Reinforcement learning based fission-fusion for heterogeneous UAV swarm under dynamic interference environments

Yufeng Wang; Xiaorong Zhang; Qing Wang; Zhilan Zhang; Hang Zhang; Xiwang Dong; Wenrui Ding

doi:10.1117/12.3032486

16 August 2024 Reinforcement learning based fission-fusion for heterogeneous UAV swarm under dynamic interference environments

Yufeng Wang, Xiaorong Zhang, Qing Wang, Zhilan Zhang, Hang Zhang, Xiwang Dong, Wenrui Ding

Author Affiliations +

Proceedings Volume 13218, First Aerospace Frontiers Conference (AFC 2024); 132180Q (2024) https://doi.org/10.1117/12.3032486
Event: First Aerospace Frontiers Conference (AFC 2024), 2024, Xi’an, China

Abstract

The control of unmanned aerial vehicle (UAV) swarms represents a complex field of study, chiefly due to the conflicting behaviors observed among individual UAVs and the impact of external movement disturbances on the swarm. However, the fission-fusion dynamics of UAV swarms in response to unknown dynamic disturbances have received comparatively less attention than their behavior in static flight. An unknown dynamic interference environments fission-fusion for heterogeneous UAV swarm via reinforcement (DEFHRL) learning algorithm is presented, which effectively addresses the challenge of fission-fusion control within unknown dynamic interference environments. Firstly, we develop a heterogeneous swarm self-organized fission-fusion control framework that enables multi-swarm fission-fusion maneuvers for UAV swarms. Next, we introduce a topological fission selection algorithm that facilitates control of fission selection under minimal interaction loads, effectively enabling controllable swarm sizes. Finally, we introduce a subgroup adversarial algorithm, grounded in reinforcement learning, designed to conduct dynamic adversarial engagements against unknown interference with minimal resource expenditure. Simulation experiments show that UAV swarms, when operating in dynamic heterogeneous environments, can successfully execute self-organized fission-fusion maneuvers, effectively safeguarding the primary swarm against heterogeneous interference.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yufeng Wang, Xiaorong Zhang, Qing Wang, Zhilan Zhang, Hang Zhang, Xiwang Dong, and Wenrui Ding "Reinforcement learning based fission-fusion for heterogeneous UAV swarm under dynamic interference environments", Proc. SPIE 13218, First Aerospace Frontiers Conference (AFC 2024), 132180Q (16 August 2024); https://doi.org/10.1117/12.3032486

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KEYWORDS

Unmanned aerial vehicles

Machine learning

Polarization

Detection and tracking algorithms

Adversarial training

Seaborgium

Algorithm development

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