Multiagent Planning and Control for Swarm Herding in 2-D Obstacle Environments Under Bounded Inputs

This article presents a method for herding a swarm of adversarial agents toward a safe area in a 2-D obstacle environment. The team of defending agents (defenders) aims to block the path of a swarm of risk-averse, adversarial agents (attackers) and guide it to a safe area while navigating in an obstacle-populated environment. To achieve this, a closed formation (StringNet) of defenders is formed around the adversarial swarm. A combination of open-loop, near time-optimal controllers (that result in forming the defenders' formation), and state-feedback controllers with finite-time convergence guarantees under bounded inputs (that guide the formation around attackers and toward the safe area) synthesize the herding strategy. For demonstration purpose, we consider that the attacking swarm moves under a flocking model, which however is unknown to the defenders. Collision-free trajectory generation for the defenders, as well as their convergence to the desired formations, is proved formally, and simulations are provided to demonstrate the efficacy of the proposed approach. An implementation of the proposed approach on quadrotor vehicles simulated in the Gazebo simulator is also provided.

Automated summary

This article introduces a method for herding adversarial agents in a 2-D obstacle environment, utilizing closed formation of defenders to guide attackers toward a safe area. The efficacy of the approach is demonstrated through formal proofs of collision-free trajectory generation and convergence of defenders to desired formations in simulations. Additionally, an implementation on quadrotor vehicles in the Gazebo simulator is provided.