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Self-organizing nervous systems for robot swarms

par Zhu, Weixu ;Dorigo, Marco ;Oguz, Sinan ;Heinrich, Mary Katherine ;Allwright, Michael ;Wahby, Mostafa ;Christensen, Anders Lyhne ;Garone, Emanuele
Référence Science Robotics, 9, 96
Publication Publié, 2024-11

Article révisé par les pairs

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Résumé :

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.