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<p>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.</p>

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Aerial-Ground Collaborative Routing with Uncertain Exploration Value

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年2月12日

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  • Chaehwan Moon,
  • Euihyeon Choi,
  • Jaemyung Ahn

摘要

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.