Artikel
ART-based fusion of multi-modal perception for robots
Elmar Berghöfer; Denis Schulze; Christian Rauch; Marko Tscherepanow; Tim Köhler; Sven Wachsmuth
In: Neurocomputing, Vol. o.A. Pages 11-22, Elsevier, 5/2013.
Abstract
Robotic application scenarios in uncontrolled environments pose high demands on mobile robots. This is especially true if human-robot interaction or robotrobot interaction is involved. Here, potential interaction partners need to be identi ed. To tackle challenges like this, robots make use of di erent sensory systems. In many cases, these robots have to deal with erroneous data from di erent sensory systems which often are processed separately. A possible strategy to improve identi cation results is to combine di erent processing results of complementary sensors. Their relation is often hard coded and dicult to learn incrementally if new kinds of objects or events occur. In this paper, we present a new fusion strategy which we call the Simpli ed Fusion ARTMAP (SiFuAM) which is very fexible and therefore can be easily adapted to new domains or sensor con gurations. As our approach is based on the Adaptive Resonance Theory (ART) it is inherently capable of incremental on-line learning. We show its applicability in di erent robotic scenarios and platforms and give an overview of its performance.