arXiv:2605.01434cs.RO2026-05

用移位寄存器实现多传感器高速读取,让机械手更灵敏。

High-Speed, Scalable Sensor Readout for Dexterous Robotic Hands via Shift-Register Multiplexing

论文配图:High-Speed, Scalable Sensor Readout for Dexterous Robotic Hands via Shift-Register Multiplexing
图 1 · 摘自论文原文
  • 基于移位寄存器的串行读取架构,仅用三根线扩展传感器模块
  • 20通道同步采样达1kHz,触觉传感实时精度93.4%
  • 适合需要高密度传感的灵巧机械手系统集成

灵巧机器人手需要在多个自由度上实现高速多模态传感,但现有读出架构常在传感器数量、布线复杂度和采样带宽间存在权衡。本文提出一种基于串入并出(SIPO)移位寄存器原理的可扩展模拟传感器读出架构。该架构支持异构模拟输出传感器的灵活集成,仅需三根信号线即可实现模块级扩展,并具备快速可配置采样能力。我们在一个腱驱动机器人手上验证了该方法,集成了16个关节传感器模块和一个四通道触觉传感器模块,实现了20个传感器通道在1 kHz全扫描速率下的稳定采集,最高可达1.5 kHz。关节传感器表征显示最大斜率绝对百分比误差(APE)为0.446%,估测误差小于1度,表明读出系统未显著降低传感性能。触觉方面,基于LSTM的模型在力估计中达到0.125 N RMSE,五类接触位置分类准确率达93.4%,并实现实时推理(1 kHz)。系统级实验表明,关节传感器反馈比电机估计算法更准确,触觉传感器可实现响应式力估计。该架构为灵巧操作的全传感机器人手提供了实用解决方案。

原文摘要 · Abstract (English)

Dexterous robotic hands require high-speed multimodal sensing across many degrees of freedom, yet existing readout architectures often impose trade-offs between sensor count, wiring complexity, and sampling bandwidth. This paper presents a scalable analog sensor readout architecture based on a serial-in parallel-out (SIPO) shift-register principle. The proposed architecture supports versatile integration of heterogeneous analog-output sensors, scalable expansion using only three signal lines between sensor modules, and fast, configurable sampling. We validate the approach on a tendon-driven robotic hand integrating 16 joint sensor modules and one four-channel tactile sensor module, enabling acquisition of 20 sensor channels at a full-scan rate of 1 kHz, with stable operation up to 1.5 kHz. Joint sensor characterization showed a maximum slope absolute percentage error (APE) of 0.446% and sub-degree estimation error, indicating that the proposed readout system does not significantly degrade sensing performance. For tactile sensing, LSTM-based models achieved an RMSE of 0.125 N for force estimation and 93.4% accuracy for five-class contact-location classification, and were deployed for real-time inference at 1 kHz. System-level experiments showed that the joint sensors provide more accurate feedback than motor-based estimation during interaction, while the tactile sensor enables responsive force estimation in contact. The proposed architecture offers a practical path toward fully sensorized robotic hands for dexterous manipulation.

机器人手传感器读取高速采样触觉感知

Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品;中文卡片由大模型生成,请以原文为准。