Teleimpedance control of a synergy-driven anthropomorphic hand

In this paper, a novel synergy driven teleimpedance controller for the Pisa-IIT SoftHand is presented. Towards the development of an efficient, robust, and low-cost hand prothesis, the Pisa-IIT SoftHand is built on the motor control principle of synergies, through which the immense complexity of the hand is simplified into distinct motor patterns. As the SoftHand grasps, it follows a synergistic path with built-in flexibility to allow grasping of objects of various shapes using only a single motor. In this work, the hand grasping motion is regulated with an impedance controller which incorporates the user's postural and stiffness synergy profiles in realtime. In addition, a disturbance observer is realized which estimates the grasping contact force. The estimated force is then fedback to the user via a vibration motor. Grasp robustness and transparency improvements were evaluated on two healthy subjects while grasping different objects. Implementation of the proposed teleimpedance controller led to the execution of stable grasps by controlling the grasping forces, via modulation of hand compliance. In addition, utilization of the vibrotactile feedback resulted in reduced physical load on the user. While these results need to be validated with amputees, they provide evidence that a low-cost, robust hand employing hardware-based synergies is a viable alternative to traditional myoelectric prostheses.

[1]  Panagiotis K. Artemiadis,et al.  EMG-Based Control of a Robot Arm Using Low-Dimensional Embeddings , 2010, IEEE Transactions on Robotics.

[2]  Nikolaos G. Tsagarakis,et al.  Tele-Impedance based stiffness and motion augmentation for a knee exoskeleton device , 2013, 2013 IEEE International Conference on Robotics and Automation.

[3]  Claudio Melchiorri,et al.  UBH 3: an anthropomorphic hand with simplified endo-skeletal structure and soft continuous fingerpads , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[4]  A. Kargov,et al.  Design and Evaluation of a Low-Cost Force Feedback System for Myoelectric Prosthetic Hands , 2006 .

[5]  Marco P. Schoen,et al.  Control strategies for smart prosthetic hand technology: An overview , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[6]  Wynne A. Lee,et al.  Neuromotor synergies as a basis for coordinated intentional action. , 1984, Journal of motor behavior.

[7]  Nikolaos G. Tsagarakis,et al.  TeleImpedance: Exploring the role of common-mode and configuration-dependant stiffness , 2012, 2012 12th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2012).

[8]  Silvestro Micera,et al.  On the Shared Control of an EMG-Controlled Prosthetic Hand: Analysis of User–Prosthesis Interaction , 2008, IEEE Transactions on Robotics.

[9]  Ning Jiang,et al.  Extracting Simultaneous and Proportional Neural Control Information for Multiple-DOF Prostheses From the Surface Electromyographic Signal , 2009, IEEE Transactions on Biomedical Engineering.

[10]  David G Lloyd,et al.  Neuromusculoskeletal modeling: estimation of muscle forces and joint moments and movements from measurements of neural command. , 2004, Journal of applied biomechanics.

[11]  Nikolaos G. Tsagarakis,et al.  Tele-impedance: Teleoperation with impedance regulation using a body–machine interface , 2012, Int. J. Robotics Res..

[12]  Wei-Der Chang,et al.  A feedforward neural network with function shape autotuning , 1996, Neural Networks.

[13]  Carlos Canudas de Wit,et al.  Adaptive friction compensation with partially known dynamic friction model , 1997 .

[14]  Toshio Tsuji,et al.  Bio-mimetic impedance control of an EMG-controlled prosthetic hand , 2000, Proceedings. 2000 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2000) (Cat. No.00CH37113).

[15]  Clément Gosselin,et al.  Underactuated Robotic Hands , 2008, Springer Tracts in Advanced Robotics.

[16]  Antonio Bicchi,et al.  On the role of hand synergies in the optimal choice of grasping forces , 2010, Auton. Robots.

[17]  Antonio Bicchi,et al.  Modelling natural and artificial hands with synergies , 2011, Philosophical Transactions of the Royal Society B: Biological Sciences.

[18]  Manuel G. Catalano,et al.  Adaptive synergies for a humanoid robot hand , 2012, 2012 12th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2012).

[19]  Toshiyuki Murakami,et al.  Torque sensorless control in multidegree-of-freedom manipulator , 1993, IEEE Trans. Ind. Electron..

[20]  M. Arbib Coordinated control programs for movements of the hand , 1985 .

[21]  J. F. Soechting,et al.  Postural Hand Synergies for Tool Use , 1998, The Journal of Neuroscience.