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A Real-Time System for Scheduling and Managing UAV Delivery in Urban Areas

Han Liu, Tian Liu, Kai Huang

TL;DR

This work addresses the gap between high-level UAV delivery scheduling and real-world execution by introducing a real-time management middleware built around the Airport–Unloading Station model. It defines a distributed architecture with Master, UAV, and AGV management nodes and develops finite-state machines for both UAVs and AGVs to enable robust, autonomous operation. Three collaborative scheduling schemes involving UAVs, AGVs, and ground staff are implemented and evaluated, showing improved delivery efficiency and resource utilization in urban scenarios. The system demonstrates practical feasibility for commercial UAV deliveries and provides a codebase for replication and extension.

Abstract

As urban logistics demand continues to grow, UAV delivery has become a key solution to improve delivery efficiency, reduce traffic congestion, and lower logistics costs. However, to fully leverage the potential of UAV delivery networks, efficient swarm scheduling and management are crucial. In this paper, we propose a real-time scheduling and management system based on the ``Airport-Unloading Station" model, aiming to bridge the gap between high-level scheduling algorithms and low-level execution systems. This system, acting as middleware, accurately translates the requirements from the scheduling layer into specific execution instructions, ensuring that the scheduling algorithms perform effectively in real-world environments. Additionally, we implement three collaborative scheduling schemes involving autonomous ground vehicles (AGVs), unmanned aerial vehicles (UAVs), and ground staff to further optimize overall delivery efficiency. Through extensive experiments, this study demonstrates the rationality and feasibility of the proposed management system, providing practical solution for the commercial application of UAVs delivery in urban. Code: https://github.com/chengji253/UAVDeliverySystem

A Real-Time System for Scheduling and Managing UAV Delivery in Urban Areas

TL;DR

This work addresses the gap between high-level UAV delivery scheduling and real-world execution by introducing a real-time management middleware built around the Airport–Unloading Station model. It defines a distributed architecture with Master, UAV, and AGV management nodes and develops finite-state machines for both UAVs and AGVs to enable robust, autonomous operation. Three collaborative scheduling schemes involving UAVs, AGVs, and ground staff are implemented and evaluated, showing improved delivery efficiency and resource utilization in urban scenarios. The system demonstrates practical feasibility for commercial UAV deliveries and provides a codebase for replication and extension.

Abstract

As urban logistics demand continues to grow, UAV delivery has become a key solution to improve delivery efficiency, reduce traffic congestion, and lower logistics costs. However, to fully leverage the potential of UAV delivery networks, efficient swarm scheduling and management are crucial. In this paper, we propose a real-time scheduling and management system based on the ``Airport-Unloading Station" model, aiming to bridge the gap between high-level scheduling algorithms and low-level execution systems. This system, acting as middleware, accurately translates the requirements from the scheduling layer into specific execution instructions, ensuring that the scheduling algorithms perform effectively in real-world environments. Additionally, we implement three collaborative scheduling schemes involving autonomous ground vehicles (AGVs), unmanned aerial vehicles (UAVs), and ground staff to further optimize overall delivery efficiency. Through extensive experiments, this study demonstrates the rationality and feasibility of the proposed management system, providing practical solution for the commercial application of UAVs delivery in urban. Code: https://github.com/chengji253/UAVDeliverySystem

Paper Structure

This paper contains 14 sections, 3 equations, 7 figures.

Table of Contents

  1. INTRODUCTION
  2. related work
  3. Preliminaries
  4. Management system
  5. Management Nodes
  6. UAV Finite state machine
  7. AGV Finite state machine
  8. State Transition Process
  9. Scheduling system
  10. Ground traffic scheduler
  11. Air traffic scheduler
  12. Experiments
  13. Conclusion
  14. Acknowledgments

Figures (7)

  • Figure 1: Delivery airport and unloading stations in urban.
  • Figure 2: The framework of our system.
  • Figure 3: Illustration of UAV FSM and AGV FSM. The blue part indicates the state. The green part indicates the command. The yellow part indicates the condition.
  • Figure 4: AGV scheduling process of three schemes
  • Figure 5: UAV delivery workflow: real-world and simulation visualization.
  • ...and 2 more figures