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.

翻译：控制机器人集群的系统架构通常在部署前设定，可采用集中式或分散式两种模式。这种二元对立具有高度约束性，因为分散式系统通常完全自组织，难以通过分析进行设计；而集中式系统则存在单点故障和可扩展性有限的缺陷。为解决这一矛盾，我们提出自组织神经系统（SoNS）——一种基于自组织层级结构的新型机器人集群架构。SoNS方法使机器人能够自主建立、维护和重构动态多层级系统架构。例如，由$n$个独立机器人组成的集群可转化为单一$n$机器人SoNS，而后进一步分解为若干独立的小型SoNS，每个小型SoNS采用临时动态层级结构。利用SoNS方法，我们证明感知、驱动与决策可在局部集中式框架下实现协同，同时不牺牲集群机器人学通常研究的可扩展性、灵活性及容错性优势。在多个概念验证机器人任务（包括二元决策与搜索救援）中，我们展示了SoNS方法对当前集群机器人学前沿技术的重大推进。这些任务在自主开发的四旋翼平台上，通过真实异构空地机器人集群完成。同时，我们在基于物理引擎的模拟器中验证了SoNS方法在多达250台机器人集群中的可扩展性，并在仿真与真实实验中演示了多种系统容错能力。