Fingertip Non-Contact Optoacoustic Sensor for Near-Distance Ranging and Thickness Differentiation for Robotic Grasping*
Fingertip Non-Contact Optoacoustic Sensor for Near-Distance Ranging and Thickness Differentiation for Robotic Grasping*
复制标题
用于机器人抓取的近距离测距和厚度区分的指尖非接触式光声传感器*
DOI：
--
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Jun Zou
中科院分区：
文献类型：
--
作者：
Cheng Fang;Di Wang;Dezhen Song;Jun Zou
我们报告的可行性研究，一种新的光声传感器的近距离测距和材料厚度分类的机器人抓。它基于光声效应，其中聚焦激光脉冲用于在目标中产生宽带超声信号。光声传感器具有更小的光学焦斑，横向分辨率达到93 μm，是相同条件下超声脉冲回波测距的6倍。为了有效地接收宽带光声信号，研制了一种新型多模宽带PZT（锆钛酸铅）换能器。接收光声信号的低频和高频分量的能力增强了材料感测能力，这使得它不仅有希望确定材料类型，而且还可以确定亚表面结构。为了演示，收集了不同厚度的硬材料和软材料的光声光谱。设计了一种基于光声光谱的Bag-of-SFA-Symbols（BOSS）分类器来进行初级材料分类和厚度分类。材料/厚度分类的准确率分别达到≥ 99%和≥ 94%，表明了光声传感器区分不同厚度固体材料的可行性。
We report the feasibility study of a new optoacoustic sensor for both near-distance ranging and material thickness classification for robotic grasping. It is based on the optoacoustic effect where focused laser pulses are used to generate wideband ultrasound signals in the target. With a much smaller optical focal spot, the optoacoustic sensor achieves a lateral resolution of 93 μm, which is six times higher than ultrasound pulse-echo ranging under the same condition. A new multi-mode wideband PZT (lead zirconate titanate) transducer is built to properly receive the wideband optoacoustic signal. The ability to receive both low- and high-frequency components of the optoacoustic signal enhances the material sensing capability, which makes it promising to determine not only material type but also the sub-surface structures. For demonstration, optoacoustic spectra are collected from hard and soft materials with different thickness. A Bag-of-SFA-Symbols (BOSS) classifier is designed to perform primary material and then thickness classification based on the optoacoustic spectra. The accuracy of material / thickness classification reaches ≥ 99% and ≥ 94%, respectively, which shows the feasibility of differentiating solid materials with different thickness by the optoacoustic sensor.
用于机器人抓取的指尖非接触式材料识别和近距离测距
DOI：
10.1109/icra.2019.8793922
发表时间：
2019
期刊：
2019 International Conference on Robotics and Automation (ICRA
影响因子：
--
作者：
Fang, Cheng;Wang, Di;Song, Dezhen;Zou, Jun
通讯作者：
Zou, Jun