UAV Path Planning with Safety Situational Field Based on SAH-PSO
Lei Yue, Xiling Luo, Yupeng Wang, Tianyi Zhang, Wenxiang Xu, Zeyang Sun · 2024
Unmanned aerial vehicles (UAVs) operating in urban environment encounter substantial safety risks arising from various factors, including static obstacles, adverse weather conditions, route conflicts with other aircraft, unstable communication signal et al. However, existing researches on low-altitude airspace situation analysis often fall short in addressing the complexities of urban environment, leading to inadequate safety assessment, monitoring effectiveness, and challenges in supporting low-altitude route planning. To mitigate these issues, this paper proposes a novel safety situation field (SSF) construction method tailored for complex urban environment. SSF uses risk probability parameters to calculate operational risks in low-altitude airspace 3D grids. To solve the path optimization problem based on SSF, we propose a hybrid particle swarm optimization algorithm named as SAH-PSO. Experimental results demonstrate the rapid convergence of the method, and statistically significant reduction in safety risks compared to benchmark algorithms. This study aims to provide some theoretical backing and methodological insights for ensuring safe UAV flights within low-altitude airspace.