This article presents a new approach to collision avoidance in drone swarms, designed for operations in large drone swarms and dynamic environments. The mechanism uses distributed communication, where drones share information about their positions and planned trajectories to predict and avoid collisions. The proposed mechanism enables drones to autonomously cooperate and maintain safe distances in complex scenarios. It is based on the concept of repulsion vectors. The avoidance response is determined by the level of immersion in the protective sphere of obstacles, including other drones. The advantage of the algorithm lies in its simplicity and low computational complexity, allowing it to be used even in small and inexpensive drones. The algorithm was tested in a developed simulation environment, created to handle swarms of over 20 drones and to demonstrate the scalability of the proposed solution. Two scenarios were analyzed: (i) two swarms, each with nine drones, flying on a collision course; (ii) a swarm of 25 drones changing formation. The results showed that the mechanism is effective in avoiding collisions, maintaining safe distances and adapting to changing conditions. The proposed mechanism represents a significant advancement in swarm coordination, offering a robust and scalable solution for real-world applications.

In the swiftly advancing field of swarm robotics and unmanned aerial vehicles, precise and effective testing methods are essential. This article explores the crucial role of software-in-the-loop (SITL) simulations in developing, testing, and validating drone swarm control algorithms. Such simulations play a crucial role in reproducing real-world operational scenarios. Additionally, they can (regardless of the type of application) accelerate the development process, reduce operational risks, and ensure the consistent performance of drone swarms. Our study demonstrates that different geometrical arrangements of drone swarms require flexible control strategies. The leader-based control model facilitates coherent movement and enhanced coordination. Addressing various issues such as communication delays and inaccuracies in positioning is essential here. These shortcomings underscore the value of improved approaches to collision avoidance. The research described in this article focused on the dynamics of drone swarms in a simulated context and emphasized their operational efficiency and adaptability in various scenarios. Advanced simulation tools were utilized to analyze the interaction, communication, and adaptability of autonomous units. The presented results indicate that the arrangement of drones significantly affects their coordination and collision avoidance capabilities. They also underscore the importance of control systems that can adapt to various situations. The impact of communication delays and errors in positioning systems on the required distance between drones in a grid structure is also presented. This article assesses the impact of different levels of GPS accuracy and communication delays on the coordination of group movement and collision avoidance capabilities.

The utilization of 2D Light Detection and Ranging (LiDAR) measurements does not always provide the precision needed to accurately determine the motion range or recalibrate the position of Autonomous Guided Vehicles (AGVs). Consequently, it is essential to employ filtering and calibration methods to enhance the precision and accuracy of measurements derived from 2D LiDAR. The article proposes a multi-sectional calibration (MSC) method incorporating a median filtration (MF) phase to enhance the measurement accuracy of 2D LiDAR. The investigation focused on identifying the optimal window width for the MF module among a selection of 2D LiDAR systems. The division of the complete measurement range into sections resulted in a significant enhancement in sensitivity to deviations in measurements. The efficacy of the proposed method is evidenced by its ability to enhance accuracy in distance measurements by up to 89% for the optimal window width. The experiments indicated that the proposed method has a significant impact on the precision and accuracy of distance measurements for 2D LiDAR systems.

Accurate localization is essential for Automated Guided Vehicles (AGVs) to ensure reliable motion planning and precise execution of docking tasks. A key challenge lies in robust environmental perception for industrial applications. This paper introduces a novel reflection-based marker detection and identification method that relies solely on two-dimensional Light Detection and Ranging (2D LiDAR) technology. The proposed docking method and 2D marker design enable the AGV to accurately estimate the marker’s distance and orientation, reliably identify it, and determine the docking point. Experimental validation on a heavy industrial AGV demonstrated that the docking method achieves accuracy of up to 1 cm in position and below 0.05 degree in YAW orientation. As a result, the AGV achieved docking precision at an assembly station with a standard deviation below 2 cm in X and Y axes and YAW orientation below 1.8 degree.