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This paper introduces a risk-aware trajectory optimization framework for UAVs in urban environments that incorporates a Target Level of Safety (TLS) constraint. They use an A* search algorithm with a risk-weighted cost function that considers population density, traffic, sheltering, UAV parameters, and wind conditions to generate flight paths. The approach is validated through simulations and a real-world case study, achieving a significant reduction in ground risk while maintaining TLS compliance and minimal detour.
Achieve a 72% reduction in UAV ground risk with only a 6% increase in flight path length by intelligently routing drones through urban environments.
Integrating Unmanned Aerial Vehicles (UAVs) into urban airspace requires a risk-aware approach to strategic flight planning and trajectory optimization, particularly for beyond-visual-line-of-sight operations. Existing regulatory frameworks impose strict restrictions and lack dynamic, trajectory-based risk assessments. This study presents a methodology to compute efficient UAV flight paths that comply with a predefined Target Level of Safety (TLS) for ground risk. An A* algorithm with an adaptive, risk-weighted cost function optimizes trajectories by balancing flight efficiency and ground risk exposure. The risk model incorporates key urban factors, including population exposure, road-traffic density and flow, sheltering effects, UAV-specific parameters, and wind conditions. The approach is validated through a large-scale simulation study using synthetic urban environments, systematically analyzing TLS compliance and the impact of UAV parameters on optimal trajectories. In a real-world case study using open urban GIS data, the method achieved a 72.2% reduction in induced ground risk compared to the direct path, while increasing the detour factor only to 1.06 and maintaining full TLS compliance, demonstrating its practical relevance for strategic, risk-aware UAV flight planning.
