Consensus-Making Algorithms for Cognitive Sharing of Object in Multi-Robot Systems

[摘要] Visual recognition in multi-robot systems is afflicted with a peculiar problem that observations made from different viewpoints present different perspectives. Hence, realizing cognitive sharing of the object among robots in an unconstructed environment has become challenging. To cope with these issues, we have proposed the Hierarchical Invariants Perception Model (HIPM) in which multiple representations of the target are dynamically evaluated and selected by the robot. In this paper, we propose consensus-making algorithms to acquire a viewpoint-invariant representation of the geometric relation, which is an unaddressed issue in the HIPM. The target is described by a combination of three representations: color, shape, and geometric relation. In terms of geometric relation, we employ relative positions between the target and the salient objects, which we call a geometric-relation-based representation (GRR). A GRR is regarded as viewpoint-invariant when satisfying two conditions: (i) It consists of sharable objects and (ii) the number of target candidates, which is reduced by using the GRR, is equivalent among the robots. Based on this definition, consensus-making algorithms are formulated Experiments with real-world robots demonstrated that robots perceived the viewpoint-invariant GRR even when objects are occluded or their appearance changes. The experiment also demonstrated that the proposed algorithms were able to reduce the candidates without succumbing to an infinite loop. The success rate of cognitive sharing was about 60%. However, the success rate was 100% when GRR was shared. As long as robots can share the GRR, cognitive sharing may be realized even if the environment is more unstructured and uncertainties increase.

[学科分类] 人工智能

[关键词] Cognitive sharing;Multi-robot;Consensus making