ニューラルセルオートマタによる分散センシング：ソフト誘導センサアレイを用いた分散マニピュレータシステム
Neural Cellular Automata for Decentralized Sensing using a Soft Inductive Sensor Array for Distributed Manipulator Systems
分散マニピュレータシステムにおいて、ニューラルセルオートマタと新規の誘導センサボードを用いて、局所的な相互作用から物体の幾何中心を推定する分散センシング手法を提案し、その精度と耐故障性・スケーラビリティを実証した。
著者: Bailey Dacre, Nicolas Bessone, Matteo Lo Preti, Diana Cafiso, Rodrigo Moreno, Andrés Faíña, Lucia Beccai
分類: cs.RO, cs.LG
原文アブストラクト
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
