Grasping force estimation detecting slip by tactile sensor adopting machine learning techniques

Abdul Md Mazid, A. B M Shawkat Ali

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

4 Citations (Scopus)


Adequate grasping force estimation and slip detection is a vital problem in wider applications of robots and manipulators in industries as well as in our everyday life. In this paper, a new methodology for slip detection during grasping by robot grippers/end-effectors using tactile sensor has been presented. During the object slippage, the tactile sensor in touch with the object surface travels along the peaks and valleys of surface texture of the object which creates vibratory motions in the tactile. A newly developed mathematical model is used to compute the scattered energy of vibrations, which contains parameters of surface texture geometry as well as trial grasping force, and other relevant parameters. Using the scattered energy of vibrations predicted by soft computing techniques, an attempt to instantly estimate the adequate grasping force has been reasonably successful. Surface texture data, for experimental estimation of grasping force, were collected from a huge number of machined specimens and were used to build four different machine learning estimation techniques. Experimental results using Linear Regression (LR), Simple Linear Regression (SLR), Pace Regression (PR) and Support Vector Machine (SVM) demonstrate a relatively better technique for industrial applications.

Original languageEnglish
Title of host publication2008 IEEE Region 10 Conference, TENCON 2008
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages6
ISBN (Print)1424424089, 9781424424085
Publication statusPublished - 2008
Event2008 IEEE Region 10 Conference, TENCON 2008 - Hyderabad, India
Duration: 19 Nov 200821 Nov 2008


Other2008 IEEE Region 10 Conference, TENCON 2008


  • Intelligent grasping
  • Slip detection
  • Support vector machine
  • Surface roughness
  • Tactile sensor


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