DFKI-LT - Functional Mapping For Human-Robot Collaborative Exploration

Shanker Keshavdas, Geert-Jan Kruijff
Functional Mapping For Human-Robot Collaborative Exploration
2 Proceedings of the 2013 IASTED Symposium on Artificial Intelligence And Applications, Innsbruck, Austria, ACTA Press, IASTED, 2013
Our problem is one of a human-robot team exploring a previously unknown disaster scenario together. The team is building up situation awareness, gathering information about the prescence and structure of specific objects of in- terest like victims or threats. For a robot working with a human team, there are several challenges. From the view- point of task-work, there is time-pressure: The exploration needs to be done efficiently, and effectively. From the view- point of team-work, the robot needs to perform its tasks together with the human users such that it is apparent to the users why the robot is doing what it is doing. With- out that, human users might fail to trust the robot, which can negatively impact overall team performance. In this paper, we present an approach to the field of semantic map- ping, as a subset of robotic mapping; aiming to address the problems in both efficiency (task), and apparency (team). The approach models the environment from a geometrical- functional viewpoint, establishing where the robot needs to be, to be in an optimal position to gather particular in- formation relative to a 3D-landmark in the environment. The approach combines top-down logical and probabilis- tic inferences about 3D-structure and robot morphology, with bottom-up quantitative maps. The inferences result in vantage positions for information gathering which are op- timal in a quantitative sense (effectivity), and which mimic human spatial understanding (apparency). A quantitative evaluation shows that functional mapping leads to signifi- cantly better vantage points than a naive approach.
Files: BibTeX, FunctionalMappingAIA.pdf