Internal model control for improving the gait tracking of a compliant humanoid robot

This paper reports on the modelling and trajectory generation of an intrinsically compliant humanoid robot. To achieve adequate gait tracking performance in a compliant robot is not trivial and cannot be addressed with the traditional control approaches used for stiff robots. To permit the development of effective gait generators which take into account the additional dynamic effects due to intrinsic compliance, an appropriate model which can predict the robot motion dynamics is required. In this work, we propose a model which combines the inverted pendulum model approach with a compliant model (Cartesian) at the level of the COM. Based on this model which permits to predict the motion of the centre of mass (COM) of the compliant robot an Internal Model Control strategy is adopted to improve the gait tracking performance. The derivation of the model is introduced followed by experimental validation which demonstrates the tracking performance achieved by the proposed reduced model. The Internal Model Control is subsequently discussed and validated on the COmpliant huMANoid COMAN using a series of ZMP based walking gaits.

[1]  Kazuhito Yokoi,et al.  Running Pattern Generation for a Humanoid Robot , 2003 .

[2]  Miomir Vukobratovic,et al.  Zero-Moment Point - Thirty Five Years of its Life , 2004, Int. J. Humanoid Robotics.

[3]  Nikolaos G. Tsagarakis,et al.  The design of the lower body of the compliant humanoid robot “cCub” , 2011, 2011 IEEE International Conference on Robotics and Automation.

[4]  Jerry E. Pratt,et al.  Intuitive control of a planar bipedal walking robot , 1998, Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146).

[5]  Bram Vanderborght,et al.  Overview of the Lucy Project: Dynamic Stabilization of a Biped Powered by Pneumatic Artificial Muscles , 2008, Adv. Robotics.

[6]  Twan Koolen,et al.  The Yobotics-IHMC Lower Body Humanoid Robot , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[7]  M. Hirose,et al.  Development of Humanoid Robot ASIMO , 2001 .

[8]  Nikolaos G. Tsagarakis,et al.  The mechanical design of the new lower body for the child humanoid robot ‘iCub’ , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[9]  Carlos E. Garcia,et al.  Internal model control. 2. Design procedure for multivariable systems , 1985 .

[10]  Kazuhito Yokoi,et al.  Biped walking stabilization based on linear inverted pendulum tracking , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[11]  Atsuo Takanishi,et al.  Development of a new humanoid robot WABIAN-2 , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[12]  M. A. Henson,et al.  An internal model control strategy for nonlinear systems , 1991 .

[13]  Nikolaos G. Tsagarakis,et al.  A compact soft actuator unit for small scale human friendly robots , 2009, 2009 IEEE International Conference on Robotics and Automation.

[14]  M. Morari,et al.  Internal Model Control: extension to nonlinear system , 1986 .

[15]  Nikolaos G. Tsagarakis,et al.  Safe human robot interaction via energy regulation control , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[16]  Nikolaos G. Tsagarakis,et al.  Lower body realization of the baby humanoid - ‘iCub’ , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[17]  T. Takenaka,et al.  The development of Honda humanoid robot , 1998, Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146).

[18]  M. Spong Modeling and Control of Elastic Joint Robots , 1987 .

[19]  Nikolaos G. Tsagarakis,et al.  iCub: the design and realization of an open humanoid platform for cognitive and neuroscience research , 2007, Adv. Robotics.