Biped Robot Walking Control with Centrodial Angular Momentum Preview

In traditional biped robot motion planning and control process, linear invert pendulum model is used to generate walking trajectory, with no consideration of the change of angular momentum, which sometimes cannot be ignored. In this paper, we propose a trajectory compensate method that takes into account the precise change of centroidal angular momentum during robot moving, including upper limb movement, foot swing and torso orientation movement, etc. We also provide a QP based formulation to solve the center of mass (CoM) trajectory from the Centroidal Moment Pivot (CMP) trajectory. We can adjust different properties of the CoM trajectory by adding different cost functions. The QP based formulation also have a bigger tolerance of trajectory error, which enhance the robustness of the solved CoM trajectory. Furthermore, we established an angular momentum preview control scheme, extend the method to not only offline trajectory planning, but also real-time trajectory control circumstances. We test our compensate method and control scheme in simulation and get obvious improvement in robot walking control.

[1]  Humanoid Robot Gait Generator: Foot Steps Calculation for Trajectory Following , 2014, TAROS.

[2]  David E. Orin,et al.  Supervisory Control of a Multilegged Robot , 1982 .

[3]  Charles C. MacAdam,et al.  Application of an Optimal Preview Control for Simulation of Closed-Loop Automobile Driving , 1981, IEEE Transactions on Systems, Man, and Cybernetics.

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

[5]  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).

[6]  Arata Tsukamoto,et al.  Ultrafast spin dynamics across compensation points in ferrimagnetic GdFeCo: The role of angular momentum compensation , 2006 .

[7]  A. Willner,et al.  Adaptive-optics-based simultaneous pre- and post-turbulence compensation of multiple orbital-angular-momentum beams in a bidirectional free-space optical link , 2014 .

[8]  Russ Tedrake,et al.  Whole-body motion planning with centroidal dynamics and full kinematics , 2014, 2014 IEEE-RAS International Conference on Humanoid Robots.

[9]  Jian Huang,et al.  Control Method for The Balance Recovery of Indirect Tight Coordination Task Based on Force Sensor , 2018, 2018 IEEE Workshop on Advanced Robotics and its Social Impacts (ARSO).

[10]  Toshio Fukuda,et al.  Reinforcement Learning of Manipulation and Grasping Using Dynamical Movement Primitives for a Humanoidlike Mobile Manipulator , 2017, IEEE/ASME Transactions on Mechatronics.

[11]  David E. Orin,et al.  Centroidal dynamics of a humanoid robot , 2013, Auton. Robots.

[12]  Heinz Ulbrich,et al.  Self-collision avoidance and angular momentum compensation for a biped humanoid robot , 2011, 2011 IEEE International Conference on Robotics and Automation.

[13]  Xin Wang,et al.  Robot Motion Planning Method Based on Incremental High-Dimensional Mixture Probabilistic Model , 2018, Complex..

[14]  Wei Guo,et al.  Exoskeleton Online Learning and Estimation of Human Walking Intention Based on Dynamical Movement Primitives , 2021, IEEE Transactions on Cognitive and Developmental Systems.

[15]  Pierre-Brice Wieber,et al.  Online walking gait generation with adaptive foot positioning through Linear Model Predictive control , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[16]  Alin Albu-Schäffer,et al.  Three-Dimensional Bipedal Walking Control Based on Divergent Component of Motion , 2015, IEEE Transactions on Robotics.

[17]  ChangHwan Kim,et al.  Stable whole-body motion generation for humanoid robots to imitate human motions , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[18]  Thomas B. Sheridan,et al.  Three Models of Preview Control , 1966 .

[19]  Sung-Hee Lee,et al.  A momentum-based balance controller for humanoid robots on non-level and non-stationary ground , 2012, Auton. Robots.

[20]  Antonio Bicchi,et al.  Combined Sensing, Cognition, Learning, and Control for Developing Future Neuro-Robotics Systems: A Survey , 2019, IEEE Transactions on Cognitive and Developmental Systems.