Aerial Swarm Defense by StringNet Herding: Theory and Experiments

Abstract

This paper studies a defense approach against one or more swarms of adversarial agents. In our earlier work, we employed 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 adversarial agents were assumed to 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 toward 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. We also provide conditions under which defenders can successfully herd all the attackers. The efficacy of the approach is demonstrated via computer simulations, as well as hardware experiments with a fleet of quadrotors.

Herding stochastic autonomous agents via local control rules and online target selection strategies

“…Varava et al (2017); Song et al (2021) developed a "herding by caging" solution, based on geometric considerations and motion planning techniques to arrange the herder agents around the flock. A similar formation was presented in Chipade and Panagou (2019), and further developed in Chipade et al (2021), to let herders identify clusters of flocking adversarial agents, dynamically encircle and drive them to a safe zone. Recently, Sebastián and Montijano (2021) developed analytical and numerical control design procedures to compute suitable herding actions to herd evading agents to a desired position, even when the nonlinearities in the evaders' dynamics yield implicit equations.…”

“…A herder's action is based on the global knowledge of the environment and of the positions of all other agents. With respect to other solutions in the literature (Lien et al 2004;Pierson and Schwager 2018;Chipade et al 2021;Song et al 2021), our approach does not involve the use of ad hoc formation control strategies to force the herders surround the herd, but we rather enforce cooperation between herders by dynamically dividing the plane among them by means of simple yet effective and robust rules that can be easily implemented in real robots.…”

AI Driven Approaches in Swarm Robotics - A review

“…The solution involves distributing image classification tasks among a group of UAVs, enabling quick image recognition and faster decision-making. [38] introduces and verifies a defensive approach known as "Multi-Swarm StringNet Herding" developed to confront adversarial swarms with a group of defenders. The methodology develops and validates a decentralized version of the Mixed-Integer Quadratically Constrained Program to effectively allocate defenders to identified attacker swarms.…”

A Bilateral Cooperative Strategy for Swarm Escort under the Attack of Aggressive Swarms

“…The shepherding model consists of two parts: shepherd agents and herd agents. One or more shepherd agents escort non-cooperative herd agents to the goal area [25], or the defense swarm is treated as multiple shepherd agents, which "escort" attackers to the safety area [26]. However, this problem differs from the escort model in that the HVU has a preset goal rather than being driven by the escort process dynamically.…”