Adaptive Planning for Multiple UAVs with In-Flight Refueling

Abstract

This paper presents a rolling-horizon replanning framework for multi-UAV missions considering fuel constraints in dynamic environments. At each decision epoch, a re-optimization subproblem is solved to minimize the total travel distance of all agents (UAVs) while visiting remaining task locations, considering en-route refueling depots. A mixed-integer linear programming formulation of the subproblem is proposed to find the optimal solution. In addition, to enable real-time computation, we develop a heuristic-based solver framework: an extended sequential greedy algorithm provides a quick baseline, while a reinforcement learning (RL) approach using an attention model and long short-term memory aims to overcome the local optimality of the greedy method. A case study and numerical experiments demonstrate the effectiveness of the proposed framework and the performance of the solution methods.