Learning Swarm Interaction Dynamics from Density Evolution

Learning Swarm Interaction Dynamics from Density Evolution

Title : Learning Swarm Interaction Dynamics from Density Evolution
Authors : Christos Mavridis, Amoolya Tirumalai, and John S. Baras
Journal : IEEE Transactions on Control of Network Systems (IEEE TCNS) pp. 1-12, DOI: 10.1109/TCNS.2022.3198784, August 16, 2022

We focus on understanding the coordinated movements of animal or artificial swarms, and propose a learning scheme to capture the coordination laws of the interacting agents, from observations of the swarm’s density over time. We describe the dynamics of the interacting agents with a general Cucker-Smale flocking model, and propose a family of parametric interaction functions which allows the mean-field macroscopic system of partial integro-differential equations, describing the swarm’s density, to be efficiently solved as an augmented system of PDEs. We solve the augmented system in an iterative optimization scheme to learn the dynamics of the interacting agents from observations of the swarm’s density evolution. The results of this work can give insights on how animal flocks coordinate, give rise to new control schemes for large distributed systems, and serve as a central part of defence mechanisms against adversarial UAV swarms. We illustrate our methodology using simulation data in two dimensions.

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