Research on Robotic Grasping Object Hardness Perception Based on Tactile Sensing

doi:10.3901/JME.2021.23.012

Abstract

Abstract: The perception of object hardness is of great significance for robots to perform fine manipulation task. Aiming at the problems of poor system robustness and easy damage to objects caused by complex sensing signals and large object compression in object hardness perception, a method of robotic grasping object hardness perception based on tactile sensing is proposed. Pressure sequences are obtained by the flexible tactile sensors when the two fingers gripper slightly squeezes the object. The pressure sequence signals are then polynomial processed to obtain nonlinear characteristics and then input into the Adaboost algorithm which is based on the decision tree to realize online grasped object hardness perception. The Adaboost algorithm is compared with other algorithms, and the hardness perception experiment for novel objects is carried out. Experimental results show that the proposed method can accurately identify the hardness of different grasped objects.

Key words: robotic grasping, hardness perception, tactile, adaboost algorithm

CLC Number:

TG156

ZHANG Xianmin, WANG Haonan, HUANG Yanjiang. Research on Robotic Grasping Object Hardness Perception Based on Tactile Sensing[J]. Journal of Mechanical Engineering, 2021, 57(23): 12-20.

References

[1] LEDERMAN S J,KLATZKY R L. Extracting object properties through haptic exploration[J]. Acta Psychologica,1993,84(1):29-40.
[2] KONSTANTINOVA J,COTUGNO G,DASGUPTA P, et al. Palpation force modulation strategies to identify hard regions in soft tissue organs[J]. PLOS ONE,2017,12(2):e0171706.
[3] CARRELLA A,BRENNAN M J,WATERS T P. Static analysis of a passive vibration isolator with quasi-zero-stiffness characteristic[J]. Journal of Sound and Vibration,2007,301(3):678-689.
[4] YAO Bitao,ZHOU Zude,WANG Lihui,et al. Sensorless and adaptive admittance control of industrial robot in physical human-robot interaction[J]. Robotics and Computer-Integrated Manufacturing,2018,51:158-168.
[5] PONRAJ G,KIRTHIKA S K,THAKOR N V,et al. Development of flexible fabric based tactile sensor for closed loop control of soft robotic actuator[C]//201713th IEEE Conference on Automation Science and Engineering (CASE),China:IEEE,2017:1451-1456.
[6] BEKIROGLU Y,KRAGIC D,KYRKI V. Learning grasp stability based on tactile data and HMMs[C]//19th International Symposium in Robot and Human Interactive Communication,Viareggio:IEEE,2010:132-137.
[7] SATO K,KAMIYAMA K,KAWAKAMI N,et al. Finger-shaped gelforce:sensor for measuring surface traction fields for robotic hand[J]. IEEE Transactions on Haptics,2009,3(1):37-47.
[8] YUAN Wenzhen,DONG Siyuan,ADELSON E H. Gelsight:High-resolution robot tactile sensors for estimating geometry and force[J]. Sensors,2017, 17(12):2762.
[9] PARK J,OH Y,TAN H Z. Effect of cutaneous feedback on the perceived hardness of a virtual object[J]. IEEE Transactions on Haptics,2018,11(4):518-530.
[10] XUE Lingyun,ZHANG Dongfang,CHEN Qingguang,et al. Tactile sensor of hardness recognition based on magnetic anomaly detection[C]//Proceedings of the Young Scientists Forum 2017. International Society for Optics and Photonics,Shanghai:SPIE,2018:107102J.
[11] YUAN Wenzhen,SRINIVASAN M A,ADELSON E H. Estimating object hardness with a GelSight touch sensor[C]//IEEE/RSJ International Conference on Intelligent Robots & Systems. Daejeon:IEEE,2016:208-215.
[12] CHU V,MCMAHON I,RIANO L,et al. Robotic learning of haptic adjectives through physical interaction[J]. Robotics and Autonomous Systems,2015,63:279-292.
[13] DRIMUS A,KOOTSTRA G,BILBERG A,et al. Classification of rigid and deformable objects using a novel tactile sensor[C]//201115th International Conference on Advanced Robotics (ICAR). Tallinn:IEEE,2011:427-434.
[14] BANDYOPADHYAYA I,BABU D,KUMAR A,et al. Tactile sensing based softness classification using machine learning[C]//2014 IEEE International Advance Computing Conference (IACC),Gurgaon:IEEE,2014:1231-1236.
[15] 汪礼超. 基于机械手触觉信息的物体软硬属性识别[D].杭州:浙江大学,2016. WANG Lichao. Recognition of object's softness and hardness based on tactile information of robotic hand[D]. Hangzhou:Zhejiang University,2016.
[16] YUAN Wenzhen,ZHU Chenzhuo,ANDREW O,et al. Shape-independent hardness estimation using deep learning and a gelsight tactile sensor[C]//2017 IEEE International Conference on Robotics and Automation (ICRA),Singapore:IEEE,2017:951-958.
[17] 惠文珊,李会军,陈萌,等. 基于CNN-LSTM的机器人触觉识别与自适应抓取控制[J]. 仪器仪表学报,2019,40(1):211-218. HUI Wenshan,LI Huijun,CHEN Meng,et al. Robotic tactile recognition and adaptive grasping control based on CNN-LSTM[J]. Chinese Journal of Scientific Instrument,2019,40(1):211-218.
[18] BHATTACHARJEE T,REHG J M,KEMP C C. Inferring object properties with a tactile-sensing array given varying joint stiffness and velocity[J]. International Journal of Humanoid Robotics,2018,15(1):1750024.
[19] 张阳阳,黄英,刘月,等. 基于多传感器信息融合的人类抓握特征学习及物体识别[J]. 机器人,2020,42(3):267-277. ZHANG Yangyang,HUANG Ying,LIU Yue,et al. Human grasp feature learning and object recognition based on multi-sensor information fusion[J]. Robot,2020,42(3):267-277.
[20] RICHARDSON B A,KUCHENBECKER K J. Learning to predict perceptual distributions of haptic adjectives[J]. Frontiers in Neurorobotics,2020,13:116.
[21] QIAN Xiaoliang,LI Erkai,ZHANG Jianwei,et al. Hardness recognition of robotic forearm based on semi-supervised generative adversarial networks[J]. Frontiers in Neurorobotics,2019,13:73.
[22] BADER F,RAHIMIFARD S. Challenges for industrial robot applications in food manufacturing[C]//Proceedings of the 2nd International Symposium on Computer Science and Intelligent Control,New York:Association for Computing Machinery,2018:1-8.
[23] BLANES C,MELLADO M,ORTIZ C,et al. Technologies for robot grippers in pick and place operations for fresh fruits and vegetables[J]. Spanish Journal of Agricultural Research,2011,4:1130-1141.
[24] BLUMER A,EHRENFEUCHT A,HAUSSLER D,et al. Occam's razor[J]. Information Processing Letters,1987,24(6):377-380.
[25] HASTIE T,ROSSET S,ZHU J,et al. Multi-class adaboost[J]. Statistics and its Interface,2009,2(3):349-360.
[26] TENZER Y,JENTOFT L P,HOWE R D. Inexpensive and easily customized tactile array sensors using MEMS barometers chips[J]. IEEE R&A Magazine,2012,21(c):2013.
[27] 汪延成,鲁映彤,丁文,等. 柔性触觉传感器的三维打印制造技术研究进展[J]. 机械工程学报,2020,56(19):252-265. WANG Yancheng,LU Yingtong,DING Wen,et al. Recent progress on three-dimensional printing processes to fabricate flexible tactile sensors[J]. Journal of Mechanical Engineering,2020,56(19):252-265.
[28] 罗毅辉,彭倩倩,朱宇超,等. 喷印柔性压力传感器试验研究[J]. 机械工程学报,2019,55(11):90-97. LUO Yihui,PENG Qianqian,ZHU Yuchao,et al. Experimental study of flexible pressure sensor via direct writing[J]. Journal of Mechanical Engineering,2019,55(11):90-97.