Nonlinear pitch and roll estimation for walking robots

Abstract

We study a nonlinear pitch and roll estimation problem for a 4-rigid body, aiming at a walking robot application. The approach taken is that sensor data from rate gyros and inclinometers are combined using a high-gain observer that can be proven to be exponentially convergent. The algorithm has successfully been evaluated experimentally during conditions resembling walking robot motion and has been compared with the more standard extended Kalman filter (EKF). It is shown that the much simpler high-gain observer performs slightly better than the EKF and that both algorithms provide small and bounded estimation errors.