ArticleFrontiers in robotics and AI2026
Hierarchical path planning for low-altitude logistics UAVs in complex urban environments: an enhanced weighted A* approach with geometric smoothing.
Article in Frontiers in robotics and AI, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Importance: 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. Gap: 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 ( Results: Monte Carlo simulations (
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