Deployment of Robotic Swarms via Density Feedback Control.

Abstract

With the rapid development of AI and robotics, deploying a large swarm of networked robots has foreseeable applications in the near future. Existing research in swarm robotics has mainly followed a bottom-up philosophy with predefined local coordination and control rules. However, it is arduous to verify the global requirements and analyze their overall performance. This motivates us to pursue a top-down approach, and develop a provable control strategy for the deployment problem of a robotic swarm to achieve a desired global configuration. Specifically, we use partial differential equations (PDEs) to model the swarm and control its probability density over a bounded spatial domain using density feedback. The presented density feedback control laws use the current density as feedback signals and generate corresponding velocity fields that, by acting locally on individual robots, guide their global configuration to a target profile. The key contribution lies in applying the concept of input-to-state stability (ISS) to show that the disturbed closed-loop system (which is a nonlinear PDE) is locally ISS with respect to estimation errors of the current density. The effectiveness of the proposed control laws is verified using agent-based simulations.