Self-organizing Nervous Systems for Robot Swarms

TL;DR

The paper introduces the Self-organizing Nervous System (SoNS), a hierarchical, self-organizing architecture for robot swarms that enhances scalability, flexibility, and fault tolerance in decentralized control.

Contribution

It presents a novel self-organizing hierarchy architecture enabling autonomous formation and reconfiguration of multi-level systems in robot swarms, improving scalability and fault tolerance.

Findings

Successfully demonstrated in real heterogeneous robot missions

Scalable to swarms of up to 250 robots in simulation

Enhanced fault tolerance in both simulation and real-world tests

Abstract

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.