Probability-Constrained Path Planning for UAV Logistics Using Mixed Integer Linear Programming
Zhongxiang Chen, Shengchun Wang, Kaige Chen, Xiaoke Zhang · Modelling—International Open Access Journal of Modelling in Engineering Science · 2025
In three-dimensional (3D) logistics environments, finding optimal paths for unmanned aerial vehicles (UAVs) is challenging due to positioning inaccuracies that require ground-based corrections. These inaccuracies are exacerbated in harsh environments, leading to a significant risk of correction failure. This research proposes a multi-objective mixed integer programming model (MILP) that transforms dynamic uncertainties into binary constraints, utilizing a hierarchical sequencing strategy in the Gurobi optimizer to efficiently identify optimal paths. Our simulations indicate that achieving an 80% mission success probability necessitates an optimal path of 104,946 m with nine corrections. For a 100% success rate, the path length increases to 105,874 m, with corrections remaining constant. These results validate the model’s effectiveness in navigating environments with probabilistic constraints, highlighting its potential for addressing complex logistical challenges.