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Learning Swarm Interaction Dynamics From Density Evolution
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DOI：10.1109/TCNS.2022.3198784.png)
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Abstract
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In this article, we consider the problem of understanding the coordinated movements of biological or artificial swarms. In this regard, we propose a learning scheme to estimate the coordination laws of the interacting agents from observations of the swarm's density over time. We describe the dynamics of the swarm based on pairwise interactions according to a Cucker-Smale flocking model, and express the swarm's density evolution as the solution to a system of mean-field hydrodynamic equations. We propose a new family of parametric functions to model the pairwise interactions, which allows for the mean-field macroscopic system of integro-differential equations to be efficiently solved as an augmented system of partial differential equations. Finally, we incorporate the augmented system in an iterative optimization scheme to learn the dynamics of the interacting agents from observations of the swarm's density evolution over time. The results of this work can offer an alternative approach to study how animal flocks coordinate, create new control schemes for large networked systems, and serve as a central part of defense mechanisms against adversarial drone attacks.
Keywords:
Mathematical models
Hydrodynamics
Power system dynamics
Numerical models
Network systems
Green's function methods
Evolution (biology)
Biological networks
learning
networks of autonomous agents
swarm interaction dynamics
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5
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1.7K
Citations:
5.8K
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