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Functional Mapping for Human-Robot Cooperative Exploration

Geert-Jan Kruijff; Shanker Keshavdas
In: Dr. Mohamed Hamza (Hrsg.). International Journal of Computer and Applications (IJCA), Vol. 36, No. 1, ACTA Press, 3/2014.


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 interest like victims or threats. For a robot working with a human team, there are several challenges. From the viewpoint of task-work, there is time-pressure: The exploration needs to be done efficiently, and effectively. From the viewpoint 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. Without 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 mapping, as a subset of robotic mapping; aiming to address the problems in both efficiency (task), and apparency (team). First, we assess the situation awareness of rescue workers during a simulated USAR scenario and use this as an empirical basis to build our robots spatial model. 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 information relative to a 3D-landmark in the environment. The approach combines top-down logical and probabilistic inferences about 3D-structure and robot morphology, with bottom-up quantitative maps. The inferences result in vantage positions for information gathering which are optimal in a quantitative sense (effectivity), and which mimic human spatial understanding (apparency). A quantitative evaluation shows that functional mapping leads to significantly better vantage points than a naive approach.


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