Hierarchical Control of Smart Particle Swarms

TL;DR

This paper introduces a hierarchical control method for large robot swarms, where a small guiding group influences a larger group of simple robots to form desired patterns, demonstrating scalability and real-world applicability.

Contribution

The paper presents a novel hierarchical control approach that enables scalable formation control of large robot swarms using a small guiding subset, validated through simulation and physical experiments.

Findings

Scales well with up to 5000 robots in simulation

Minimal pattern distortion observed in experiments

Physical robot experiments show comparable results to simulations

Abstract

We present a method for the control of robot swarms using two subsets of robots: a larger group of simple, oblivious robots (which we call the workers) that is governed by simple local attraction forces, and a smaller group (the guides) with sufficient mission knowledge to create and displace a desired worker formation by operating on the local forces of the workers. The guides coordinate to shape the workers like smart particles by changing their interaction parameters. We study the approach with a large scale experiment in a physics based simulator with up to 5000 robots forming three different patterns. Our experiments reveal that the approach scales well with increasing robot numbers, and presents little pattern distortion. We evaluate the approach on a physical swarm of robots that use visual inertial odometry to compute their relative positions and obtain results that are comparable with simulation. This work lays the foundation for designing and coordinating configurable smart particles, with applications in smart materials and nanomedicine.