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Updated: May 26, 2025

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A distributed task allocation approach for multi-UAV persistent monitoring in dynamic environments.

Changming Zhang1,2,3, Caiyue Xu1,2,3, Gang Li4,5,6,7

  • 1College of Electronics and Information Engineering, Tongji University, Shanghai, 201804, China.

Scientific Reports
|February 22, 2025
PubMed
Summary

This study presents IRADA, a novel distributed method for multiple Unmanned Aerial Vehicles (UAVs) to efficiently monitor dynamic environments. IRADA enhances situational awareness by minimizing information uncertainty, even with UAV failures.

Keywords:
Distributed algorithmMulti-UAV systemsPersistent monitoringTask allocation

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Area of Science:

  • Robotics and Autonomous Systems
  • Distributed Artificial Intelligence
  • Environmental Monitoring

Background:

  • Persistent monitoring in dynamic environments using multiple Unmanned Aerial Vehicles (UAVs) faces challenges from time-varying states, limited energy, and communication constraints.
  • Maintaining up-to-date situational awareness and minimizing information uncertainty are critical for effective multi-UAV operations.
  • Existing methods often struggle with the complexities of distributed decision-making under resource limitations.

Purpose of the Study:

  • To introduce IRADA (Integrated Reward Aggregation for Distributed Allocation), a novel distributed task allocation method for multi-UAV persistent monitoring.
  • To enhance situational awareness by minimizing information uncertainty across distributed points of interest (POIs) in dynamic environments.
  • To improve the efficiency and resilience of multi-UAV systems in complex operational scenarios.

Main Methods:

  • A distributed task allocation method guiding each UAV's decision-making through real-time, distributed computation of POI rewards.
  • Rewards integrate information collection efficiency, energy constraints, and inter-UAV communication tendencies.
  • Spatial aggregation of integrated rewards using Gaussian Mixture Models (GMM) for computationally efficient multi-step decision-making.

Main Results:

  • IRADA demonstrates superior performance in rapid information collection across diverse system configurations (UAV numbers, budgets, communication ranges, POI counts).
  • Ablation studies confirm the performance and computational efficiency benefits of GMM-based reward aggregation.
  • The method shows resilience to UAV failures, maintaining effective coverage via autonomous task redistribution.

Conclusions:

  • IRADA provides an effective and computationally efficient distributed approach for multi-UAV persistent monitoring in dynamic environments.
  • The GMM-based reward aggregation significantly enhances decision-making capabilities and system performance.
  • The proposed method offers robust system resilience, ensuring continuous coverage despite potential UAV failures.