首页|基于纹理图像和用户动作信息的纹理触觉再现方法研究

基于纹理图像和用户动作信息的纹理触觉再现方法研究

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近年来,对虚拟表面纹理进行工具介导的振动触觉再现已经成为触觉领域的一个研究热点.针对已有的纹理触觉渲染方法还存在适用范围窄、泛化能力弱、交互真实感低等问题,以改进后的MelGAN为基本网络,构建了一种新的纹理触觉渲染模型.该模型以纹理图像和用户的实时动作信息为输入,在能够高保真地生成振动触觉信号的同时,对常见纹理图像拥有更好的泛化能力.此外,设计了一款具有动作信息实时采集和振动触觉表达功能的笔式装置.在采集了数据库之外的真实纹理表面的振动触觉信号后,比较了提出的模型与已有方法在信号生成方面的性能差异.结果表明,模型实现了最低的均方根误差(0.173),验证了其对未建模纹理进行触觉渲染的能力.最后,使用笔式装置进行了两项用户实验.平均 6.01 的主观相似度评分说明,即使是数据库之外的新纹理,模型也能为用户提供较高的纹理交互真实感.
Research on texture haptic display method based on texture image and user action information
In recent years,tool-medicated haptic feedback for virtual surface textures has become a hot topic in the field of haptics.In view of the problems of narrow application range,weak generalization ability and low interactive realism of the existing haptic texture rendering methods,a new texture haptic rendering model is constructed in this paper based on the improved MelGAN.This model takes texture image and real-time user action information as inputs,which can generate vibrotactile signals with high fidelity and has better generalization ability for common texture images.Furthermore,this paper designs a pen-type device with real-time action data acquisition and vibrotactile expression.After collecting vibrotactile signals from real texture surfaces outside the database,this paper compared the performance differences between the proposed model and existing methods in signal generation.The results indicate that the model in this paper achieved the lowest root mean square error(0.173),verifying its ability to perform haptic rendering on unmodeled textures.Finally,this paper conducted two user experiments using a pen-type device.A subjective similarity score of 6.01 on average indicates that even for new textures outside of the database,our model can provide users with a high level of texture interaction realism.

texture haptic renderingdeep learningaction informationpen-based interactionvibrotactile feedback

陈大鹏、陈庚、刘佳、方映平、张运杰

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南京信息工程大学自动化学院 南京 210044

江苏省智能气象探测机器人工程研究中心 南京 210044

江苏省大气环境与装备技术协同创新中心 南京 210044

纹理触觉渲染 深度学习 动作信息 笔式交互 振动触觉反馈

国家自然科学基金江苏省自然科学基金青年基金江苏产业前瞻与关键核心技术重点项目江苏省研究生科研与实践创新计划项目江苏省研究生科研与实践创新计划项目江苏省研究生科研与实践创新计划项目

62003169BK20200823BE2020006-2SJCX22_0348KYCX22_1202SJCX22_0351

2024

10.19650/j.cnki.cjsi.J2311948

仪器仪表学报
中国仪器仪表学会

仪器仪表学报

CSTPCD北大核心
影响因子：2.372
ISSN：0254-3087
年,卷(期)：2024.45(4)