Sensor fusion with spatial uncertainties in a holonic multirobot workcell

Abstract

A holonic multi-robot cell is a cell specifying a system based on autonomous and cooperative units called holons. This paper presents a method using Bayesian estimation to model uncertainties in a multi-robot cell. Using sensor fusion with several robots and combining all the information together gives new possibilities for planning the sensing operations, e.g. to minimize the size of locating measurements. If the robots and work object are accurately modelled (noise model and surface forms), an iterative planner can be used to plan a minimal task sequence for the treatment of the work object. Further analysis of the remaining uncertainties is based on observing the direction of the eigenvectors and the respective eigenvalues of the error covariance matrix.