1. A non-linear kinematics and dynamics estimator based on unscented Kalman filter with angular momentum for humanoid compliant walking
- Author
-
Pei Chun Zheng, Alexander Chang, and Ren C. Luo
- Subjects
0209 industrial biotechnology ,Forward kinematics ,Computer science ,020208 electrical & electronic engineering ,02 engineering and technology ,Kalman filter ,Sensor fusion ,Computer Science::Robotics ,Extended Kalman filter ,020901 industrial engineering & automation ,Linearization ,Control theory ,Filter (video) ,0202 electrical engineering, electronic engineering, information engineering ,Humanoid robot ,Zero moment point - Abstract
This paper propose a non-linear estimator for humanoid robot. The accurate estimation of the center of mass (CoM) of the robot plays an important role to enhance the dynamic locomotion and manipulation of a humanoid robot. However, the estimation of the CoM is difficult because of the non-linear dynamics. To deal with the nonlinearity, the Extended Kalman Filter (EKF) is a common solution used in real time control application. However, one of the disadvantages of EKF is the linearization of the transition and observation function. The Unscented Kalman Filter (UKF) is a derivative-free filter without linearization error. In this paper, a novel method is presented to estimate humanoid robot locomotion with an Unscented Kalman Filter for data fusion with encoders, Inertial Measurement Units (IMU) and Force/Torque (F/T) sensors. The method relies on forward kinematics, Zero Moment Point (ZMP) with the variation of the angular momentum and 5 mass preview control/time-varying divergent component of motion to estimate the locomotion of a robot. By the use of the humanoid robot Renbo in our lab, we indicate the efficiency and the efficacy of the proposed estimator.
- Published
- 2017