Case-Study Non-Equilibrium Particle-Dynamics Modeling of Swarm-on-Swarm Engagements
Abstract
This report describes simulations demonstrating the modeling of swarm engagements using particle-dynamics models and artificial-potential based control algorithms. Such control algorithms are based on near-neighbor communication and near-neighbor tracking of noncooperative agents. These laws cause the swarms to mimic the actions of particles whose dynamics are defined by potential functions. The general approach of using such models is similar to that of nonequilibrium molecular-dynamics modeling of mixing dissimilar particulate materials. Swarm engagement scenarios can be complex because of small time-periods of engagement, multiple types of blue-red force interactions, and the requirement of near-neighbor target tracking. With respect to particle-dynamics representation of swarm-engagements, fundamental quantities that can represent characteristics of particles interactions are the defined potential functions, which can be functions of particle-particle separation, the types of particles interacting, and type of the interaction. These potential functions can provide formal representation of both deterministic and nondeterministic particle-particle interaction scenarios. The complexity of swarm interactions suggests that such a modeling tool is necessary, and can be used in creating potential-theory based control algorithms for swarm-on-swarm interactions.
Document Details
- Document Type
- Technical Report
- Publication Date
- Mar 16, 2022
- Accession Number
- AD1163920
Entities
People
- Andrew Shabaev
- Christopher A. Howells
- David Chichka
- Samuel G. Lambrakos
Organizations
- United States Naval Research Laboratory