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Adaptive airspace allocation model for urban drone logistics using multi-objective optimization under uncertainty.

Yao Zhu1, Xin Sun2, Tongdi Hou2

  • 1Business School, Yancheng Polytechnic College, Yancheng, 224005, Jiangsu, China. yphz5223@outlook.com.

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Summary
This summary is machine-generated.

This study introduces a hybrid framework for urban unmanned aerial vehicle (UAV) logistics, enhancing efficiency and safety in complex environments. The DRL-RO model addresses uncertainties for robust city-level UAV traffic management.

Keywords:
Adaptive airspace allocationDeep reinforcement learningDistributively robust optimizationMulti-objective optimizationUrban unmanned Aerial Vehicle logistics

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Area of Science:

  • Logistics and Transportation Science
  • Artificial Intelligence and Robotics
  • Urban Planning and Management

Background:

  • Urban unmanned aerial vehicle (UAV) logistics face challenges with limited airspace, demand volatility, and uncertainties.
  • Static allocation methods are inadequate for dynamic urban settings.

Purpose of the Study:

  • To develop an adaptive framework for urban UAV logistics management.
  • To address challenges of airspace limitations, demand fluctuations, and uncertainties.

Main Methods:

  • A DRL-RO (Deep Reinforcement Learning and Discrete Robust Optimization) hybrid framework was developed.
  • A three-layer uncertainty modeling system and an attention-enhanced policy network were employed.
  • An improved MOEA/D-DRL algorithm was used for Pareto frontier approximation.

Main Results:

  • The framework achieved sub-quadratic computational complexity with a high success rate in Shenzhen.
  • A hierarchical airspace management strategy balanced distribution efficiency, flight safety, and costs.
  • Wasserstein sphere constraints ensured robustness and scalability in extreme scenarios.

Conclusions:

  • The DRL-RO framework offers a robust solution for urban UAV traffic management.
  • It provides theoretical support and technical solutions for city-level UAV systems.
  • The study demonstrates effective balancing of efficiency, safety, and cost in UAV logistics.