We present a method for the control of robot swarms which allows the shaping and the translation of patterns of simple robots (“smart particles”), using two types of devices. These two types represent a hierarchy: 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 ) that dynamically inject new interaction parameters into the worker swarm, reprogramming their local attraction/repulsion rules to achieve and maintain desired formations. This framework exploits the knowledge of the guides, which coordinate to shape the workers like smart particles by changing their interaction parameters. We study the approach with a large‐scale simulation experiment in a physics‐based simulator with up to 1000 robots forming three different patterns. Our experiments reveal that the approach scales well with increasing robot numbers and presents little pattern distortion for a set of target moving shapes. 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 foundation for designing and coordinating configurable smart particles, with applications in smart materials and nanomedicine.
Varadharajan et al. (Mon,) studied this question.