistically significant improvements over standard A: computation time reduced by 42.8% ( $p<0.001$ , Wilcoxon signed-rank $W=1275$ ), cumulative turning angle reduced by 69.9% ( $p<0.001$ ; $W=1275$ ), and path length maintained within 1.8% of the standard A baseline despite the sub-optimal heuristic inflation. Comparisons against Theta* and a representative PSO baseline confirm the competitive advantages of the proposed method for deterministic, low-latency logistics deployment within the tested synthetic simulation conditions.