Using a wavelet network to characterize real environments for haptic display

This paper will introduce a framework for characterizing real environments, using recorded force/displacement data, for use in haptic display. Steps in the framework include data acquisition, identification, model verification, and implementation. Identification and implementation will be developed in detail. After obtaining a conceptual understanding of the roles data acquisition and model verification play in the process, the methods used in this paper will be described. To meet the requirement for the identification stage, a proven technique in nonlinear system identification will be adopted. This technique, called wavelet network, will provide a tool that is capable of identifying environments with significant nonlinear features. A theoretical development along with experimental results will be presented using a spring attached to a wall. This environment exhibits a linear region with a single nonlinearity. The wavelet network was chosen because it was designed specifically for use with problems of high input dimension. Therefore, it is the expectation that the procedure will be useful in identifying environments of varying complexity. Currently, the technique can be used to identify static nonlinear environments. Work is being done to extend its capabilities to handle dynamic environments.