# Cognitive Model of the Closed Environment of a Mobile Robot Based on Measurements
## 2 Problem Description
### System Description  
The main goal of the research presented in this paper is experimental checks, corrections, and adjustments of the used autonomous mobile robot's behavior on the real polygon. Additionally, adjusting the real robot system's behavior to match the virtual simulation robot's model behavior is needed [30]. All the robot's environment recognition and control algorithms were tested in the virtual reality software tool CoppeliaSim [31]. A virtual dynamic model of an eMIR (educational Mobile Intelligent Robot) mobile robot shown in Figure 4b and corresponding polygon configuration shown in Figure 5, are designed. Software support for constructing an experimental system is written in C++ programing language. By giving parameters and harmonizing these experiment outcomes, patterns and guidelines are provided for research, development and testing of algorithms for an autonomous cognitive behavior of mobile robot models in environments that do not have to be highly defined, for a much more comprehensive range of users, all with the aim of more effortless knowledge transfer [32]. The primary guideline of the proposed research in this paper is the development of a mobile robot behavior model that is entirely identical to the real system. The model is based on measurements. Within this research, the exploration of the environment by a mobile robot has been successfully conducted. The goal of building such a model is robot behavior ability to monitor lines on the polygon, plan the robot's motion path in the model environment, and avoid obstacles on the polygon using range sensors installed on the mobile robot. The proposed neuro-evolution learning is a cognitive navigation model that integrates cognitive mapping and the ability of episodic memory so that the robot can perform more versatile cognitive tasks. Three neural networks have been modelled for the construction of an environmental map. Neural networks have ten inputs representing laser range sensor beams (one beam every 36\({}^{\circ}\)). In two hidden layers, neural networks have 5, 10 or 15 neurons, with the corresponding biases. The neural networks also contain two outputs scaled to the mobile robot wheels velocities, described in Section 3. A system for storing and retrieving task-related information is also developed. Information between the cognitive map and the memory network is exchanged through appropriate encoding and decoding schemes. Neural network's learning scores are tested in a simulation environment that is also developed in this research. The cognitive system is eventually applied to the real mobile robot's system. Exploration, localization and navigation of the mobile robot are performed using this information data implementation. Real mobile robot experiments in the final experimental verification and validation proved the efficiency of the proposed system. The proposed research uses the following materials and equipment:  
* eMIR mobile robot of differential structure--is a mobile educational robot designed and built at the Faculty of Mechanical Engineering and Naval Architecture in Zagreb. It is equipped with an independent power supply, communication modules, camera module, range sensors and software interface. The eMIR is shown in Figure 1a. For this research, an additional mobile robot eMiR (white) has been made. * A polygon with dimensions 2 \(\times\) 4 m--an enclosed environment in which eMIR mobile robots perform various tasks. Environment floor and side walls are white to make the environment more vision system ready. There is the possibility to configure it with additional district/sector partitions if needed. * The camera above the polygon--captures the tasks on a polygon, and it can calculate on the vision base robot's position and orientation. The dynamics of the object tracking system is approximately ten calculations per second, which is quite sufficient, for the research purpose. The camera can also detect additional partitions because of its black coloured edges.