A modified gait planning method for biped robot based on central pattern generators

The gait planning is essential in the sampling-based footstep planning of biped robot. However, conventional gait planning methods of Zero Moment Point and neural networks are much time-consuming, and existing central pattern generator (CPG) based method can only generate the gait of going forward. Based on the phase and trajectory analysis, this paper proposes a modified gait planning method based on CPG for the sampling-based footstep planning. By adjusting the parameter of the CPG, it is available to obtain different gaits of going forward, stepping side and swerving, and realize smooth transition between these gaits. Physical experiments on NAO robot verified the effectiveness of the proposed method.

[1]  Shin Ishii,et al.  Reinforcement learning for a biped robot based on a CPG-actor-critic method , 2007, Neural Networks.

[2]  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..

[3]  Ken Chen,et al.  Parameter self-adaptation in biped navigation employing nonuniform randomized footstep planner , 2010, Robotica.

[4]  M. Mishkin,et al.  Perseverative interference in monkeys following selective lesions of the inferior prefrontal convexity , 1970, Experimental Brain Research.

[5]  Gordon Cheng,et al.  A Study of Adaptive Locomotive Behaviors of a Biped Robot: Patterns Generation and Classification , 2010, SAB.

[6]  William Bialek,et al.  Adaptive spike coding , 2002 .

[7]  J. Chestnutt,et al.  Planning Biped Navigation Strategies in Complex Environments , 2003 .

[8]  Kazuhito Yokoi,et al.  A high stability, smooth walking pattern for a biped robot , 1999, Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C).

[9]  Masayuki Inaba,et al.  Footstep planning among obstacles for biped robots , 2001, Proceedings 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems. Expanding the Societal Role of Robotics in the the Next Millennium (Cat. No.01CH37180).

[10]  Takeo Kanade,et al.  Footstep Planning for the Honda ASIMO Humanoid , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[11]  Gordon Cheng,et al.  Qualitative Adaptive Reward Learning With Success Failure Maps: Applied to Humanoid Robot Walking , 2013, IEEE Transactions on Neural Networks and Learning Systems.

[12]  Ken Chen,et al.  A deterministic sampling-based approach to global footstep planning for humanoid robots , 2009, 2009 9th IEEE-RAS International Conference on Humanoid Robots.

[13]  P. Cochat,et al.  Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.

[14]  Ken Chen,et al.  Global Navigation for Humanoid Robots Using Sampling-Based Footstep Planners , 2011, IEEE/ASME Transactions on Mechatronics.

[15]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[16]  J. Morimoto,et al.  A Biologically Inspired Biped Locomotion Strategy for Humanoid Robots: Modulation of Sinusoidal Patterns by a Coupled Oscillator Model , 2008, IEEE Transactions on Robotics.

[17]  Masayuki Inaba,et al.  Motion Planning for Humanoid Robots , 2003, ISRR.

[18]  Jianwei Zhang,et al.  A Survey on CPG-Inspired Control Models and System Implementation , 2014, IEEE Transactions on Neural Networks and Learning Systems.