Comparison study of two inverted pendulum models for balance recovery

The inverted pendulum model (IPM) represents better a human-like gait, however, its nonlinearity introduced by the impact during the change of support leg prevents its implementation. The analytic feature of the Linear Inverted Pendulum Model (LIPM) makes it widely applied for the bipedal gait control and balance recovery. We resolve the analytic solution issue for the IPM by using the principle orders in the Taylor series, and further prove the predictive properties of both models. Our theoretical and simulation studies quantitatively compare these two models on the prediction of the target foot placement and allowable swing time. The dynamic simulation and preliminary experiments validated the effectiveness of the IPM based foot placement control.

[1]  Twan Koolen,et al.  Capturability-based analysis and control of legged locomotion, Part 2: Application to M2V2, a lower-body humanoid , 2012, Int. J. Robotics Res..

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

[3]  Arthur D Kuo,et al.  Energetics of actively powered locomotion using the simplest walking model. , 2002, Journal of biomechanical engineering.

[4]  Kazuhito Yokoi,et al.  Reactive stepping to prevent falling for humanoids , 2009, 2009 9th IEEE-RAS International Conference on Humanoid Robots.

[5]  Nikolaos G. Tsagarakis,et al.  COMpliant huMANoid COMAN: Optimal joint stiffness tuning for modal frequency control , 2013, 2013 IEEE International Conference on Robotics and Automation.

[6]  Masayuki Inaba,et al.  Online decision of foot placement using singular LQ preview regulation , 2011, 2011 11th IEEE-RAS International Conference on Humanoid Robots.

[7]  Tad McGeer,et al.  Passive Dynamic Walking , 1990, Int. J. Robotics Res..

[8]  Nikolaos G. Tsagarakis,et al.  Stabilizing humanoids on slopes using terrain inclination estimation , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[9]  Nikolaos G. Tsagarakis,et al.  Development of a dynamic simulator for a compliant humanoid robot based on a symbolic multibody approach , 2013, 2013 IEEE International Conference on Mechatronics (ICM).

[10]  Eric Kubica,et al.  Introduction of the Foot Placement Estimator: A Dynamic Measure of Balance for Bipedal Robotics , 2008 .

[11]  Gordon Cheng,et al.  Full-Body Compliant Human–Humanoid Interaction: Balancing in the Presence of Unknown External Forces , 2007, IEEE Transactions on Robotics.

[12]  Nikolaos G. Tsagarakis,et al.  A passivity based admittance control for stabilizing the compliant humanoid COMAN , 2012, 2012 12th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2012).

[13]  Alin Albu-Schäffer,et al.  Development of a biped robot with torque controlled joints , 2010, 2010 10th IEEE-RAS International Conference on Humanoid Robots.

[14]  Rodney A. Brooks,et al.  Humanoid robots , 2002, CACM.

[15]  Christopher G. Atkeson,et al.  Push Recovery by stepping for humanoid robots with force controlled joints , 2010, 2010 10th IEEE-RAS International Conference on Humanoid Robots.

[16]  Kenichi Narioka,et al.  3D limit cycle walking of musculoskeletal humanoid robot with flat feet , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[17]  Bernard Espiau,et al.  Limit Cycles in a Passive Compass Gait Biped and Passivity-Mimicking Control Laws , 1997, Auton. Robots.