Data from: Self-organizing nervous systems for robot swarms

We present the self-organizing nervous system (SoNS), a robot swarm
architecture based on self-organized hierarchy. The SoNS approach
enables robots to autonomously establish, maintain,
and reconfigure dynamic multilevel system architectures. For
example, a robot swarm consisting of n independent robots could
transform into a single n–robot SoNS and then into several
independent smaller SoNSs, where each SoNS uses a temporary and
dynamic hierarchy. Leveraging the SoNS approach, we showed that
sensing, actuation, and decision making can be coordinated in a
locally centralized way without sacrificing the benefits of
scalability, flexibility, and fault tolerance, for which swarm
robotics is usually studied. In several proof-of-concept robot
missions—including binary decision making and search and
rescue—we demonstrated that the capabilities of the SoNS approach
greatly advance the state of the art in swarm robotics. The missions were
conducted with a real heterogeneous aerial-ground robot swarm, using a
custom-developed quadrotor platform. We also demonstrated the
scalability of the SoNS approach in swarms of up to 250 robots in
a physics-based simulator and demonstrated several types of system fault
tolerance in simulation and reality.