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SCIENCE CHINA Information Sciences, Volume 61, Issue 12: 122202(2018) https://doi.org/10.1007/s11432-017-9319-0

Experiment on impedance adaptation of under-actuated gripper using tactile array under unknown environment

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  • ReceivedOct 3, 2017
  • AcceptedNov 30, 2017
  • PublishedNov 23, 2018

Abstract

Theexperiment on impedance adaptation to achieve stable grasp for an under-actuated gripper grasping different unknown objects with tactile array is conducted Under-actuated gripper has a wildly application in the field of space robot and industrial robot because of its better shape-adaptation. However it is difficult to achieve stable grasp owning to the uncertain properties of environment. Acontrol strategy of adaptive matching the impedance parameters is proposedto achieve stable grasp. Firstly, the unknown objects are described as linear systems with unknown dynamics, and the parameters of the object are identified with the recursive least-squares (RLS) method through tactile sensor array. Then a desired impedance model is obtained by defining a cost function that includes the contact force, velocity and displacement errors, and the critical impedance parameters are found to minimize it. Finally, an experiment is presented and shows that the proposed impedance model can guarantee the stable grasp for various unknown objects.


Acknowledgment

This work was supported by National Natural Science Foundation of China (Grant Nos. 61773028, 51375034), Natural Science Foundation of Beijing (Grant No. 4172008), and the Fundamental Research Funds for the Central Universities (Grant No. YWF-17-BJ-J-78).


References

[1] Kragten G A, van der Helm F C T, Herder J L. A planar geometric design approach for a large grasp range in underactuated hands. Mechanism Machine Theor, 2011, 46: 1121-1136 CrossRef Google Scholar

[2] Huang P, Wang D, Meng Z. Impact Dynamic Modeling and Adaptive Target Capturing Control for Tethered Space Robots With Uncertainties. IEEE/ASME Trans Mechatron, 2016, 21: 2260-2271 CrossRef Google Scholar

[3] Wang D, Huang P, Meng Z. Coordinated stabilization of tumbling targets using tethered space manipulators. IEEE Trans Aerosp Electron Syst, 2015, 51: 2420-2432 CrossRef ADS Google Scholar

[4] Huang P, Wang D, Zhang F. Postcapture robust nonlinear control for tethered space robot with constraints on actuator and velocity of space tether. Int J Robust NOnlinear Control, 2017, 27: 2824-2841 CrossRef Google Scholar

[5] Chu Z, Di J, Cui J. Analysis of the effect of attachment point bias during large space debris removal using a tethered space tug. Acta Astronaut, 2017, 139: 34-41 CrossRef ADS Google Scholar

[6] Guo D, Sun F, Liu H, et al. A hybrid deep architecture for robotic grasp detection. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Singapore, 2017. 1609--1614. Google Scholar

[7] Liu H, Liu Y, Huang L, et al. Discovery of topical object in image collections. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Seattle, 2015. 1886--1892. Google Scholar

[8] Liu H, Wu Y, Sun F, et al. Multi-label tactile property analysis. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Singapore, 2017. 366--371. Google Scholar

[9] Guo D, Kong T, Sun F, et al. Object discovery and grasp detection with a shared convolutional neural network. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Stockholm, 2016. 2038--2043. Google Scholar

[10] Tiwana M I, Shashank A, Redmond S J. Characterization of a capacitive tactile shear sensor for application in robotic and upper limb prostheses. Senss Actuators A-Phys, 2011, 165: 164-172 CrossRef Google Scholar

[11] Luo M, Sun F, Liu H. Dynamic T-S Fuzzy Systems Identification Based on Sparse Regularization. Asian J Control, 2015, 17: 274-283 CrossRef Google Scholar

[12] Ma R, Liu H P, Sun F C, et al. Linear dynamic system method for tactile object classification. Sci China Inf Sci, 2014, 57: 120205. Google Scholar

[13] Asif U, Iqbal J. On the Improvement of Multi-Legged Locomotion over Difficult Terrains Using a Balance Stabilization Method. Int J Adv Robotic Syst, 2012, 9: 1 CrossRef Google Scholar

[14] Hogan N. Hogan N. Impedance control: an approach to manipulation-part I: theory. Google Scholar

[15] Xu Q. Robust Impedance Control of a Compliant Microgripper for High-Speed Position/Force Regulation. IEEE Trans Ind Electron, 2015, 62: 1201-1209 CrossRef Google Scholar

[16] Li M, Hang K, Kragic D. Dexterous grasping under shape uncertainty. Robotics Autonomous Syst, 2016, 75: 352-364 CrossRef Google Scholar

[17] Stanisic R Z, Fernández V. Adjusting the parameters of the mechanical impedance for velocity, impact and force control. Robotica, 2012, 30: 583-597 CrossRef Google Scholar

[18] Yoon J, Manurung A, Kim G S. Impedance control of a small treadmill with sonar sensors for automatic speed adaptation. Int J Control Autom Syst, 2014, 12: 1323-1335 CrossRef Google Scholar

[19] Petkovi? D, Issa M, Pavlovi? N D. Adaptive neuro fuzzy controller for adaptive compliant robotic gripper. Expert Syst Appl, 2012, 39: 13295-13304 CrossRef Google Scholar

[20] Buchli J, Stulp F, Theodorou E. Learning variable impedance control. Int J Robotics Res, 2011, 30: 820-833 CrossRef Google Scholar

[21] Li M, Bekiroglu Y, Kragic D, et al. Learning of grasp adaptation through experience and tactile sensing. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems(IROS), Chicago, 2014. 3339--3346. Google Scholar

[22] Jiang Y, Jiang Z P. Computational adaptive optimal control for continuous-time linear systems with completely unknown dynamics. Automatica, 2012, 48: 2699-2704 CrossRef Google Scholar

[23] Johansson R, Spong M W. Quadratic optimization of impedance control. In: Proceedings of IEEE International Conference of Robotics and Automation (ICRA), San diego, 1994. 616--621. Google Scholar

[24] Xu B, Zhang P. Composite learning sliding mode control of flexible-link manipulator. Complexity, 2017, 3: 1--6. Google Scholar

[25] He W, Dong Y. Adaptive fuzzy neural network control for a constrained robot using impedance learning. IEEE Trans Neural Netw Learn Syst, 2017, 99: 1--13. Google Scholar

[26] He W, Dong Y, Sun C. Adaptive Neural Impedance Control of a Robotic Manipulator With Input Saturation. IEEE Trans Syst Man Cybern Syst, 2016, 46: 334-344 CrossRef Google Scholar

[27] Byungchan Kim , Jooyoung Park , Shinsuk Park . Impedance learning for robotic contact tasks using natural actor-critic algorithm.. IEEE Trans Syst Man Cybern B, 2010, 40: 433-443 CrossRef PubMed Google Scholar

[28] Xu B, Zhang P. Minimal-learning-parameter technique based adaptive neural sliding mode control of MEMS gyroscope. Complexity, 2017, 12: 1--8. Google Scholar

[29] Ge S S, Li Y, Wang C. Impedance adaptation for optimal robot-environment interaction. Int J Control, 2014, 87: 249-263 CrossRef Google Scholar

[30] Chu Z Y, Lai M, Yan S. Optimization design of spring stiffness for under-actuated gripper (in Chinese). Acta Aeronautica Astronautica Sin, 2018, 39: 421370. Google Scholar

[31] Liu Y, Ding F. Convergence properties of the least squares estimation algorithm for multivariable systems. Appl Math Model, 2013, 37: 476-483 CrossRef Google Scholar

[32] Chu Z Y, Yan S B, Hu J. Impedance Identification Using Tactile Sensing and Its Adaptation for an Underactuated Gripper Manipulation. Int J Control Autom Syst, 2018, 16: 875-886 CrossRef Google Scholar

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