Purpose In relation to rapid development of possible applications of unmanned vehicles, new opportunities for their use are emerging. Among the most dynamic, we can distinguish package shipments, rescue and military applications, autonomous flights and unattended transportation. However, most of the UAV solutions have limitations related to their power supplies and the field of operation. Some of these restrictions can be overcome by implementing the cooperation between unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs). The purpose of this paper is to explore the problem of sensor fusion for autonomous landing of a UAV on the UGV by comparing the performance of precision landing algorithms using different sensor fusions to have precise and reliable information about the position and velocity. Design/methodology/approach The difficulties in this scenario, among others, are different coordination systems and necessity for sensor data from air and ground. The most suitable solution seems to be the use of widely available Global Navigational Satellite System (GNSS) receivers. Unfortunately, the position measurements obtained from cheap receivers are encumbered with errors when desiring precision. The different approaches are based on the usage of sensor fusion of Inertial Navigation System and image processing. However most of these systems are very vulnerable to lightning. Findings In this paper, methods based on an exchange of telemetry data and sensor fusion of GNSS, infrared markers detection and others are used. Different methods are compared. Originality/value The subject of sensor fusion and high-precision measurements in reference to the autonomous vehicle cooperation is very important because of the increasing popularity of these vehicles. The proposed solution is efficient to perform autonomous landing of UAV on the UGV.

Energy production and supply are important challenges for civilisation. Renewable energy sources present an increased share of the energy supply. Under these circumstances, small-scale grids operating in small areas as fully functioning energy systems are becoming an interesting solution. One crucial element to the success of micro-grid structures is the accurate forecasting of energy consumption by large customers, such as factories. This study aimed to develop a universal forecasting tool for energy consumption by end-use consumers. The tool estimates energy use based on real energy-consumption data obtained from a factory or a production machine. This model allows the end-users to be equipped with an energy demand prediction, enabling them to participate more effectively in the smart grid energy market. A single, long short-term memory (LSTM)-layer-based artificial neural network model for short-term energy demand prediction was developed. The model was based on a manufacturing plant’s energy consumption data. The model is characterised by high prediction capability, and it predicted energy consumption, with a mean absolute error value of 0.0464. The developed model was compared with two other methodologies.

No abstract

No abstract

In the paper experimental investigations related with analysis of navigational precision of three chosen GNSS receivers are shown. Used receivers allow for measurement of navigational signals in following modes of operations: receiving signals from single-frequency GPS system, dual-frequency GPS/GLONASS system, and receiving signals from GPS constellation with use of differential measurements. In the last mode the base station and mobile receiver were configured for transmitting/receiving differential corrections by pair of industry-grade radio modems. The most important features and configuration of navigational receivers for conducted experiment are presented. Afterward the features of computer program designed especially for simultaneous acquisition, analysis of quality parameters and archiving of navigational signals are shown. The results of conducted investigations are also shown. For each of the receivers quantity and quality parameters such as maximum and minimum numbers of visible satellites and DOP (dilution of precision) parameters achieved during the experiment are given.