Particle-dynamics modeling of drone swarms

A. Shabaev; D. Chichka; E. Ward; C. Howells; S. G. Lambrakos

doi:10.1117/12.2661331

14 June 2023 Particle-dynamics modeling of drone swarms

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

Proceedings Volume 12549, Unmanned Systems Technology XXV; 125490K (2023) https://doi.org/10.1117/12.2661331
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Conference Poster

Abstract

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.

Citation Download Citation

A. Shabaev, D. Chichka, E. Ward, C. Howells, and S. G. Lambrakos "Particle-dynamics modeling of drone swarms", Proc. SPIE 12549, Unmanned Systems Technology XXV, 125490K (14 June 2023); https://doi.org/10.1117/12.2661331

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available