This study examines the use of Unmanned Aerial Vehicles (UAVs) in transportation, focusing on traffic monitoring and accident prevention. UAVs provide a cost-effective means for traffic surveillance, route planning, and accident analysis, enhancing data accuracy and timeliness. The paper discusses autonomous and human-intervention-supported drone systems for traffic surveillance, addressing technological and operational challenges and the balance needed for practical implementation. It also presents recent advancements, including a forerunner drone model, and references research on UAVs for maritime navigation safety, underscoring the need for their safe and efficient integration into transportation systems.

Unmanned Aerial Vehicles are being applied in an increasing number of fields; however, their autonomous operation is associated with significant regulatory challenges. In this study, the performance of a PID and a Model Predictive Controller is compared based on the transfer function of the BLDC motor of a quadcopter using MATLAB simulations in the presence of white noise. The simulation results are used as reference values for measurements conducted on a cost-effective, custom-developed prototype drone. The prototype has been designed for short-duration hovering, allowing for an initial evaluation, but a more thorough analysis requires prolonged hovering tests to be carried out in an industrial environment. Based on the results, a recommendation is formulated for improving the PID controller to achieve performance closer to that of the MPC. The research is aimed at enhancing the energy efficiency of UAV systems and optimizing battery capacity, enabling longer autonomous flight time and more reliable control.

The energy efficiency and hover stability of unmanned aerial vehicles are critical factors, since improper battery utilization and unstable control are major sources of operational failures and accidents. The proportional–integral–derivative (PID) controller, which is applied in approximately 97% of multirotor unmanned aerial vehicle (UAV) systems, is widely used due to its simplicity; however, it is sensitive to external disturbances and often fails to ensure optimal energy utilization, resulting in reduced flight time. Therefore, the experimental investigation of advanced control methods in a real physical environment is well justified. The objective of the present research is the comparative evaluation of seven control strategies—PID, linear quadratic controller with integral action (LQI), model predictive control (MPC), sliding mode control (SMC), backstepping control, fractional-order PID (FOPID), and H∞ control—using a single-degree-of-freedom drone test platform in a MATLAB R2023b-Arduino hardware-in-the-loop (HIL) environment. Although the theoretical advantages and model-based results of the aforementioned control methods are well documented, the number of real-time comparative HIL experiments conducted under identical physical conditions remains limited. Consequently, only a small amount of unified and directly comparable experimental data is available regarding the performance of different controllers. The measurements were performed at a reference height of 120 mm under disturbance-free conditions and under wind loading with a velocity of 10 km/h applied at an angle of 45°. The controller performance was evaluated based on hover accuracy, settling time, overshoot, and real-time measured power consumption. The results indicate that modern control strategies provide significantly improved energy efficiency and faster stabilization compared to the PID controller in both disturbance-free and wind-loaded test scenarios. The investigations confirm that several advanced controllers can be applied more effectively than the PID controller to enhance hover stability and reduce energy consumption.

This study analyzes how dynamic disturbances influence the decisions made during the human supervision of autonomous unmanned aerial vehicles. While previous research has primarily focused on control algorithms and system stability, the effect of disturbances originating from system dynamics on operator intervention behavior has been less extensively investigated. To examine this problem, a hardware-in-the-loop (HIL) experimental framework was developed, which is based on a previously validated unmanned aerial vehicles (UAVs) test platform and was adapted in this study to enable the investigation of human supervisory decision-making. Participants observed the behavior of an autonomously operating system under controlled disturbances and were provided with the possibility to intervene by activating an emergency landing mechanism. The results indicate that the disturbance intensity had a significant effect on intervention decisions, while the reaction times did not show notable differences. This finding suggests that supervisory behavior is primarily determined by the evaluation of the system state rather than by timing characteristics. It also identifies that subjective risk perception plays a decisive role in the formation of intervention decisions, indicating the presence of an implicit decision threshold for participant behavior. The research findings offer a novel approach to the interpretation of human–UAV interaction by emphasizing the role of system dynamics in shaping user decisions. The presented method may provide a foundation for the development of predictive and adaptive supervisory systems that take into account the characteristics of human decision-making, thereby contributing to the design of safer and more efficient autonomous systems.

In recent times, the application areas of unmanned aerial vehicles (UAVs) are expanding, and autonomous functions are becoming increasingly prominent, eliminating the need for remote pilot control. Certain applications require the drone to use a docking station autonomously, even for wireless charging. This involves performing autonomous takeoff and landing, where high preci-sion is demanded. These maneuvers can be significantly influenced by environmental factors. The effectiveness of the precision landing function of the DJI Mavic 2 Pro UAV in strong winds is in-vestigated in this article based on experimental measurements conducted outdoors. New findings on the precision landing capabilities of the DJI Mavic 2 Pro UAV are presented in this paper, par-ticularly under windy conditions. The efficiency of precision landing is compared in cases when the precision landing function is enabled versus when it is disabled. It is investigated whether there is a statistically significant difference between the two landing modes in strong winds and it is examined whether the DJI Mavic 2 Pro can perform precision landing on a surface of a specific size in windy conditions. This helps to predict the suitability of the DJI Mavic 2 Pro for precise landing and wireless charging at a docking station in strong winds and high gusts.

The aim of the present study was to investigate a control strategy designed for the BLDC (Brushless Direct Current) motor of a quadcopter-type drone, with particular emphasis on the precise and stable maintenance of altitude in a disturbed environment. Two different control methods were implemented and compared during the research: the classical PID (Proportional–Integral–Derivative) controller and the H∞ (H-infinity) control technique. The investigations were carried out along two approaches. On the one hand, a transfer function—reproduced from a previous study—was used as a possible reference, and tested in the MATLAB simulation environment. On the other hand, a custom-developed physical prototype with one degree of freedom was created, capable of vertical motion along a single axis, allowing for the examination of altitude control under real-world conditions. The purpose of the system was to maintain a predetermined hovering altitude even in the presence of external disturbances, such as artificially generated wind. During the design of the control algorithms, a state-space-based modeling approach was applied, and appropriate weighting functions were defined, with special attention given to robustness against disturbances, control accuracy, and energy-efficient operation. The simulation results showed that the H∞ controller reduced average power demand by 19.43% compared to PID control, while practical measurements demonstrated a 38% decrease in average power consumption. In addition, overshoot was reduced by 96% and oscillation amplitude by 86% under wind disturbance. The objective of the research was to examine the practical applica-bility of an advanced control method that can provide greater stability, reliability, and energy effi-ciency under varying environmental conditions compared to traditional solutions.