实现可旋转不变的神经元胞自动机硬件平台，支持模块化机器人自组织。
A Rotation-Invariant Embedded Platform for (Neural) Cellular Automata
- 模块化对称结构使单元任意朝向连接无影响。
- 每单元自带电池，断连仍保状态，支持独立运行。
- 适用于分布式机器人与自组织系统，开源可复现。
本文提出一种用于模拟（神经）胞自动机（NCA）的旋转不变嵌入式平台，专为模块化机器人系统设计。受前期物理NCA研究启发，我们引入关键创新，克服了以往硬件设计的局限性。平台采用对称模块化结构，实现细胞间任意方向连接无缝衔接；每个细胞配备电池供电，可独立运行并保持状态，即使脱离集体也能维持功能。为验证平台适用性，我们提出一种新型旋转不变NCA模型，用于各向同性形状分类。该系统为物理实现NCA提供了可靠基础，潜在应用于分布式机器人系统与自组织结构。实现代码、硬件设计、仿真器及演示视频已公开共享于：https://github.com/dwoiwode/embedded_nca。
原文摘要 · Abstract (English)
This paper presents a rotation-invariant embedded platform for simulating (neural) cellular automata (NCA) in modular robotic systems. Inspired by previous work on physical NCA, we introduce key innovations that overcome limitations in prior hardware designs. Our platform features a symmetric, modular structure, enabling seamless connections between cells regardless of orientation. Additionally, each cell is battery-powered, allowing it to operate independently and retain its state even when disconnected from the collective. To demonstrate the platform's applicability, we present a novel rotation-invariant NCA model for isotropic shape classification. The proposed system provides a robust foundation for exploring the physical realization of NCA, with potential applications in distributed robotic systems and self-organizing structures. Our implementation, including hardware, software code, a simulator, and a video, is openly shared at: https://github.com/dwoiwode/embedded_nca
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