A new sensor planning paradigm and its application to robot self-localization

Abstract

A new paradigm of sensor planning is presented based on a hierarchically distributed perception net (HDPN) proposed as a general sensing architecture. In the proposed parametric sensor planning, the uncertainties are propagated in HDPN, and the sensing parameters of HDPN are iteratively modified so that HDPN ultimately generates the desired accuracy of outputs at a minimum sensing cost. An experiment is conducted by applying the proposed parametric sensor planning method for the accurate self-localization of a mobile robot operating in a known environment. The proposed paradigm provides a formal, yet general and efficient method of representing and solving a sensor planning problem for an integrated sensor system.