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Cooperative Optimized Leader Selection in Large UAV Swarms by Maximizing the Average Controllability in a Hierarchical Control Structure
Conference proceeding   Peer reviewed

Cooperative Optimized Leader Selection in Large UAV Swarms by Maximizing the Average Controllability in a Hierarchical Control Structure

Kolitha Warnakulasooriya, Hakki Erhan Sevil and Aviv Segev
AIAA SCITECH 2026 Forum
AIAA SCITECH 2026 Forum (Orlando, Florida, USA, 01/12/2026–01/16/2026)
01/08/2026
DOI: https://doi.org/10.2514/6.2026-1996

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Abstract

The increasing demand for unmanned aerial vehicle (UAV) systems across various sectors underscores their escalating significance within multiple industries. A variety of unmanned aerial vehicles are employed in military operations, entertainment, and delivery services. Nevertheless, the control of extensive UAV swarms presents considerable complexity. Hierarchical control is recognized for its scalability and reliability in managing large swarms; however, the failure of a leader node can lead to a significant collapse of the swarm agents due to the inherently unstable control mechanisms. This research proposes a distributed, autonomous, time and memory efficient method for leader selection, leveraging the principles of swarm intelligence. The problem is formulated under the best-of-n mode, achieving satisfactory productivity. This algorithm outperforms leading methods such as Raft and GHS, demonstrating that the proposed approach exhibits over 90% accuracy while optimizing both time and memory usage. The objective of this approach is to enhance the reliability of search and rescue operations, ensure the safety of drone displays, and facilitate various large UAV swarm applications.

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