Fuzzy control-based real-time robust balance for a humanoid robot

Abstract This paper studies and implements a real-time robust balance control for a humanoid robot under three environment disturbances which are an external thrust, an inclinable platform, and a see-saw. More precisely to say, the robot with robust control can resist an external thrust, stand on a two-axis inclinable platform, or walk on a see-saw successfully. The main feature of the robot is that it has a waist joint which has three degrees of freedom. With the aids of the proposed fuzzy controllers, the robot can change the posture of the body nimbly by adjusting the waist joint and two ankle joints to strengthen the stabilization capacity. The sensory system of the robot includes eight force sensors and one inertial measurement unit sensor in order to measure the center of pressure and the slant angle of the robot’s body. According to the measured data from the sensors and by imitating human reflex actions, the proposed fuzzy controllers perform real-time balance control for the robot under three environment disturbances. According to the experiment results, the stability of the robot is increased at least 32.2 and 61.7% under the first two environment disturbances, respectively. In addition, the robot walking on a see-saw has a success rate of about 95%.

[1]  Yuan F. Zheng,et al.  Gait synthesis for the SD-2 biped robot to climb sloping surface , 1990, IEEE Trans. Robotics Autom..

[2]  Jong Hyeon Park,et al.  Fuzzy-logic zero-moment-point trajectory generation for reduced trunk motions of biped robots , 2003, Fuzzy Sets Syst..

[3]  Friedrich Pfeiffer,et al.  Sensors and control concept of a biped robot , 2004, IEEE Transactions on Industrial Electronics.

[4]  Bernardete Ribeiro,et al.  Control of a Biped Robot With Support Vector Regression in Sagittal Plane , 2009, IEEE Transactions on Instrumentation and Measurement.

[5]  Jung-Shik Kong,et al.  Study on the Real-Time Walking Control of a Humanoid Robot Using Fuzzy Algorithm , 2008 .

[6]  A. Paulo Coimbra,et al.  Human Gait Acquisition and Characterization , 2009, IEEE Transactions on Instrumentation and Measurement.

[7]  Jerry E. Pratt,et al.  Learning Capture Points for humanoid push recovery , 2007, 2007 7th IEEE-RAS International Conference on Humanoid Robots.

[8]  Ching-Long Shih,et al.  The motion control of a statically stable biped robot on an uneven floor , 1998, IEEE Trans. Syst. Man Cybern. Part B.

[9]  Robert Babuska,et al.  Observer-based postural balance control for humanoid robots , 2013, 2013 IEEE International Conference on Robotics and Biomimetics (ROBIO).

[10]  Helmut Hauser,et al.  Biologically inspired kinematic synergies enable linear balance control of a humanoid robot , 2011, Biological Cybernetics.

[11]  Kemalettin Erbatur,et al.  Trajectory generation with natural ZMP references for the biped walking robot SURALP , 2010, 2010 IEEE International Conference on Robotics and Automation.

[12]  Leonard Barolli,et al.  Real time gait generation for autonomous humanoid robots: A case study for walking , 2003, Robotics Auton. Syst..

[13]  Marko B. Popovic,et al.  Ground Reference Points in Legged Locomotion: Definitions, Biological Trajectories and Control Implications , 2005, Int. J. Robotics Res..

[14]  Jong Hyeon Park,et al.  ZMP compensation by online trajectory generation for biped robots , 1999, IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.99CH37028).

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

[16]  Atsuo Kawamura,et al.  A study on the zero moment point measurement for biped walking robots , 2002, 7th International Workshop on Advanced Motion Control. Proceedings (Cat. No.02TH8623).

[17]  Ambarish Goswami,et al.  Postural Stability of Biped Robots and the Foot-Rotation Indicator (FRI) Point , 1999, Int. J. Robotics Res..

[18]  Jimmy Or,et al.  A Hybrid CPG–ZMP Controller for the Real-Time Balance of a Simulated Flexible Spine Humanoid Robot , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[19]  Sung-Hee Lee,et al.  Ground reaction force control at each foot: A momentum-based humanoid balance controller for non-level and non-stationary ground , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.