Using maps from local sensors for volume-removing tools

Abstract

Currently industrial robotic systems employ almost exclusively global maps for navigation purposes, if any. Additional information - intra-process, spatial, current, and persistent sensor data - is useful to cope with uncertainty, measurement errors, and incompleteness of data. We propose to augment robot world models by using local sensors (which provide data from a local epsiv-environment) and build precise maps from local sensors, with force and audio classification in orthopedics applications with a medical robot system (RONAF) as an example. Improving precision of this map-building is presented both for data localisation and data insertion.