## 1 Introduction  
Environmental cognition is a fundamental skill for mammalian foraging and survival. Physiological studies have indicated that mammals, when freely moving in unfamiliar environments, are capable of maintaining relative spatial relationships to nests or food through specific cognitive mechanisms. This provides them with positional information for navigation in unfamiliar environments and enables real-time updates based on changes in external environmental cues, thus endowing them with strong perceptual abilities in unknown surroundings [1; 2; 3; 4]. However, existing mobile robot technologies fail to utilize distance information between themselves and obstacles or walls to update their current position when facing unexpected obstacles or barriers. Therefore, investigating and replicating the environmental cognition mechanisms observed in mammals holds significant importance in enhancing the environmental cognition capabilities of mobile robots and advancing our understanding of biological environmental cognition [5; 6; 7]. In 1971, [PERSON] et al. found, in the rat hippocampus, a cell with a selective firing to spatial locations. This cell undergoes firing activity only when the rat is in a spatially specific environmental location [8]. This cell is called a place cell and its corresponding spatial firing area is called the place field [9; 10]. In 2005, [PERSON] et al. identified another type of cell in the entorhinal cortex of rats that produces periodic firing to specific regions of space grid cells and whose hexagonal firing fields spread throughout the spatial environment with the movement of the rat [11]. Related studies have shown that when rats move freely in a two-dimensional space, grid cells in the entorhinal cortex undergo repetitive firing behavior at specific locations; furthermore, it was noted that their firing activity is highly stable and, as rats continue to explore the environment, the generated grid cells cover the entire environment and complete the spatial representation of the environment [12; 13; 14]. [PERSON] et al. proposed an oscillatory interference (OI) to model the hexagonal firing structure of the grid cell. In the model, the self-motion information (direction and speed) of the mobile robot was used as the input of grid cells to update and maintain the grid field [15]. However, the verification of the model remained in the simulation stage and did not realize effective map construction in the real environment. In [16], the rat simultaneous location and mapping (RatSLAM) model, which was based on a rodent model, was investigated. This model centralizes path integration information and external visual scene information into the pose cell and is able to perform navigation and map construction tasks. However, this model does not incorporate the physiological characteristics of the hippocampal structures in the rat brain, which thus leads to a lack of accuracy and a low stability with respect to this model [17]. To address the problems of the insufficient physiological characteristics of the RatSLAM method, [PERSON] et al. proposed a grid cell to place cell competitive neural network models in a Hebb learning algorithm, based on the phenomenon of lateral inhibition in the rat hippocampus, which conforms to the physiological characteristics of the hippocampal navigation cells and can realize the information transfer and can also map from grid cells to place cells [18]. [PERSON] et al. similarly used the Hebb learning algorithm in the work of constructing environmental cognitive maps by imitating the hippocampal cognitive mechanism in the rat brain. They also implemented the environmental cognitive map construction through a real physical platform and obtained better experimental results [19]. [PERSON] et al. found that the size of the place cell firing field changes somewhat when the rat moves to the environmental boundary; to explain this phenomenon in their experiments, [PERSON] et al.