Neural Cellular Automata for Decentralized Sensing using a Soft Inductive Sensor Array for Distributed Manipulator Systems

TL;DR

This paper presents a decentralized sensing method using Neural Cellular Automata and a novel inductive sensor array for distributed manipulator systems, enhancing scalability, fault tolerance, and noise resilience.

Contribution

It introduces a hardware implementation of decentralized sensing with NCA in DMS, enabling scalable and robust object property estimation without centralized control.

Findings

Accurately estimates object position at 0.24 times the inter sensor distance

Maintains performance under sensor faults and noise

Scales seamlessly across different network sizes

Abstract

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