Multi-Swarm Herding: Protecting against Adversarial Swarms

TL;DR

This paper introduces a multi-swarm herding strategy using clustering and assignment algorithms to defend against multiple adversarial swarms, enhancing previous confinement methods.

Contribution

It proposes a novel approach combining DBSCAN clustering and assignment optimization to adaptively reassign defenders to multiple attacker swarms.

Findings

Effective identification of attacker sub-swarms using DBSCAN.

Successful reallocation of defenders to multiple swarms.

Simulation results demonstrate improved defense performance.

Abstract

This paper studies a defense approach against one or more swarms of adversarial agents. In our earlier work, we employ a closed formation (`StringNet') of defending agents (defenders) around a swarm of adversarial agents (attackers) to confine their motion within given bounds, and guide them to a safe area. The control design relies on the assumption that the adversarial agents remain close enough to each other, i.e., within a prescribed connectivity region. To handle situations when the attackers no longer stay within such a connectivity region, but rather split into smaller swarms (clusters) to maximize the chance or impact of attack, this paper proposes an approach to learn the attacking sub-swarms and reassign defenders towards the attackers. We use a `Density-based Spatial Clustering of Application with Noise (DBSCAN)' algorithm to identify the spatially distributed swarms of the attackers. Then, the defenders are assigned to each identified swarm of attackers by solving a constrained generalized assignment problem. Simulations are provided to demonstrate the effectiveness of the approach.