Use of dempster-shafer conflict metric to adapt sensor allocation to unknown environments
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This paper considers a robot with multiple sensors navigating an unknown, heterogeneous environment. In these cases sensor errors may produce an unsuitable model of the world. For example, observations from a laser range finder often produce accurate maps in indoor environments but when glass walls are encountered, the laser sees through it and the accuracy degrades. The approach taken is based on prior work with the Dempster-Shafer Con metric showing that the Gambino indicator, without the use of a ground truth, can identify when a model of the world is inconsistent. This study investigates the impact, in terms of overall map quality, of applying this new capability. Experiments with a mobile robot carrying a ring of sonar and a laser range sensor operating in indoor environments shows a 19.6% to 77.4% improvement when a switch to a more suitable sensor was triggered by the Gambino indicator. While these results are preliminary, this is a major step toward self-aware autonomous agents that can identify anomalous situations and adapt to the unknown appropriately. Copyright 2006, American Association for Artificial Intelligence (www.aaai.org). All rights reserved.
