Center of Mass States and Disturbance Estimation for a Walking Biped

Abstract

An on-line assessment of the balance of the robot requires information of the state variables of the robot dynamics and measurement data about the environmental interaction forces. However, modeling errors, external forces and hard to measure states pose difficulties to the control systems. This paper presents a method of using the motion information to estimate the center of mass (CoM) states and the disturbance of walking humanoid robot. The motion is acquired from the inertial measurement unit (IMU) and forward kinematics only. Kalman filter and disturbance observer are employed, Kalman filter is used for the states and disturbance estimation, and the disturbance observer is used to decompose the disturbance into modeling error and acceleration error based on the frequency band. The disturbance is modeled mathematically in terms of previous CoM and Zero moment point (ZMP) states rather than augmenting it in the system states. The ZMP is estimated using the quadratic programming method to solve the constraint dynamic equations of the humanoid robot in translational motion. A biped robot model of 12-degrees-of-freedom (DOF) is used in the full-dynamics 3-D simulations for the estimation validation. The results indicate that the presented estimation method is successful and promising.