  The system architecture controlling a group of robots is generally set before
deployment and can be either centralized or decentralized. This dichotomy is
highly constraining, because decentralized systems are typically fully
self-organized and therefore difficult to design analytically, whereas
centralized systems have single points of failure and limited scalability. To
address this dichotomy, we present the Self-organizing Nervous System (SoNS), a
novel robot swarm architecture based on self-organized hierarchy. The SoNS
approach enables robots to autonomously establish, maintain, and reconfigure
dynamic multi-level 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 show 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 demonstrate that the capabilities of the SoNS approach
greatly advance the state of the art in swarm robotics. The missions are
conducted with a real heterogeneous aerial-ground robot swarm, using a
custom-developed quadrotor platform. We also demonstrate the scalability of the
SoNS approach in swarms of up to 250 robots in a physics-based simulator, and
demonstrate several types of system fault tolerance in simulation and reality.
