Mr. Evan Ward Profile

Evan Ward

at US Naval Research Lab

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Proceedings Article | 14 June 2023 Poster + Paper

Particle-dynamics modeling of drone swarms

A. Shabaev, D. Chichka, E. Ward, C. Howells, S. Lambrakos

Proceedings Volume 12549, 125490K (2023) https://doi.org/10.1117/12.2661331

KEYWORDS: Simulations, Modeling, Particles, Detection and tracking algorithms, Data modeling, Algorithm development, Unmanned aerial vehicles, Systems modeling, Weapons, Sensors

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The concept of enabling drone-swarm engagement simulations using particle-dynamics models and near-neighbors tracking algorithms, motivated by SDI battle management, is examined. The general approach of using particle-dynamics models and near-neighbors tracking algorithms for modeling drone-swarm engagements is similar to nonequilibrium molecular-dynamics modeling of mixing dissimilar particulate materials. With respect to particle-dynamics representation of swarm-engagements, fundamental quantities that can represent characteristics of drone interactions, are interparticle potential functions, which are a function of drone-drone separation, the types of drones interacting, and the nature of the interaction. These potential functions provide formal representation of both deterministic and non-deterministic dronedrone interaction scenarios. The complexity of drone-swarm engagements, similar to that of SDI scenarios, characterized by small time-periods of engagement, multiple types of blue-red force interactions, and the requirement of near-neighbor target tracking, suggest that such a tool be necessary. The utility of the tool in creating potential-theory based control algorithms for swarm-on-swarm engagements is demonstrated using particle-dynamics simulations.

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