Stochastic route optimization under dynamic ground risk uncertainties for safe drone delivery operations

Abstract

The rise of unmanned aircraft systems (UAS) for urban drone delivery introduces significant risks, particularly the potential for crash-induced fatalities on the ground. A crucial strategy to address this challenge is through risk assessment and mitigation of flight routes that consider the stochastic nature of urban populations. Traditional strategies treat drone flight route approval and execution independently, which fall short in such a dynamic risk environment where plans deemed safe at the strategic approval stage may later prove hazardous, and vice versa. To address these intricacies, this paper introduces a novel two-stage stochastic optimization model that integrates strategic route feasibility assessment with tactical route selection and timing adjustments. A unique aspect of our model is the implementation of a risk penalty that effectively bridges decisions between the two stages, thereby reducing the likelihood of decision errors caused by stochastic variations. Through extensive simulations within Singapore’s urban context, our model demonstrates a risk reduction by an average of 36.13%, which significantly outperforms traditional methods. This performance consistency across 100 simulated urban scenarios proved the robustness and broad applicability of our model. Furthermore, our model shows an 18% improvement in resolving potential decision errors, with the stochastic solution further affirming a notable risk decrease of 27.18%. Our research enhances the domain of UAS risk-based stochastic decision making and provides opportunities for automated flight approval, drone fleet management, and urban airspace management.