Cognitive sharing of object with subgraph matching and entropy minimization in multi robot systems

Visual recognition in multi-robot systems is afflicted with a peculiar problem that observations made from different viewpoints bring different perspectives. Due to the lack of a discriminable representation between a target and its surroundings in different viewpoints, realizing cognitive sharing of the object among the robots in an unconstructed environment is a challenging issue. In this paper, we propose novel description algorithms of the target representation based on ambiguity minimization of peripheral context. The target is represented by a labeled-graph and its structure is determined by minimizing a metric of representational ambiguity using an entropy evaluation with metaheuristics. Experimental results show the significantly improvement of cognitive sharing by the proposed method.