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<p>We propose a network architecture for electronic skin with an extensive sensor array—crucial for enabling robots to perceive their environment and interact effectively with humans. Fault tolerance is essential for electronic skins on robot exteriors. Although self-healing electronic skins targeting minor damages are studied using material-based approaches, substantial damages such as severe cuts necessitate re-establishing communication pathways, traditionally performed with high-functionality microprocessor sensor nodes. However, this method is costly, increases latency, and boosts power usage, limiting scalability for large, nuanced sensation-mimicking sensor arrays. Our proposed system features sensor nodes consisting of only a few dozen logic circuits, enabling them to autonomously reconstruct reading pathways. These nodes can adapt to topological changes within the sensor network caused by disconnections and reconnections. Testing confirms rapid reading times of only a few microseconds and power consumption of 1.88 μW/node at a 1 kHz sampling rate. This advancement significantly boosts robots’ collaborative potential with humans.</p>

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Self-rerouting sensor network for electronic skin resilient to severe damage

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年1月30日

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  • T. Ozaki,
  • N. Ohta,
  • M. Fujiyoshi

摘要

We propose a network architecture for electronic skin with an extensive sensor array—crucial for enabling robots to perceive their environment and interact effectively with humans. Fault tolerance is essential for electronic skins on robot exteriors. Although self-healing electronic skins targeting minor damages are studied using material-based approaches, substantial damages such as severe cuts necessitate re-establishing communication pathways, traditionally performed with high-functionality microprocessor sensor nodes. However, this method is costly, increases latency, and boosts power usage, limiting scalability for large, nuanced sensation-mimicking sensor arrays. Our proposed system features sensor nodes consisting of only a few dozen logic circuits, enabling them to autonomously reconstruct reading pathways. These nodes can adapt to topological changes within the sensor network caused by disconnections and reconnections. Testing confirms rapid reading times of only a few microseconds and power consumption of 1.88 μW/node at a 1 kHz sampling rate. This advancement significantly boosts robots’ collaborative potential with humans.