Risk-bounded and fairness-aware path planning for urban air mobility operations under uncertainty

Collision avoidance is an important issue in the field of the autonomous operations of aerial vehicles in dynamic and uncertain urban environments. This paper introduces a risk-bounded and fairness-aware path planning algorithm for multiple electrical vertical take-off and landing (eVTOL) aircraft operating in such environments. This algorithm advances the sampling-based path planning with the risk domain formulation to generate stochastically safety assured collision-free paths that are robust to both vehicle and environmental obstacle uncertainties for multiple eVTOL aircraft. To address the concern of flight fairness between different eVTOLs in the free flight airspace, the strategy of permit assignment is introduced. By incorporating the risk domain formulation into the sampling-based path planning with the strategy of permit assignment, the algorithm proposed in this paper not only inherits the computational advantage of the sampling-based methods, but also guarantees a stochastically feasible flying zone at every time step. Simulation studies demonstrate the promising performance of the proposed algorithm.