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<p>In this paper, the dynamic allocation problem of relief supplies by a heterogeneous unmanned system (HUS) is discussed. A reinforcement learning-based intelligent allocation strategy is proposed that considers dynamic changes in disaster areas and payload differences among heterogeneous unmanned platforms, which effectively addresses the allocation conundrum. Firstly, an evaluation function closely related to the elapsed time of the HUS is designed, enabling the quantitative assessment of resource allocation schemes, thereby providing a basis for optimizing allocation strategies. Secondly, in response to the varying velocities and load capacities among HUS, an intelligent task allocation strategy is specifically devised to accommodate rescue operations. In addition, variations are introduced in both the number of affected areas and the demand for supplies. The application of intelligent algorithms enables rescue operations to consistently maintain efficient and flexible execution capabilities across various scenarios through dynamic adjustment strategies. Finally, compared to the strategy optimized by traditional distance-based optimization methods and greedy algorithms, the strategy generated by the proposed algorithm reduces task completion time and demonstrates better performance in rescue assignment.</p>

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Reinforcement learning based dynamic allocation strategy for relief supplies by heterogeneous unmanned system

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月8日

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  • Hang Guo,
  • Yuxi Ding,
  • Xiaoming Wang,
  • Wenxing Fu,
  • Jie Yan

摘要

In this paper, the dynamic allocation problem of relief supplies by a heterogeneous unmanned system (HUS) is discussed. A reinforcement learning-based intelligent allocation strategy is proposed that considers dynamic changes in disaster areas and payload differences among heterogeneous unmanned platforms, which effectively addresses the allocation conundrum. Firstly, an evaluation function closely related to the elapsed time of the HUS is designed, enabling the quantitative assessment of resource allocation schemes, thereby providing a basis for optimizing allocation strategies. Secondly, in response to the varying velocities and load capacities among HUS, an intelligent task allocation strategy is specifically devised to accommodate rescue operations. In addition, variations are introduced in both the number of affected areas and the demand for supplies. The application of intelligent algorithms enables rescue operations to consistently maintain efficient and flexible execution capabilities across various scenarios through dynamic adjustment strategies. Finally, compared to the strategy optimized by traditional distance-based optimization methods and greedy algorithms, the strategy generated by the proposed algorithm reduces task completion time and demonstrates better performance in rescue assignment.