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Multi-UAV Redeployment Optimization Based on Multi-Agent Deep Reinforcement Learning Oriented to Swarm Performance

Qilong Wu¹, Zitao Geng¹, Yi Ren¹

¹School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China.

Sensors (Basel, Switzerland)

|December 9, 2023

Summary

This summary is machine-generated.

This study introduces a new strategy for multi-unmanned aerial vehicle (multi-UAV) systems to quickly redeploy after vehicle loss. The multi-agent deep reinforcement learning approach enhances swarm performance and ensures optimal group formation.

Keywords:

UAV swarm redeployment distributed reconfiguration strategy multi-agent deep reinforcement learning unmanned aerial vehicle (UAV)

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Area of Science:

Artificial Intelligence
Robotics
Distributed Systems

Background:

Distributed artificial intelligence is crucial for multi-unmanned aerial vehicle (multi-UAV) operations.
Optimal redeployment of multi-UAVs after vehicle destruction presents significant distributed reconfiguration (DR) challenges.

Purpose of the Study:

To develop a novel multi-agent deep reinforcement learning-based distributed reconfiguration strategy (DRS) for multi-UAV systems.
To optimize multi-UAV group redeployment for enhanced swarm performance.

Main Methods:

A two-layer DRS framework was designed, utilizing a QMIX network for swarm-level redeployment and individual deep Q-networks for single-group adjustments.
Simulations were conducted in Python to evaluate the proposed strategy.

Main Results:

The proposed DRS effectively optimizes multi-UAV group redeployment, leading to improved swarm performance.
The strategy demonstrated high-quality performance in large-scale scenarios through case study validation.

Conclusions:

The developed multi-agent deep reinforcement learning-based DRS provides an effective solution for the dynamic reconfiguration of multi-UAV systems.
This approach enhances the resilience and performance of multi-UAV swarms in complex operational environments.