# Swarm of Drones in a Simulation Environment--Efficiency and Adaptation
[PERSON]  
1 Department of Distributed Systems and Informatic Devices, Faculty of Automatic Control, Electronics and Computerareas to detect and track wildfire outbreaks). In the case of responding to crisis situations, drone swarms can effectively deliver medical supplies or food to regions with limited access. Drone swarms also offer groundbreaking tactical capabilities in a military context (autonomous reconnaissance and the execution of combat missions with minimal risk to crewed units). Therefore, solutions aim to mitigate the threats arising from the use of unmanned aerial vehicles (UAV) [3; 4]. Drone swarms represent a true revolution, due to their ability to perform complex, coordinated actions. The control of a drone swarm is executed based on advanced decision-making algorithms, utilizing sensors and computational units located on the drone [1]. In the evolving field of automation and robotics, controlling drone swarms poses a challenge that engineers and scientists are striving to meet. A comprehensive insight into swarm engineering is provided in [5]. The article focuses on the intricacies of designing, implementing, and verifying control algorithms for swarm systems. The diversity of practical applications of swarm robotics, along with the challenges of their practical deployment, are emphasized in [6]. The paper in [7] highlights the unmanned aerial vehicle (UAV) systems being extensively applied in various fields like smart agriculture, search and rescue operations, and military surveillance. The concept of distributed consensus, which is pivotal in coordinating multiple drones, is elaborated in [7; 8]. This encompasses the theoretical foundations and practical implementations of a swarm of drones. The utility of swarm intelligence in adaptive routing within telecommunication networks is explored in [9]. The article illustrates the versatility of swarm-based approaches. An explanation and verification of methods for flight formation and autonomous coordination of drones in real-world conditions is presented in [10]. The unique challenges faced by non-holonomic robots in modeling and controlling formation are discussed in [11]. A detailed review covering the history, technologies, and applications of drones is provided in [12]. An exploration of the biological inspirations for swarm robotics and their application in heterogeneous swarms across various contexts is provided in [13; 14]. In addition to the foundational works on drone management and coordination, such as the distributed consensus model [8], recent research has introduced unsupervised methods for characterizing drone swarms using RF signal analysis and machine learning [15]. They provide a comprehensive review of reliability assessment methods for drone swarms, emphasizing the importance of structural analysis and the importance of evaluation in ensuring operational reliability [16]. An exploration of evolutionary algorithms in enhancing the design and problem-solving capabilities of swarm robotics is presented in [17]. The article in [18] demonstrates the innovative applications of swarm robotics in theoretical and practical domains. Distributed collaborative partial SLAM for efficient navigation of autonomous robots [19] and the development of control algorithms for group formation flights in dense environments [20] can improve coordination within drone swarms. In this article, we present our research conducted using simulation methods. We used software-in-the-loop (SITL) [21] to study the control mechanisms of drone swarms. The presented simulation methods are essential to determine the impacts of positioning errors, speed variations, and communication delays from the point of view of preserving a specific flight structure within a drone swarm. One of the aspects presented in the article is estimating the minimum distance between drones, dependent on the flight speed, to enable collision-free flights of the swarm.