Distributed Camouflage for Swarm Robotics and Smart Materials
This addresses the challenge of dynamic camouflage for swarm robotics and smart materials, offering an improvement over static or viewpoint-dependent systems, though it appears incremental compared to existing adaptive methods.
The paper tackles the problem of enabling a swarm of active particles to camouflage in varying environments by developing a distributed algorithm that allows them to sense and adapt their patterns quickly, achieving convergence in simulations and on miniature robots for diverse patterns.
We present a distributed algorithm for a swarm of active particles to camouflage in an environment. Each particle is equipped with sensing, computation and communication, allowing the system to take color and gradient information from the environment and self-organize into an appropriate pattern. Current artificial camouflage systems are either limited to static patterns, which are adapted for specific environments, or rely on back-projection, which depend on the viewer's point of view. Inspired by the camouflage abilities of the cuttlefish, we propose a distributed estimation and pattern formation algorithm that allows to quickly adapt to different environments. We present convergence results both in simulation as well as on a swarm of miniature robots "Droplets" for a variety of patterns.