Herding an Adversarial Swarm in an Obstacle Environment

Abstract

This paper studies a problem of defending safety-critical infrastructure from adversarial swarm. We employ a closed formation ('StringNet') of defending agents around the adversarial agents to restrict their motion and guide them to a safe area through obstacle populated environment. Control laws for forming this StringNet and guiding it to a safe area are developed and the performance is analyzed formally. Flocking motion is considered for the adversarial swarm in the presence of rectangular obstacles for which modifications to the existing flocking control laws are provided.The authors are with the

Interacting Swarm Sensing and Stabilization

“…Recent work in swarm robotics and autonomy has begun to address how one swarm can detect, redirect, capture, or defend itself against another [49,50,51]. However, most approaches are algorithmic and lack basic physical and analytical insights.…”

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.…”

“…Notable herding solutions are those proposed in Vaughan et al (2000), Lien et al (2004), Strombom et al (2014), Paranjape et al (2018), Licitra et al (2019) for single herders and in Lien et al (2005), Haque et al (2009), Lee and Kim (2017), Pierson and Schwager (2018), Nalepka et al (2017b), Montijano et al (2013), Varava et al (2017), Song et al (2021), Chipade and Panagou (2019), Sebastián and Montijano (2021) for multiple herders.…”

Multi-robot Implicit Control of Herds

“…It is not mandatory to achieve a compact and closed encirclement to succeed but just the distribution of the hunters near the preys. Examples of approximation and encirclement that could be used are [1], [5], [8] or [15]. Similarly, a simple task assignment is computed to associate each prey with its corresponding desired location, e.g., with the Hungarian algorithm, reducing this way the chances of collision caused by crossing paths.…”

“…The work in [4] drives groups of entities by an active encirclement E. Sebastián and E. Montijano are associated with the Instituto de Investigación en Ingeniería de Aragón, Universidad de Zaragoza, Spain esebastian@unizar.es emonti@unizar.es but does not consider specific final positions for each prey. Following a similar approach, the authors in [8] go a step further and propose an active encirclement which avoids obstacles. In [9], a single robot is in charge of herding multiple targets controlling them one by one.…”