  Various applications of advanced air mobility (AAM) in urban environments
facilitate our daily life and public services. As one of the key issues of
realizing these applications autonomously, path planning problem has been
studied with main objectives on minimizing travel distance, flight time and
energy cost. However, AAM operations in metropolitan areas bring safety and
society issues. Because most of AAM aircraft are unmanned aerial vehicles
(UAVs) and they may fail to operate resulting in fatality risk, property damage
risk and societal impacts (noise and privacy) to the public. To quantitatively
assess these risks and mitigate them in planning phase, this paper proposes an
integrated risk assessment model and develops a hybrid algorithm to solve the
risk-based 3D path planning problem. The integrated risk assessment method
considers probability and severity models of UAV impact ground people and
vehicle. By introducing gravity model, the population density and traffic
density are estimated in a finer scale, which enables more accurate risk
assessment. The 3D risk-based path planning problem is first formulated as a
special minimum cost flow problem. Then, a hybrid estimation of distribution
algorithm (EDA) and risk-based A* (named as EDA-RA*) algorithm is proposed to
solve the problem. To improve computational efficiency, k-means clustering
method is incorporated into EDA-RA* to provide both global and local search
heuristic information, which formed the EDA and fast risk-based A* algorithm we
call EDA-FRA*. Case study results show that the risk assessment model can
capture high risk areas and the generated risk map enables safe UAV path
planning in urban complex environments.
