This review paper presents an overview of depth cameras. Our goal is to describe the features and capabilities of the introduced depth sensors in order to determine their possibilities in robotic applications, focusing on objects that might appear in applications with high accuracy requirements. A series of experiments was conducted, and various depth measuring conditions were examined in order to compare the measurement results of all the depth cameras. Based on the results, all the examined depth sensors were appropriate for applications where obstacle avoidance and robot spatial orientation were required in coexistence with image vision algorithms. In robotic vision applications where high accuracy and precision were obligatory, the ZED depth sensors achieved better measurement results.

Terrain classification provides valuable information for both control and navigation algorithms of wheeled mobile robots. In this paper, a novel online outdoor terrain classification algorithm is proposed for wheeled mobile robots. The algorithm is based on only time-domain features with both low computational and low memory requirements, which are extracted from the inertial and magnetic sensor signals. Multilayer perceptron (MLP) neural networks are applied as classifiers. The algorithm is tested on a measurement database collected using a prototype measurement system for various outdoor terrain types. Different datasets were constructed based on various setups of processing window sizes, used sensor types, and robot speeds. To examine the possibilities of the three applied sensor types in the application, the features extracted from the measurement data of the different sensors were tested alone, in pairs and fused together. The algorithm is suitable to operate online on the embedded system of the mobile robot. The achieved results show that using the applied time-domain feature set the highest classification efficiencies on unknown data can be above 98%. It is also shown that the gyroscope provides higher classification rates than the widely used accelerometer. The magnetic sensor alone cannot be effectively used but fusing the data of this sensor with the data of the inertial sensors can improve the performance.

This paper presents the analysis of electrical and dielectric properties of the yttrium-doped nickel ferrite nano-powders synthesized using the co-precipitation method. Impedance and dielectric measurements have been carried out as a function of frequency at different temperatures from 200 to 25 °C in the range of 0.1 kHz–1 MHz. In order to investigate the conduction mechanism and highlight the role of yttrium doping in different concentrations, impedance spectroscopy was employed. The obtained data were analyzed in terms of equivalent circuits made of resistor and capacitor components describing the contributions from different electrical active regions in a material. Further, this study highlights the importance of a single constant phase element (CPE) in the description of dispersion behavior of the impedance response of the investigated samples in the given frequency range. The use of this technique enabled the characterization of grain and grain boundaries contribution in overall conductivity mechanism. The dielectric dispersion nature of all investigated materials is reflected in this study. Very high values of the real part of permittivity at low frequencies are assigned to space-charge polarization. The dependence of the real part of dielectric permittivity values of the yttrium content was also discussed. Doping with yttrium in different concentrations that reflects in different electric and dielectric responses is concluded in this study. The greatest change is noticed for the sample with the minimum dopant content for a x = 0.05 atomic percent share of yttrium. To reveal the potential role of more than one ion contribution to the overall relaxation process in investigated compounds, a modified Debye’s equation was utilized.

The utilization of stereo cameras in robotic applications is presented in this paper. The use of a stereo depth sensor is a principal step in robotics applications, since it is the first step in sequences of robotic actions where the intent is to detect and extract windows and obstacles that are not meant to be painted from the surrounding wall. A RealSense D435 stereo camera was used for surface recording via a real-time, appearance-based (RTAB) mapping procedure, as well as to navigate the painting robot. Later, wall detection and the obstacle avoidance processes were performed using statistical filtering and a random sample consensus model (RANSAC) algorithm.

In stochastic computing (SC) systems numbers are represented with mean values of random binary sequences. This paper introduces a novel fuzzy inference architecture, in which the computational mechanism is based on stochastic logic (SL). First, the basic concept of SL is described, then the architecture of the SL-based fuzzy logic controller (SFLC) is built up systematically using the derived stochastic elements. The second part of the paper demonstrates the application of the proposed techniques, where the SFLC-based control performance is evaluated on a real mechatronic system. The results show that the SL-based approach provides effective and robust control performance, simple architecture and high noise tolerance. The proposed method is also benchmarked against conventional FLCs indicating that the robustness of the stochastic architecture allowed to outperform the benchmark controllers in noisy environments.