Center-of-Mass Estimation for a Polyarticulated System in Contact-A Spectral Approach

This paper discusses the problem of estimating the position of the center of mass for a polyarticulated system (e.g., a humanoid robot or a human body), which makes contact with its environment. The only sensors providing measurements on this point are either interaction force sensors or kinematic reconstruction applied to a dynamic model of the system. We first study the observability of the center-of-mass position using these sensors and we show that the accuracy domain of each measurement can be easily described through a spectral analysis. We finally introduce an original approach based on the theory of complementary filtering to efficiently merge these input measurements and obtain an estimation of the center-of-mass position. This approach is extensively validated in simulations by using a model of a humanoid robot through which we confirm the spectral analysis of the signal errors and show that the complementary filter offers a lower average reconstruction error than the classical Kalman filter. Some experimental applications of this filter on real signals are also presented.