Online generation of cyclic leg trajectories synchronized with sensor measurement

The generation of trajectories for a biped robot is a problem which has been largely studied for several years, and many satisfying offline solutions exist for steady-state walking in absence of disturbances. The question is a little more complex when the generation of the desired trajectories of joints or links has to be achieved or adapted online, i.e. in real time, for example when it is wished to strongly synchronize these trajectories with an external motion. This is precisely the problem addressed in this paper. Indeed, we consider the case where the ''master'' motion is measured by a position sensor embedded on a human leg. We propose a method to synchronize the motion of a robot or of other device with respect to the output signal of the sensor. The main goal is to estimate as accurately as possible the current phase along the gait cycle. We use for that purpose a model based on a nonlinear oscillator, which we associate an observer. Introducing the sensor output in the observer allows us to compute the oscillator phase and to generate a synchronized multilinks trajectory, at a very low computational cost. The paper also presents evaluation results in terms of robustness against parameter estimation errors and velocity changes in the input.

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

[2]  Martijn Wisse,et al.  A Three-Dimensional Passive-Dynamic Walking Robot with Two Legs and Knees , 2001, Int. J. Robotics Res..

[3]  Jun Morimoto,et al.  An empirical exploration of a neural oscillator for biped locomotion control , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[4]  Louis M. Pecora,et al.  Synchronizing chaotic circuits , 1991 .

[5]  Jian L. Zhou,et al.  User's Guide for CFSQP Version 2.0: A C Code for Solving (Large Scale) Constrained Nonlinear (Minimax) Optimization Problems, Generating Iterates Satisfying All Inequality Constraints , 1994 .

[6]  J. Betts Survey of Numerical Methods for Trajectory Optimization , 1998 .

[7]  Pierre-Brice Wieber,et al.  Trajectory Free Linear Model Predictive Control for Stable Walking in the Presence of Strong Perturbations , 2006, 2006 6th IEEE-RAS International Conference on Humanoid Robots.

[8]  G. Taga Nonlinear Dynamics of the Human Motor Control-Real-Time and Anticipatory Adaptation of Locomotion and Development of Movements - , 2000 .

[9]  A. Ijspeert,et al.  From Dynamic Hebbian Learning for Oscillators to Adaptive Central Pattern Generators , 2005 .

[10]  Chandana Paul,et al.  Development of a human neuro-musculo-skeletal model for investigation of spinal cord injury , 2005, Biological Cybernetics.

[11]  Thomas Sinkjær,et al.  Long-term follow-up of patients using the ActiGait implanted drop-foot stimulator , 2005 .

[12]  D. Luenberger An introduction to observers , 1971 .

[13]  Stéphane Bonnet,et al.  A Magnetometer-Based Approach for Studying Human Movements , 2007, IEEE Transactions on Biomedical Engineering.

[14]  Matthew M. Williamson,et al.  Neural control of rhythmic arm movements , 1998, Neural Networks.

[15]  R. Brand,et al.  The biomechanics and motor control of human gait: Normal, elderly, and pathological , 1992 .

[16]  D. A. Linkens The method of harmonic balance applied to coupled asymmetrical van der Pol oscillators for intestinal modelling , 1979 .

[17]  H. Nijmeijer,et al.  Self-synchronization and controlled synchronization of dynamical systems , 1997, 1997 European Control Conference (ECC).

[18]  S. D. Prentice,et al.  Simple artificial neural network models can generate basic muscle activity patterns for human locomotion at different speeds , 1998, Experimental Brain Research.

[19]  Auke Jan Ijspeert,et al.  A connectionist central pattern generator for the aquatic and terrestrial gaits of a simulated salamander , 2001, Biological Cybernetics.

[20]  Kazuhito Yokoi,et al.  Biped walking pattern generation by using preview control of zero-moment point , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

[21]  P. Holmes,et al.  Nonlinear Oscillations, Dynamical Systems, and Bifurcations of Vector Fields , 1983, Applied Mathematical Sciences.

[22]  Thomas Kailath,et al.  Linear Systems , 1980 .

[23]  Bernard Espiau,et al.  A Study of the Passive Gait of a Compass-Like Biped Robot , 1998, Int. J. Robotics Res..

[24]  Henk Nijmeijer,et al.  An observer looks at synchronization , 1997 .

[25]  Peggy Arnell,et al.  The Biomechanics and Motor Control of Human Gait , 1988 .

[26]  Gentaro Taga,et al.  A model of the neuro-musculo-skeletal system for human locomotion , 1995, Biological Cybernetics.

[27]  S. Rossignol,et al.  Neural Control of Rhythmic Movements in Vertebrates , 1988 .

[28]  J. Zebrowski,et al.  Modeling cardiac pacemakers with relaxation oscillators , 2004 .

[29]  I.P.I. Pappas,et al.  A reliable, gyroscope based gait phase detection sensor embedded in a shoe insole , 2002, Proceedings of IEEE Sensors.

[30]  Jürgen Kurths,et al.  Synchronization - A Universal Concept in Nonlinear Sciences , 2001, Cambridge Nonlinear Science Series.

[31]  S. Grillner Neurobiological bases of rhythmic motor acts in vertebrates. , 1985, Science.

[32]  Arkady Pikovsky,et al.  A universal concept in nonlinear sciences , 2006 .

[33]  Kiyotoshi Matsuoka,et al.  Sustained oscillations generated by mutually inhibiting neurons with adaptation , 1985, Biological Cybernetics.

[34]  A. Isidori Nonlinear Control Systems , 1985 .

[35]  Nancy S Makay A universal concept. , 2006, Rehab management.

[36]  Ludovic Righetti,et al.  Programmable central pattern generators: an application to biped locomotion control , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..