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<p>Unmanned Aerial Vehicles (UAVs), adept at aerial surveillance, are often constrained by their limited battery capacity. Refueling on slow-moving Unmanned Ground Vehicles (UGVs) can significantly enhance UAVs’ operational endurance. This paper explores the computationally complex problem of cooperative UAV-UGV routing for vast area surveillance, considering speed and fuel constraints. It presents a sequential multi-agent planning framework aimed at achieving feasible and optimally satisfactory solutions. By considering the UAV fuel limit and utilizing a minimum set cover algorithm, we determine UGV refueling stops. This, in turn, facilitates UGV route planning as the first step. Through a task allocation technique and energy-constrained vehicle routing problem modeling with time windows (E-VRPTW), we then achieve the UAV route in the second step of the framework. The effectiveness of our multi-agent strategy is demonstrated through the implementation on 30 different task scenarios across three different scales. This work provides significant insight into the collaborative advantages of UAV-UGV systems and introduces heuristic approaches to bypass computational challenges and swiftly reach high-quality solutions.</p>

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Cooperative Multi-Agent Planning Framework for Fuel Constrained UAV-UGV Routing Problem

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年1月9日

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  • Md Safwan Mondal,
  • Subramanian Ramasamy,
  • James D. Humann,
  • James M. Dotterweich,
  • Jean-Paul F. Reddinger,
  • Marshal A. Childers,
  • Pranav A. Bhounsule

摘要

Unmanned Aerial Vehicles (UAVs), adept at aerial surveillance, are often constrained by their limited battery capacity. Refueling on slow-moving Unmanned Ground Vehicles (UGVs) can significantly enhance UAVs’ operational endurance. This paper explores the computationally complex problem of cooperative UAV-UGV routing for vast area surveillance, considering speed and fuel constraints. It presents a sequential multi-agent planning framework aimed at achieving feasible and optimally satisfactory solutions. By considering the UAV fuel limit and utilizing a minimum set cover algorithm, we determine UGV refueling stops. This, in turn, facilitates UGV route planning as the first step. Through a task allocation technique and energy-constrained vehicle routing problem modeling with time windows (E-VRPTW), we then achieve the UAV route in the second step of the framework. The effectiveness of our multi-agent strategy is demonstrated through the implementation on 30 different task scenarios across three different scales. This work provides significant insight into the collaborative advantages of UAV-UGV systems and introduces heuristic approaches to bypass computational challenges and swiftly reach high-quality solutions.