Fuzzy Control of Vertical Jumping With a Planar Biped

This paper develops a control strategy to produce vertical jumps in a planar biped robot as a preliminary investigation into dynamic maneuvers. The control strategy was broken into two functional levels to separately solve the problems of coordination and execution of the jump maneuver. A high-level fuzzy controller addresses the complexities that arise from the system’s hybrid nonlinear dynamics and series-elastic actuators embedded in the articulated legs. A novel fuzzy training scheme is used because the system is too complex for traditional training methods. A low-level controller is based on a state machine that sequences the legs through the phases of a jump. The modular nature of the control strategy allows quick adaptation to other dynamic maneuvers. Validity is demonstrated through dynamic simulation and testing with the experimental biped KURMET which result in stable successive jumps over a range of heights.Copyright © 2010 by ASME

[1]  Atsuo Kawamura,et al.  Development of ROCOS (Robot Control Simulator)-Jump of human-type biped robot by the adaptive impedance control , 2000, 6th International Workshop on Advanced Motion Control. Proceedings (Cat. No.00TH8494).

[2]  Jadran Lenarčič,et al.  A Biarticulated Robotic Leg for Jumping Movements: Theory and Experiments , 2009 .

[3]  Matthew M. Williamson,et al.  Series elastic actuators , 1995, Proceedings 1995 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human Robot Interaction and Cooperative Robots.

[4]  Panos J. Antsaklis,et al.  An introduction to intelligent and autonomous control , 1993 .

[5]  Yasuo Kuniyoshi,et al.  Mowgli: A Bipedal Jumping and Landing Robot with an Artificial Musculoskeletal System , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[6]  Marc H. Raibert,et al.  Legged Robots That Balance , 1986, IEEE Expert.

[7]  Luther R. Palmer,et al.  Intelligent Control of High-Speed Turning in a Quadruped , 2010, J. Intell. Robotic Syst..

[8]  E. Bizzi,et al.  Article history: , 2005 .

[9]  Jessy W. Grizzle,et al.  Monopedal running control: SLIP embedding and virtual constraint controllers , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[10]  Stefan Schaal,et al.  Is imitation learning the route to humanoid robots? , 1999, Trends in Cognitive Sciences.

[11]  Luther R. Palmer,et al.  Intelligent control of an experimental articulated leg for a galloping machine , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

[12]  Koh Hosoda,et al.  Biped robot design powered by antagonistic pneumatic actuators for multi-modal locomotion , 2008, Robotics Auton. Syst..

[13]  David E. Orin,et al.  Intelligent control of quadruped gallops , 2003 .

[14]  David E. Orin,et al.  DynaMechs: An Object Oriented Software Package for Efficient Dynamic Simulation of Underwater Robotic Vehicles , 1995 .

[15]  Christine Chevallereau,et al.  Achieving Bipedal Running with RABBIT: Six Steps Toward Infinity , 2006 .

[16]  R. O’Reilly Six principles for biologically based computational models of cortical cognition , 1998, Trends in Cognitive Sciences.