Smart cities require the use of many different types of sensors to make the communication, and distance sensors are one of the most commonly used elements in transportation systems and related infrastructures. The introduction of increasingly advanced autonomous systems in many areas of smart cities requires high measurement precision of the sensors used. High precision is essential for proper operation, long-term use, and safety in machine-to-machine or machine-to-human interactions. This paper presents a comparison of the accuracy of distance measurements for two commercially available single-beam LiDARs and two ultrasonic sensors. The aim of the research was to develop a calibration method in order to improve the accuracy of distance sensors. Based on the collected distance measurements, the sensors were calibrated using selected machine learning algorithms. The results of the experiments show the effectiveness of the proposed calibration methods, which yield an average mean absolute error (MAE) of 1.76 E-05 meters (m) and a root mean square error (RMSE) of 1.33 E-04 m for the tested sensors.

In the presented paper, detailed schematics and descriptions concerning communication in the context of swarm drone control are introduced. Methods and technologies behind them are discussed. The implementation of the presented concept was verified through a series of tests. Simulation results which confirm the effectiveness and efficiency of the proposed solutions are presented. The obtained results prove the correctness implementation of the presented methods and also demonstrate the benefits derived from the proposed approach. The presented concept of controlling a swarm of drones represents the current state of knowledge and technology in the field of drones and their control. By utilizing advanced communication technologies, positioning, and analysis of communication structures, this work makes a significant contribution to the UAV (Unmanned Aerial Vehicle) field.

This paper presents an environment simulator dedicated to assess the behavior of Automated Guided Vehicles in industry. The concept proposed in this framework makes it possible to evaluate the hazards that can occur in an industrial plant. Conducting all of the necessary research in a natural environment requires a great deal of time and is very expensive. A simulation environment reduces the costs and also reduces the burden of the verification process of an AGV operation. This study presents an assessment of simulation environments based on the most proven solutions. The analysis were conducted from the point of view of the requirements that have been set by industry. As a result, the authors built the evaluation environment that was based on the Gazebo simulator.

We reviewed the existing and currently used approach in processing the residual charge of an AGV battery. The method of setting up the experiment for collecting the historical data for an AGV Formica 1 of the AIUT company was proposed and implemented. The collected properties of the time series were analyzed and the algorithm for the necessary data pre-processing was selected. This algorithm includes padding any the suppression spontaneous peaks, the recovery of any lost data and data normalization.The collected data for the AGVs were also analyzed using the correlation analysis methods (Pearson, Spearman and Kendall correlations). These determined the parameters on which the AGV battery discharge depends. A battery discharge prediction approach that is based on the quasi-stochastic signal's probabilistic characteristics is suggested.A Multiparameter ANN model using a time window was developed. The dependence of the forecast error on the length of the time window was also investigated. The optimal parameters of the ANN were selected experimentally. The mean absolute percentage error for the AGV short-term forecast of a battery discharging was less than 1%. For the other parameters on which it depends, the AGV battery discharging was less than 9%. All of the studies were conducted within the framework of the "Automated Guided Vehicles integrated with Collaborative Robots for Smart Industry Perspective" project.

This article presents the comparative analysis of various JavaScript libraries, providing the possibilities of web-based geo-visualization, that can be applied in the case of AGV (Automated Guided Vehicle) working in the open area using GNSS (Global Navigation Satellite Systems) localization systems. A representative group of JS libraries has been selected for analysis. We focused on libraries that enable map generation with the use of the Scalable Vector Graphics format. Their performance issues and development potential are compared.