EA-AHS: A Perception-Driven Adaptive Heuristic Framework for Real-Time UAV Path Planning in Complex Urban Environments

With the progressive opening of urban low-altitude airspace, drone applications increasingly rely on real-time environmental perception for safe navigation in complex, obstacle-dense environments. Traditional planning algorithms struggle to efficiently process spatially heterogeneous sensor data, leading to computational bottlenecks and unsafe trajectories. To bridge raw perception and agile decision-making, we propose an Environment-Aware Adaptive Heuristic Search (EA-AHS) framework that translates local obstacle density into adaptive heuristic weights via a sliding window, while a historical feedback loop enables macro-level parameter adaptation. Experiments demonstrate that EA-AHS reduces planning time by 87.9% in a simple maze and by 81.0% in a complex point-block map compared to standard A*. In a 3D urban scenario, EA-AHS trades a modest computational overhead for substantial safety gains: its cumulative risk is only 48% of that of standard A* and 57% of that of weighted A*, and its minimum obstacle distance increases to 6.04 m. Unlike learning-based methods, EA-AHS requires no pre-training or neural inference, offering a highly practical, lightweight solution for resource-constrained onboard sensor systems.