Publikation
Functional Mapping For Human-Robot Collaborative Exploration
Shanker Keshavdas; Geert-Jan Kruijff
In: Proceedings of the 2013 IASTED Symposium on Artificial Intelligence And Applications. IASTED International Conference on Artificial Intelligence and Applications (AIA-13), February 11-13, Innsbruck, Austria, ACTA Press, 2013.
Zusammenfassung
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.