Abstract. The expanding capabilities of today's microcontrollers and other devices lead to an increased utilization of these technologies in diverse fields. The automation and issue of remote control of moving objects belong to these fields. In this project, a microcontroller Raspberry Pi 2B was chosen for controlling DC motors and servo-motors. This paper provides basic insight into issue of controlling DC motors and servo-motors, connection between Raspberry and other components on breadboard and programming syntaxes for controlling motors in Python programming language.

This work deals with investigative methods used for evaluation of the surface quality of selected metallic materials’ cutting plane that was created by CO2 and fiber laser machining. The surface quality expressed by Rz and Ra roughness parameters is examined depending on the sample material and the machining technology. The next part deals with the use of neural networks in the evaluation of measured data. In the last part, the measured data were statistically evaluated. Based on the conclusions of this analysis, the possibilities of using neural networks to determine the material of a given sample while knowing the roughness parameters were evaluated. The main goal of the presented paper is to demonstrate a solution capable of finding characteristic roughness values for heterogeneous surfaces. These surfaces are common in scientific as well as technical practice, and measuring their quality is challenging. This difficulty lies mainly in the fact that it is not possible to express their quality by a single statistical parameter. Thus, this paper's main aim is to demonstrate solutions using the cluster analysis methods and the hidden layer, solving the problem of discriminant and dividing the heterogeneous surface into individual zones that have characteristic parameters.

Recently, the interest in development of service robots highly increases. The annual turnover in this area is estimated to a number close to 17 milliard Euro in the past years. The annual turnover could rise to 0.1 billion Euro by 2020 by IFR considering 30 % growth every year. Investments are expected to flow into all areas related to service robotics, mainly into the development of assistant robots for seniors and the handicapped people, health monitoring and surgical robots, robots in agriculture, pilotless drones and helicopters and ground vehicles without a driver. Very promising years seems to be coming for all the new and already existing companies focused on this area with their software and sensor engineers together with producers of important accessories.

The danger of terrorism is a result of the increased risk of critical infrastructure. We will focus on enumerating each area then listing of possible real uses in security and protection and finally we will focus in the list of used 3D sensors (in practice focused mainly on the use of laser scanning of space, camera sensing and subsequent transfer to cyberspace or scanning with infrared sensors).

This paper deals with the design and construction of a neural network for predicting the results of roughness parameters for heterogeneous surfaces. At the same time, it demonstrates that other statistical methods, especially regression analysis, fail in this respect, and their results cannot be used reliably. The samples produced using waterjet cutting were used to obtain the necessary data for constructing the neural network. Its heterogeneity characterizes this surface. This paper describes these samples, the parameters of their creation, the laboratory measurements, the complete construction of the neural network and the subsequent comparison of the results with regression functions. This paper aims to design a functional neural network that will best describe the roughness pattern of the surface under study. This neural network will predict this waveform based on the input variables and prove that this advanced statistical method completely exceeds the capabilities and predictive value of conventional regression analyses.