Abstract
This paper presents an online method that can accurately estimate the time-varying posture of a moving hose-shaped robot having multiple microphones and loudspeakers. Sound-based posture estimation has been considered to be promising for circumventing the cumulative error problem of conventional integral-type methods using differential information obtained by inertial sensors. Our robot emits a reference signal from a loud-speaker one by one and estimates its posture by measuring the time differences of arrival (TDOAs) at the microphones. To accurately estimate the posture of the robot (the relative positions of the microphones and loudspeakers) even when the robot moves, we propose a novel state-space model that represents the dynamics of not only the posture itself but also its change rate in the state space. This model is used for predicting the current posture by using an unscented Kalman filter. The experiments using a 3m moving hose-shaped robot with eight microphones and seven loudspeakers showed that our method achieved less than 20 cm error at the tip position even after the robot moved over a long time, whereas the estimation error obtained by a conventional integral-type method increased monotonically over time.
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