仿生手指的触觉感知系统设计及性能

Design and performance of a tactile sensing system for a bionic finger

机器人的类人能力离不开完善的感知系统,而触觉感知系统能极大地扩展机器人的应用范围。该文以人手指为仿生对象,基于液体压强传导原理研制了一种具有柔性外壳、指甲、指骨、液体、压敏元件和温度敏感元件的仿生手指,并研究了仿生手指的触觉感知能力。结果表明:仿生手指的触摸压力曲线变化率和接触温度曲线变化率能够反映物体的硬度和导热性能,表明仿生手指具有硬度和温度感知能力。由仿生手指触摸振动信号提取的峰值均值和平均功率特征值能够表征织物粗糙感,由仿生手指触摸振动信号频谱图提取的主频率和功率谱重心频率特征值能够表征织物细密感,表明仿生手指具有纹理粗糙度和细密度的感知功能。基于支持向量机模型,使用仿生手指触摸振动信号的峰值均值、平均功率、主频率、功率谱重心频率及6个频段强度作为特征参数,进行织物表面纹理识别,平均识别准确率为92.8%,高于人主观分类的平均识别准确率(88.8%),表明仿生手指能够对织物表面纹理进行有效识别与分类,且优于人主观分类识别织物能力。研究成果可为触感智能机器人、触肤产品质感量化评定等提供技术支持和理论支撑。

[Objective]The human-like capabilities of robots are linked to their well-established perception systems.Tactile perception enables the robot to perceive the texture and grain of objects through touch.Robots equipped with tactile perception can grasp and manipulate objects more precisely and detect the characteristics and attributes of objects,which enhances their perceptual and cognitive abilities.Tactile perception provides robots with important advantages,helps them achieve human-like capabilities,and promotes the continuous development and innovation of robotic technology.[Methods]Herein,a bionic finger with a flexible shell,nail,finger bone,liquid,pressure-sensitive element,and temperature-sensitive element was developed based on the principle of liquid pressure conduction.The hardness,temperature,and texture sensing ability of the bionic finger were investigated;the tactile feature parameters of the bionic finger touching the textured surface were extracted;and classification and recognition of the fabric surface texture were achieved by the bionic finger using the support vector machine algorithm.[Results]Results revealed that when the bionic finger applied pressure to three different materials,the rates of change in the pressure curve were in descending order:L_(wo)>L_(fo)>L_(sp).These results were consistent with the hardness of the materials tested.The steepness of the temperature change curves obtained by the bionic finger touching the three materials was in descending order:T_(ss)>T_(pb)>T_(wo),which aligned with the thermal conductivities of the materials.As the roughness of the fabric surface increased,the peak average value and average power increased.Thus,a positive correlation existed between the peak average and average power values and roughness,namely the higher the peak average and average power,the higher the roughness of the fabric.With increasing fineness of the fabric surface,the dominant frequency and the spectral centroid increased,resulting in an enhanced sense of fineness.A significant positive corelation existed between the sense of fineness and both the dominant frequency and the spectral centroid.The larger the dominant frequency and the spectral centroid,the higher the sense of fabric fineness.The average accuracy of fabric surface texture recognition using the bionic finger and support vector machine method,based on the peak average,average power,dominant frequency,spectral centroid,and six frequency band feature intensities,was 92.8%,which was higher than the average human subjective recognition accuracy of 88.8%.[Conclusions]The rate of change of the touch pressure curve and the temperature curve of the bionic finger can indicate the softness and thermal conductivity of an object,indicating that the bionic finger has the ability to perceive hardness and temperature.The peak average and average power of the bionic finger extracted from the touch vibration signal can characterize fabric roughness,while the dominant frequency and the spectral centroid can characterize fabric fineness,indicating the ability of the bionic finger to perceive roughness and fineness.The average recognition accuracy of the bionic finger is higher than that of human subjective recognition,indicating the efficient and superior capability of the bionic finger to recognize and classify textile surface textures compared to human judgment.