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.

Software-Defined Network (SDN) is the new paradigm in the computer network architecture. The concept is based on the decoupling of the data plane from the control plane. Such decoupling creates the possibility for central traffic management, hence offering the potential to improve the network’s performance and monitoring capabilities. We use those capabilities to perform extensive testing of the communication system for the fleet of Automated Guided Vehicles (AGV). First, we present the configuration of our testbed, the methodology of performing such testing, and a framework for experiment design. Finally, we present our results based on the presented approach.

Automated Guided Vehicles (AGVs) are a key component of many modern industrial systems. AGVs are supposed to communicate with each other in real time using wireless networks. In this article, the advantages and disadvantages of the ZigBee wireless network related to the control of AGVs are considered. We analyze the performance of the ZigBee network programmed with both C# and Python libraries to control ZigBee devices. The throughput and signal strength are presented and discussed depending on the transmission speed of the serial port, the payload size, and the presence and distance from the obstacles. The results of the experiments show the effective values of these parameters, the methods of using C# and Python, and the reliability of the throughput up to a certain point in network devices.

Automated Ground Vehicles (AGVs) use deep-learning-based vision systems to perceive the surrounding environment and extract relevant information about it. Although deep learning models offer high capabilities, they require large amounts of data to be properly trained and tested. Testing is especially important when off-the-shelf models are used by the AGVs - to examine whether they can meet the demands of complex environments such as the production halls of automated factories. One area of such perception algorithms is object recognition. To test such systems, we propose a solution based on ArUco fiducial markers used for automatic labeling of objects. Our solution can be used to test deep learning systems in real time directly on a robot. Our solution requires minimal interference with the environment and additional infrastructure - the desired objects only need to be marked with a marker printed on a home printer. Therefore, the presented testing procedure can be used for testing of AGVs in real-life environments during a real ride from an actual robot perspective. Data gathered during the online testing can be used for the offline comparison of the accuracy of different deep learning models. Although we focus on the online and offline testing in our study, we also incorporated a marker masking procedure. Therefore, the resulting datasets may also be used for training.

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.

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.