Vision and force/torque integration for realtime estimation of fast-moving object under intermittent contacts

This paper considers the fast and accurate estimation of motion variables of a rigid body object whose movement occurs from intermittent contacts with coordinating manipulators in nonprehensile manipulation tasks. The estimator operates under multiple sensory data including visual, joint torque, joint position and/or tactile measurements which are combined at the lower level to compensate for the latency and the slow sampling of the visual data. The estimator is real-time in the sense that it provides the motion data of the target object at the same fast sample rate of the servo controller without delay. The basic formulation is the multi-rate Kalman filter with the contact force vector as its process input, and the visual observation as its measurement output signal which is the down-sampled and delayed version of the configuration of the target object. Experimental tests are conducted for the case of planar object manipulation as well as the non-centroidal rotation under gravity using a robotic hand, and their results are presented to demonstrate the validity of the proposed estimation scheme.