Aerial-Ground Collaborative Routing with Uncertain Exploration Value

Abstract This study develops a novel framework for aerial-ground collaborative routing that addresses uncertainty in exploration value. The framework enables an unmanned aerial vehicle (UAV) to conduct rapid reconnaissance while a ground vehicle (GV) gathers profit, dynamically optimizing the GV’s route based on updated information. Two complementary approaches are presented: a mixed integer linear programming (MILP) formulation for optimal path selection and an adaptive two-phase method for robust, uncertain environment exploration. A case study validates these approaches, demonstrating how aerial-ground collaboration improves mission efficiency and information acquisition in uncertain environments.