# Cognitive Model of the Closed Environment of a Mobile Robot Based on Measurements
## 7 Discussion  
Research has shown that mobile robot controlled by a 5 \(\times\) 5 neural network has poor movement performance, as it tends to the linear description of a motion path (Figure 27).  
A learning phase in the simulation showed that a mobile robot tends to circular motion paths, driven by the assumption that a smaller neural network will have a more linear motion path.  
Figure 27: Neural network learning results through the neural network generations: (**a**) exploration score; (**b**) speed score (velocity coefficient); (**c**) difference score (velocity difference); (**d**) loop score (number of iterations); (**e**) distance score (distance reached); (**f**) angle score (angle between eMIR and free environment).  
The activation functions  
\[f(x)=\frac{16}{1+e^{0.55-8x}} \tag{6}\]  
\[f(x)=\left\{\begin{array}{ll}7x,&0<x<1\\ 7,&1<x<7\end{array}\right. \tag{7}\]  
where \(x\) represents the robot distance from an obstacle are the excellent starting point for continued research.  
This research emphasizes the possibility of a mobile robot's behavior in new unknown environments, with high state of similarity to human behavior in the same conditions of environments. The same is proven with conducted experiments.  
Reviewing papers about neuroevolution in mobile robotics, the exact parameters of neural networks and genetic algorithms were not mentioned. It means that there is no reference to an activation function, neural network scoring methods, population numbers in one generation and mutation percentages, which all were used in this research. This research also shows how increasing or decreasing neural networks affects the speed and quality of neural networks learning and training process.  
The activation Equation (6), has much more aggressive response at distances up to 2 m, i.e., that the derivation (or change) is closer to 0. At distances of 2 m and further, activation function has a very mild response, and it asymptotically approaches the value of 8.  
The activation Equation (7) is a linear function that multiplies the input by 7, within range of 1 m. The function rounds all greater distances than 1 m to a value of 7, i.e., a function is saturated above 1 m. With the activation function, the mobile robot's local environment within a radius of 1 m is described. The perimeter is placed around a mobile robot's current location, where is a need for robot behavior verification in correlation to its environment with included obstacles and limits. The mobile robot (neural network control algorithm) will not react to values further (larger) than 1 m. The mobile robot's exploration motion path used for the testing are shown in Figure 28.  
Before aiming for future research, it should first be mentioned that the research has been proven to reduce the gap between the simulated and the real movements of mobile robots in unknown environments. One of the guidelines was that all simulations, learning and testing processes and real system experiments are carried out on equipment easily available to the average user. Guided by these constraints, the authors propose increasing the polygon dimensions for experiments and more complex environments similar to those surrounding humans. That implicates using more advanced range sensors capable of detection, classification and cognitive perception of the more complex environments. Sensors must be thought of as an information collecting device, that forwards collected information to developed neural networks, i.e., mobile robots. The described experiments require more powerful computers for the learning, simulation and interpretation of learned knowledge on real mobile robots. However, it should be noted that a computer RaspberryPi 4B used in this research has its limitations for more complex tasks.