This paper introduces a pioneering approach to robot environment representation by integrating a novel hypergraph-based method for modeling fuzzy signatures with a quadtree-like structure for obstacle detection. By structuring fuzzy signatures through hypergraphs, we establish a robust framework that not only streamlines information representation but also simplifies the aggregation-based decision-making process. This synergy is applied to the domain of mobile robotics, where accurate and efficient environment representation is essential. Utilizing a quadtree-like structure for data organization, our technique systematically evaluates feature points against a set of fuzzy operations, determining the significance of obstacles and reconstructing the environmental model through the traversal of the quadtree-like structure. Furthermore, the hypergraph-based formalism sets the stage for a future transition to a tensor-based representation of fuzzy signatures, as envisioned in future work.