Analyzing of flexible gripper by computational intelligence approach

Abstract Adaptive grippers should be able to detect and recognize grasping objects. To be able to do it control algorithm need to be established to control gripper tasks. Compliant underactuated mechanisms with passive behavior can be used for modelling of adaptive robotic fingers. Undearactuation is a feature which allows fully adaptability of robotic fingers for different objects. In this study gripper with two fingers was established. Finite element method (FEM) procedure was used to optimize the gripper structural topology. Kinetostatic model of the underactuated finger mechanism was analyzed. This design of the gripper has embedded sensors as part of its structure. The use of embedded sensors in a robot gripper gives the control system the ability to control input displacement of the gripper and to recognize specific shapes of the grasping objects. Since the conventional control strategy is a very challenging task, soft computing based controllers are considered as potential candidates for such an application. The sensors could be used for grasping shape detection. Given that the contact forces of the finger depend on contact position of the finger and object, it is suitable to make a prediction model for the contact forces in function of contact positions of the finger and grasping objects. The prediction of the contact forces was established by using a soft computing (computational intelligence) approach. To perform the contact forces estimation adaptive neuro-fuzzy (ANFIS) methodology was used. FEM simulations were performed in order to acquire experimental data for ANFIS training. The main goal was to apply ANFIS network in order to find correlation between sensors’ stresses and finger contact forces. Afterwards ANFIS results were compared with benchmark models (extreme learning machine (ELM), extreme learning machine with discrete wavelet algorithm (ELM-WAVELET), support vector machines (SVM), support vector machines with discrete wavelet algorithm (SVM-WAVELET), genetic programming (GP) and artificial neural network (ANN)). The reliability of these computational models was analyzed based on simulation results.

[1]  Okyay Kaynak,et al.  Adaptive neuro-fuzzy inference system based autonomous flight control of unmanned air vehicles , 2007, Expert Syst. Appl..

[2]  Dalibor Petkovic,et al.  Adaptive neuro-fuzzy estimation of autonomic nervous system parameters effect on heart rate variability , 2011, Neural Computing and Applications.

[3]  Kim Doang Nguyen,et al.  Adaptive control of underactuated robots with unmodeled dynamics , 2015, Robotics Auton. Syst..

[4]  Fulei Chu,et al.  Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography , 2004 .

[5]  Antonio Morales,et al.  Model of tactile sensors using soft contacts and its application in robot grasping simulation , 2013, Robotics Auton. Syst..

[6]  Wahida Banu,et al.  Identification and Control of Nonlinear Systems using Soft Computing Techniques , 2011 .

[7]  Zoe Doulgeri,et al.  Force position control for a robot finger with a soft tip and kinematic uncertainties , 2007, Robotics Auton. Syst..

[8]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[9]  Ismael Payo,et al.  Force control of a very lightweight single-link flexible arm based on coupling torque feedback , 2009 .

[10]  Robert M. Panas,et al.  Design of Flexure-based Precision Transmission Mechanisms using Screw Theory , 2011 .

[11]  Babak Rezaee,et al.  Application of adaptive neuro-fuzzy inference system for solubility prediction of carbon dioxide in polymers , 2009, Expert Syst. Appl..

[12]  Hiroki Tamura,et al.  Batch type local search-based adaptive neuro-fuzzy inference system (ANFIS) with self-feedbacks for time-series prediction , 2009, Neurocomputing.

[13]  V. Galabov,et al.  Synthesis of an adaptive gripper , 2014 .

[14]  F ROSENBLATT,et al.  The perceptron: a probabilistic model for information storage and organization in the brain. , 1958, Psychological review.

[15]  Chiharu Ishii,et al.  Design and Control of a Robotic Forceps Manipulator with Screw-Drive Bending Mechanism and Extension of Its Motion Space , 2013 .

[16]  R. A. Russell,et al.  Object location and recognition using whisker sensors , 2003 .

[17]  Miomir Vukobratovic,et al.  The Application of Connectionist Structures to Learning Impedance Control in Robotic Contact Tasks , 2004, Applied Intelligence.

[18]  Ruxandra Botez,et al.  Adaptive neuro-fuzzy inference system-based controllers for smart material actuator modelling , 2009 .

[19]  Dalibor Petković,et al.  Applications and Adaptive Neuro-Fuzzy Estimation of Conductive Silicone Rubber Properties , 2012 .

[20]  Masahiro Ohka,et al.  Object Exploration Using a Three-Axis Tactile Sensing Information , 2011 .

[21]  Yan Leng,et al.  Combining active learning and semi-supervised learning to construct SVM classifier , 2013, Knowl. Based Syst..

[22]  Masahiro Ohka,et al.  Grasping strategy and control algorithm of two robotic fingers equipped with optical three-axis tactile sensors , 2012 .

[23]  Massimo Bergamasco,et al.  A sensorized robot gripper , 1988, Robotics Auton. Syst..

[24]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

[25]  Lotfi A. Zadeh,et al.  Fuzzy logic, neural networks, and soft computing , 1993, CACM.

[26]  Jürgen Sturm,et al.  Tactile object class and internal state recognition for mobile manipulation , 2010, 2010 IEEE International Conference on Robotics and Automation.

[27]  Hendrik Van Brussel,et al.  A sensory controlled gripper system , 1990, Robotics Auton. Syst..

[28]  Eriola Betiku,et al.  Modeling and optimization of bioethanol production from breadfruit starch hydrolyzate vis-à-vis response surface methodology and artificial neural network , 2015 .

[29]  Hongming Zhou,et al.  Extreme Learning Machine for Regression and Multiclass Classification , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[30]  M. Ali Akcayol.  Application of adaptive neuro-fuzzy controller for SRM , 2004 .

[31]  Dalibor Petkovic,et al.  Adaptive neuro fuzzy estimation of underactuated robotic gripper contact forces , 2013, Expert Syst. Appl..

[32]  Nicola J. Ferrier,et al.  Tongue-based electrotactile feedback to perceive objects grasped by a robotic manipulator: preliminary results , 2001, 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[33]  Richard M. Crowder,et al.  Adaptive neurofuzzy control of a robotic gripper with on-line machine learning , 2004, Robotics Auton. Syst..

[34]  Shuanggen Jin,et al.  An adjoint-based FEM optimization of coseismic displacements following the 2011 Tohoku earthquake: new insights for the limits of the upper plate rebound , 2014 .

[35]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[36]  Jan Bien,et al.  Application of Kinematic Method and FEM in Analysis of Ultimate Load Bearing Capacity of Damaged Masonry Arch Bridges , 2013 .

[37]  Curtis Collins,et al.  Adaptive neuro-fuzzy control of a flexible manipulator , 2005 .

[38]  Ole Sigmund,et al.  On the Design of Compliant Mechanisms Using Topology Optimization , 1997 .

[39]  Mirna Issa,et al.  Intelligent rotational direction control of passive robotic joint with embedded sensors , 2013, Expert Syst. Appl..

[40]  Ke Lu,et al.  An algorithm for semi-supervised learning in image retrieval , 2006, Pattern Recognition.

[41]  Guang-Bin Huang,et al.  Trends in extreme learning machines: A review , 2015, Neural Networks.

[42]  J. Adamowski,et al.  A wavelet neural network conjunction model for groundwater level forecasting , 2011 .

[43]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[44]  Joachim M. Buhmann,et al.  A New Contour-Based Approach to Object Recognition for Assembly Line Robots , 2001, DAGM-Symposium.

[45]  T. S. Mruthyunjaya,et al.  Synthesis of path generating flexible-link mechanisms , 1995 .

[46]  Betul Bektas Ekici,et al.  Prediction of building energy needs in early stage of design by using ANFIS , 2011, Expert Syst. Appl..

[47]  Youngjin Choi,et al.  Robotic index finger prosthesis using stackable double 4-BAR mechanisms , 2013 .

[48]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[49]  Welf-Guntram Drossel,et al.  Method for the Optimization of Kinematic and Dynamic Properties of Parallel Kinematic Machines , 2006 .

[50]  Dalibor Petković,et al.  Evaluation of wind turbine noise by soft computing methodologies: A comparative study , 2016 .

[51]  Jian Feng,et al.  Mobility analysis of generalized angulated scissor-like elements with the reciprocal screw theory , 2014 .

[52]  R. Khalifehzadeh,et al.  Prediction of the effect of vacuum sintering conditions on porosity and hardness of porous NiTi shape memory alloy using ANFIS , 2007 .

[53]  Hikaru Inooka,et al.  Force control of a robot gripper based on human grasping schemes , 2001 .

[54]  Danica Kragic,et al.  Learning grasping points with shape context , 2010, Robotics Auton. Syst..

[55]  Youyi Wang,et al.  Extreme learning machine based wind speed estimation and sensorless control for wind turbine power generation system , 2013, Neurocomputing.

[56]  Zhongya Zhang,et al.  Artificial neural networks applied to polymer composites: a review , 2003 .

[57]  Yu Sun,et al.  An ANFIS model for the prediction of flow stress of Ti600 alloy during hot deformation process , 2011 .

[58]  M. Sugeno,et al.  Derivation of Fuzzy Control Rules from Human Operator's Control Actions , 1983 .

[59]  M. Shunmugam,et al.  A unified framework for tolerance analysis of planar and spatial mechanisms using screw theory , 2013 .

[60]  Dominiek Reynaerts,et al.  Featureless classification of tactile contacts in a gripper using neural networks , 1996 .

[61]  Dalibor Petkovic,et al.  Adaptive neuro fuzzy selection of heart rate variability parameters affected by autonomic nervous system , 2013, Expert Syst. Appl..

[62]  Vicente Feliu,et al.  On Line Visual-Grasping System Based on a Gripper with Two Flexible Fingers , 2011 .

[63]  Peter K. Allen.  Robotic Object Recognition Using Vision and Touch , 1987 .

[64]  M. Mohanraj,et al.  Applications of artificial neural networks for thermal analysis of heat exchangers – A review , 2015 .

[65]  Sigal Berman,et al.  Efficient sensory-grounded grasp pose quality mapping for gripper design and online grasp planning , 2014, Robotics Auton. Syst..

[66]  Ancai Zhang,et al.  Stabilization of underactuated two-link gymnast robot by using trajectory tracking strategy , 2015, Appl. Math. Comput..

[67]  M. Sugeno,et al.  Structure identification of fuzzy model , 1988 .

[68]  D WahidaBanu.R.S.,et al.  Identification and Control of Nonlinear Systems using Soft Computing Techniques , 2011 .

[69]  Gert Kootstra,et al.  Design of a flexible tactile sensor for classification of rigid and deformable objects , 2014, Robotics Auton. Syst..

[70]  Melih İnal,et al.  Determination of dielectric properties of insulator materials by means of ANFIS: A comparative study , 2008 .

[71]  E. Amezua,et al.  Kinematic analysis of linkages based in finite elements and the geometric stiffness matrix , 2008 .

[72]  Dalibor Petković,et al.  Adaptive Neuro-Fuzzy Optimization of the Net Present Value and Internal Rate of Return of a Wind Farm Project under Wake Effect , 2015 .

[73]  R. H. Fouad,et al.  ELECTRICITY CONSUMPTION IN THE INDUSTRIAL SECTOR OF JORDAN: APPLICATION OF MULTIVARIATE LINEAR REGRESSION AND ADAPTIVE NEURO‐FUZZY TECHNIQUES , 2009 .

[74]  Wim Sweldens,et al.  An Overview of Wavelet Based Multiresolution Analyses , 1994, SIAM Rev..

[75]  Ole Sigmund,et al.  A 99 line topology optimization code written in Matlab , 2001 .

[76]  Mirna Issa,et al.  Adaptive neuro fuzzy controller for adaptive compliant robotic gripper , 2012, Expert Syst. Appl..

[77]  Stéphane Mallat,et al.  A Wavelet Tour of Signal Processing - The Sparse Way, 3rd Edition , 2008 .

[78]  Aman Mohammad Kalteh,et al.  Monthly river flow forecasting using artificial neural network and support vector regression models coupled with wavelet transform , 2013, Comput. Geosci..

[79]  José Manuel Iñesta Quereda,et al.  Planar Grasping Characterization Based on Curvature-Symmetry Fusion , 2004, Applied Intelligence.

[80]  Dalibor Petković,et al.  A New Principle of Adaptive Compliant Gripper , 2012 .

[81]  Ship-Peng Lo,et al.  The prediction of wafer surface non-uniformity using FEM and ANFIS in the chemical mechanical polishing process , 2005 .

[82]  Alexander J. Smola,et al.  Support Vector Method for Function Approximation, Regression Estimation and Signal Processing , 1996, NIPS.

[83]  B. Karaağaç,et al.  Predicting optimum cure time of rubber compounds by means of ANFIS , 2012 .

[84]  Mirna Issa,et al.  Adaptive neuro-fuzzy estimation of conductive silicone rubber mechanical properties , 2012, Expert Syst. Appl..

[85]  Matteo Giacopini,et al.  Multiphase CFD–CHT optimization of the cooling jacket and FEM analysis of the engine head of a V6 diesel engine , 2013 .

[86]  Se-Young Oh,et al.  A neural network based retrainable framework for robust object recognition with application to mobile robotics , 2011, Applied Intelligence.

[87]  C. Burrus,et al.  Introduction to Wavelets and Wavelet Transforms: A Primer , 1997 .

[88]  T. N. Singh,et al.  Estimation of elastic constant of rocks using an ANFIS approach , 2012, Appl. Soft Comput..

[89]  Xin Xin,et al.  Reduced-order stable controllers for two-link underactuated planar robots , 2013, Autom..

[90]  Mirna Issa,et al.  Sensor elements made of conductive silicone rubber for passively compliant gripper , 2013 .