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.

Funding provided by: Fund for Scientific ResearchROR ID: https://ror.org/03q83t159Award Number: J.0064.20 Funding provided by: Fund for Scientific ResearchROR ID: https://ror.org/03q83t159Award Number: Funding provided by: Independent Research Fund DenmarkAward Number: 0136-00251B Funding provided by: China Scholarship Council awardAward Number: 201706270186 Funding provided by: Office of Naval Research Global AwardAward Number: N62909-19-1-2024 Funding provided by: Horizon 2020 Marie Skłodowska-CurieAward Number: 846009 Funding provided by: Université Libre de BruxellesROR ID: https://ror.org/01r9htc13Award Number:

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