Novel state estimation framework for humanoid robot

Abstract

This study proposes a new Kalman filter-based framework for humanoid robot state estimation. The conventional Kalman filter generates optimal estimation solutions only when the nominal equations of the model and measurement include zero-mean, uncorrelated, white Gaussian noise. Because a humanoid robot is a complex system with multiple degrees of freedom, its mathematical model is limited in terms of expressing the system accurately, resulting in the generation of non-zero-mean, non-Gaussian, correlated modeling errors. Therefore, it is difficult to obtain accurate state estimates if the conventional Kalman filter-based approaches are used with such inexact humanoid models. The proposed modified Kalman filter framework consists of two loops: a loop to estimate the state, and a loop to estimate the disturbance generated by the modeling errors (a dual-loop Kalman filter). The disturbance values estimated by the disturbance estimation loop are provided as feedback to the state estimation loop, thereby improving the accuracy of the model-based prediction process. By considering the correlation between the state and disturbance in the estimation process, the disturbance can be accurately estimated. Therefore, the proposed estimator allows the use of a simple model, even if it implies the presence of a large modeling error. In addition, it can estimate the humanoid state more accurately than the conventional Kalman filter. Furthermore, the proposed filter has a simpler structure than the existing robust Kalman filters, which require the solution of complex Riccati equations; hence, it can facilitate recursive online implementation. The performance and characteristics of the proposed filter are verified by comparison with other existing linear/nonlinear estimators using simple examples and simulations. Furthermore, the feasibility of the proposed filter is verified by implementing it on a real humanoid robot platform. (C) 2017 Elsevier B.V. All rights reserved.