用气道网络让软体指尖感知触觉,简单耐用适合机器人操作
Fluidically Innervated Lattices Make Versatile and Durable Tactile Sensors
- 用3D打印弹性骨架加密闭气道,通过压力变化感知触觉
- 能准确预测接触位置和力度,支持高冲击反复使用
- 适合需要柔韧感知的机器人抓取与环境探索任务
触觉感知在机器人应对动态非结构化环境方面起着基础作用,尤其在精细物体操作、表面探测和人机交互中至关重要。本文介绍一种集成触觉传感功能的被动式软体机械手指,采用3D打印的弹性体晶格结构并嵌入空气通道实现。该传感方法称为流体神经化(fluidic innervation),通过检测密封气道内的压力变化,将晶格转化为触觉传感器,提供一种简单且坚固的机器人触觉传感解决方案。与依赖复杂材料或结构的传统方法不同,流体神经化实现了简单、可扩展的单材料制造流程。我们表征了传感器响应特性,建立了几何模型以估算指端位移,并训练神经网络以准确预测接触位置和接触力。此外,将该指尖与阻抗控制器集成,模拟弹簧行为,通过触觉反馈实现环境探索能力,并验证其在高冲击和循环加载条件下的耐久性。该触觉传感技术在简便性、适应性和耐久性方面具有优势,为多功能机器人操作开辟了新路径。
原文摘要 · Abstract (English)
Tactile sensing plays a fundamental role in enabling robots to navigate dynamic and unstructured environments, particularly in applications such as delicate object manipulation, surface exploration, and human-robot interaction. In this paper, we introduce a passive soft robotic fingertip with integrated tactile sensing, fabricated using a 3D-printed elastomer lattice with embedded air channels. This sensorization approach, termed fluidic innervation, transforms the lattice into a tactile sensor by detecting pressure changes within sealed air channels, providing a simple yet robust solution to tactile sensing in robotics. Unlike conventional methods that rely on complex materials or designs, fluidic innervation offers a simple, scalable, single-material fabrication process. We characterize the sensors' response, develop a geometric model to estimate tip displacement, and train a neural network to accurately predict contact location and contact force. Additionally, we integrate the fingertip with an admittance controller to emulate spring-like behavior, demonstrate its capability for environment exploration through tactile feedback, and validate its durability under high impact and cyclic loading conditions. This tactile sensing technique offers advantages in terms of simplicity, adaptability, and durability and opens up new opportunities for versatile robotic manipulation.
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