This paper
addresses the problem of identifying the uncertainties present in a
robotic contact situation. These uncertainties are errors and misalignments of
an object with respect to its ideal position. The paper
describes how to solve for the errors caused during grasping
and errors present when coming into contact with the environment.
A force sensor is used together with Kalman Filters to
solve for all the uncertainties. The straightforward use of a
force sensor and the Kalman Filters is found to be
effective in finding only some of the uncertainties in robotic
contact. The other uncertainties form dependencies that cannot be estimated
in this manner. This dependency brings about the problem of
observability. To make the unobservable uncertainties observable a sequence of
contacts are used. The error covariance matrix of the Kalman
Filter (KF) is used to obtain new contacts that are
required to solve for all the uncertainties completely. There is
complete freedom in choosing which unobservable quantity to be excited
in forming the next contact. The paper describes how these
new contacts can be randomly executed. A two dimensional contact
situation will be used to demonstrate the effectiveness of the
method. Experimental data are also presented to prove the validity
of the procedure. Due to the non-linear relationship between the
uncertainties and the forces, an Extended Kalman Filter (EKF) has
been used.