用神经元细胞自动机实现分布式机械臂系统的去中心化传感
Neural Cellular Automata for Decentralized Sensing using a Soft Inductive Sensor Array for Distributed Manipulator Systems
- 基于神经元细胞自动机，通过局部交互实现去中心化感知
- 在0.24倍传感器间距下精准估计物体位置
- 适合需要高鲁棒性与可扩展性的分布式系统
在分布式机械臂系统（DMS）中，去中心化能提升系统鲁棒性并促进可扩展性，但现有系统多依赖单摄像头等集中式感知方式，存在可靠性风险且限制系统规模。本文提出一种基于神经元细胞自动机（NCA）的去中心化感知方法，并设计了一种新型分布式电感传感器板。通过局部计算与交互，该系统可估计全局物体属性（如几何中心）。实验表明，基于NCA的感知网络可在0.24倍传感器间距精度下准确估计物体位置，在传感器故障和噪声干扰下仍保持稳定，且可无缝扩展至不同规模网络。结果证明，局部去中心化计算有望实现可扩展、容错强、抗噪的物体属性估计。
原文摘要 · Abstract (English)
In Distributed Manipulator Systems (DMS), decentralization is a highly desirable property as it promotes robustness and facilitates scalability by distributing computational burden and eliminating singular points of failure. However, current DMS typically utilize a centralized approach to sensing, such as single-camera computer vision systems. This centralization poses a risk to system reliability and offers a significant limiting factor to system size. In this work, we introduce a decentralized approach for sensing and in a Distributed Manipulator Systems using Neural Cellular Automata (NCA). Demonstrating a decentralized sensing in a hardware implementation, we present a novel inductive sensor board designed for distributed sensing and evaluate its ability to estimate global object properties, such as the geometric center, through local interactions and computations. Experiments demonstrate that NCA-based sensing networks accurately estimate object position at 0.24 times the inter sensor distance. They maintain resilience under sensor faults and noise, and scale seamlessly across varying network sizes. These findings underscore the potential of local, decentralized computations to enable scalable, fault-tolerant, and noise-resilient object property estimation in DMS
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