Application of the Method of Maximum Likelihood to Identification of Bipedal Walking Robots

Abstract

This brief studies the problem of parameter estimation and model identification for a class of underactuated mechanical systems modeled via the Euler–Lagrange formalism, such as underactuated walking robots. This problem is closely related with the measurement of the absolute orientation during walking. A novel identification method suited for this problem was proposed. The method takes advantage of the linear structure of the model with respect to estimated parameters. The resulting estimator is calculated iteratively and maximizes the likelihood of the data. The method was tested on both simulated and experimental data. Simulation was carried out for an underactuated walking robot with a distance meter to measure absolute orientation. Laboratory experiment was carried out on a leg of a laboratory walking robot model equipped with a three-axis accelerometer and gyroscope to measure absolute orientation. The method performs favorably in comparison with other benchmark estimation algorithms and both the simulation example and the laboratory experiment confirmed its high potential for the use in identification of underactuated robotic walkers.