Building local maps in surgical robotics

Abstract

Currently, robotic systems employ almost exclusively global sensor information for navigation purposes. While a global map facilitates planning, it may have insufficient quality. Especially with autonomous robots, additional information - intra-process, spatial, current, and persistent sensor data - proves necessary to cope with uncertainty, measurement errors, and incompleteness of data. We aim at augmenting the world model for standard robotic systems by implementing local navigation and map-building based on local sensors (which provide local data without primary location information), with the use of force and audio classification in a medical robot system (RONAF) in orthopedics applications as an example.