用CNN识别塑料移液管倾斜角度，提升远程操作精度
TiltXter: CNN-based Electro-tactile Rendering of Tilt Angle for Telemanipulation of Pasteur Pipettes
- 用CNN分析传感器数据，实时生成对应倾斜角度的电触觉刺激
- 用户倾斜识别准确率从23.13%提升至57.9%，操作成功率达92.18%
- 适合远程手术、精密操作等需高触觉反馈的场景
柔性物体在机械臂抓取过程中形状变化剧烈，导致其对齐状态感知模糊，进而引发机器人定位误差和远程操作失误。清晰的触觉反馈对提升操作者精度与灵巧性至关重要。本文提出一种基于卷积神经网络（CNN）的远程操控系统，用于塑料移液管操作，采用Force Dimension Omega.7力反馈设备，配备两个电刺激阵列与两个嵌入式2指Robotiq夹持器的触觉传感器阵列。该方法通过CNN识别柔性物体的倾斜角度，并据此生成电触觉模式，向用户提供反馈。实验表明，使用CNN算法后，用户对倾斜角度的识别准确率由原始数据的23.13%提升至57.9%，远程操作成功率从53.12%提升至92.18%。
原文摘要 · Abstract (English)
The shape of deformable objects can change drastically during grasping by robotic grippers, causing an ambiguous perception of their alignment and hence resulting in errors in robot positioning and telemanipulation. Rendering clear tactile patterns is fundamental to increasing users' precision and dexterity through tactile haptic feedback during telemanipulation. Therefore, different methods have to be studied to decode the sensors' data into haptic stimuli. This work presents a telemanipulation system for plastic pipettes that consists of a Force Dimension Omega.7 haptic interface endowed with two electro-stimulation arrays and two tactile sensor arrays embedded in the 2-finger Robotiq gripper. We propose a novel approach based on convolutional neural networks (CNN) to detect the tilt of deformable objects. The CNN generates a tactile pattern based on recognized tilt data to render further electro-tactile stimuli provided to the user during the telemanipulation. The study has shown that using the CNN algorithm, tilt recognition by users increased from 23.13\% with the downsized data to 57.9%, and the success rate during teleoperation increased from 53.12% using the downsized data to 92.18% using the tactile patterns generated by the CNN.
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