## 1 Introduction  
In robotics, the robust localization on an object is very crucial in the perspective of physical tasks. Specifically, in navigation, the localization and path integration is really a big issue over which several efforts have taken place for more than 20 years [1; 2; 3]. The very first approach proposed for the localization of an agent was the extended Kalman filter (EKF) [4]. The complexity of EKF is quadratic with respect to the landmark's number, and thus, it behaves badly in a large environment [5]. The localization is also crucial for the object handling tasks, where body motion integration is required with a cognitive map of an object. Several other cognitive tasks can be done using body motion integration like picking an object, recognizing an object by touch, etc. In the context of object-handling tasks, the work is surely not appropriate. In human brain research, it has been well established that the collaborative network of grid and place cell neurons are responsible for learning the spatial semantics of an environment/object. Moreover, the network integrates the body motion with the learned semantics and helps a human in localization within an environment/object [6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16]. Using such localization, a human does several physical tasks. Several bio-inspired modeling works have been published on localization that has used the general idea of grid cell neuron. RatSLAM (Simultaneous localization and mapping) [17] is the most popular among all. The model has shown the localization using a 3D continuous attractor network of pose cells. The size of the network used in the model is dependent on the size of the environment; therefore, the model is highly infeasible for large environments. Numerous computational models of grid neurons have been published since 2005 that have generated the hexagonal grid patterns, like the continuous attractor [18] and oscillatory interference model [18], and hybrid oscillatory attractor network [19; 20]. However, the models have not addressed the problem of ambiguity in the interference of several grid patterns. Due to ambiguity, an agent can localize to the wrong place and direction. In the direction of modeling a place neuron, a new robotic architecture has merged the visual place cell with the grid cell to limit the angular drift of the path integration [21]. Despite a huge amount of theoretical models, none of the models has used the grid neuron and the place cell activities in performing the object-handling tasks. Moreover, the grid and the place cell neurons play a crucial role in the episodic memory as well [22; 23; 24]. As an episodic memory makes use of a place cell neuron in learning of procedures for making and recalling, predictions, and planning. Although the robots in current scenarios are using advanced sensors and advanced vision techniques to make actions [25; 26; 27; 28; 29; 30; 31; 32], still the techniques are so erroneous. Somewhere, we need body object integration along with the advanced vision and sensors for the smooth functioning and it may have many applications in machine learning, smart grids, and the energy sector [33; 34; 35; 36; 37; 38; 39; 40; 41; 42]. The article proposes a quadrant model for the grid neuron and an interference model for the place neuron modeling to address the aforementioned shortcomings and the existing research gaps of grid modeling. The quadrant model can generate the periodic hexagonal grid pattern from the navigational input of an artificial agent. Furthermore, an interaction system of place and grid neuron has also been proposed that can learn the cognitive map of an environment. The cognitive map learning is done using the integration of a quadrant grid neuron's activation pattern with the associated sensation of a place neuron. The proposed computational mechanism for the place neuron is able to localize the agent correctly in the mental representation of space and provides the ability to do path integration as well.