Event Based Intelligent Slip Sensor For Manufacturing Automation

  • Aamna Alali

Student thesis: Master's Thesis


In the past few years, several institutions applied effort on developing a new robotic grippers that capable of sensing. A neuromorphic sensors take their inspiration from human behaviors which can be applied in manufacturing industry. The aim of this project is to develop a slip sensing approach based on dynamic and active-pixel vision sensor, which can be employed to estimate the grasping force of a gripper in order to achieve stable manipulation. Dynamic and active-pixel vision sensor (DAVIS) is a special camera characterized by high speed, low computational cost and low energy consumption. DAVIS is placed behind a transparent deformable fingertip in order to constantly monitor the contact area and estimate the slip margin. The acquired data from DAVIS is processed in different stages. Firstly, the features of the data are extracted by using morphology operations. secondly, the developed algorithms were employed to detect the moment of incipient slip which synchronized with tactile sensor for validation purpose. Followed by reconstruction of the contact area. The information is utilized for force magnitude and direction estimation. The results of estimated forces using the proposed approach are compared with tactile sensor readings show a good agreement.
Date of AwardMay 2018
Original languageAmerican English


  • Incipient Slip; Tactile Sensor; Dynamic Vision Sensor (DVS).

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