The paper proposes a method for the synthesis of an adaptive control system (CS) for the spatial motion of a quadrotor unmanned aerial vehicle (UAV), ensuring desired motion dynamics under conditions of parametric uncertainty and external disturbances. The proposed CS is designed to maintain stability and tracking accuracy in the presence of model inaccuracies and unknown environmental effects, including time-varying disturbances. The CS has a cascaded structure with an outer position control loop and an inner attitude control loop, implemented using nonlinear position controllers (NPC) and nonlinear attitude controllers (NAC), respectively. Parametric uncertainties and external disturbances are estimated using variable structure observers (VSOs), whose estimates are incorporated into the control laws to provide real-time compensation of uncertainties. The synthesis of the CS and VSOs is based on a mathematical model of the UAV that accounts for the center-of-mass (CM) offset and key dynamic effects. The combined influence of external disturbances and parametric uncertainties is represented in the dynamic equations as an additive disturbance term. The effectiveness of the proposed approach is demonstrated through numerical simulations in stabilization and trajectory tracking tasks.
The objective of this study is to develop a stabilization system for unmanned aerial vehicles (UAVs) in agricultural industry using machine vision and inertial sensors. The proposed software solution is designed to collect and process visual and inertial data, estimate the UAV’s position in space, and support flight stabilization in conditions with limited or unavailable satellite navigation. The analysis conducted revealed that many existing UAV navigation and stabilization systems are based on GNSS. However, GNSS performance is limited in densely populated urban areas, forests, and other environments with unstable radio signals. Furthermore, GNSS signals are vulnerable to interference, jamming, and spoofing. Therefore, visual-inertial methods are a relevant approach for increasing UAV autonomy. A simulator based on ArduPilot SITL and the MAVLink interface was used as the modeling environment. UAV state estimation was performed using the OpenVINS visual-inertial platform, which combines inertial measurements with sparse visual trajectories using the MSCKF approach. The developed system demonstrates that inertial and visual data can be effectively used to estimate the UAV’s position and perform corrective control. The results demonstrate stabilization accuracy of up to 0.5 m, confirming the potential of the proposed approach.
Autonomous unmanned aerial vehicle (UAV) reconnaissance imposes three simultaneous requirements on onboard target detectors: high accuracy for small targets that frequently appear in medium-altitude imagery, robustness under realistic image degradation, and sufficient compactness for deployment on embedded hardware with strict power and latency constraints. This paper presents UAVR-YOLOv8, an enhanced YOLOv8-based detector designed to address these three requirements within a unified framework. The proposed model incorporates three main components: a P2 detection branch to better exploit shallow features for small-target detection, a Coordinate Attention module to enhance spatial feature representation, and a curriculum augmentation strategy covering motion blur, low-light, fog, sensor noise, and atmospheric haze. The model was trained and evaluated on a selected aerial image dataset consisting of UAV images containing three major target groups: main battle tanks, armored personnel carriers, and military trucks, captured under various terrain and daylight conditions. In addition to conventional evaluation metrics, this study introduces a robustness index to quantify model reliability under degraded imaging conditions, thereby extending the assessment beyond average accuracy on clean images alone. Experimental results on the NVIDIA Jetson Orin Nano show that the proposed model maintains strong detection accuracy on clean images, significantly improves performance retention under the five considered degradation types, and achieves real-time inference within the allowed power budget. The full ablation study further confirms that all three proposed modifications contribute positively, with small but consistent synergistic gains when integrated into a single design. These results indicate that an appropriate combination of proven techniques, together with evaluation under realistic operating conditions, can provide a practical detector for autonomous UAV reconnaissance deployment.
This paper examines the problem of controlling a swarm of unmanned aerial vehicles (UAVs) operating in a changing environment, with degraded communication quality and incomplete a priori information about the current situation, potentially leading to communication loss. It is shown that requirements must be imposed on the swarm of UAVs themselves, on the operator who controls the swarm and monitors its movements, as well as on the types of interfaces and their qualitative characteristics. A method for studying operator capabilities is proposed, based on determining the time characteristics of switching operating modes depending on observed phenomena and control capabilities. As part of the study, various methods of information processing are identified and their quality assessed. Based on these characteristics, the feasibility of controlling a swarm of UAVs and preventing loss of control over the swarm is assessed. To prevent false alarms, a justification for the most important characteristics is proposed. Modeling results show that the proposed approach reduces the impact of possible distractions to maintain control over the swarm of UAVs.
The paper deals with a control system synthesis method for a group of unmanned aerial vehicles (UAV) which can implement search or monitoring applications (missing people, fish exploration, aerial photography and so on). Very often this requires efficiency and occurs in adverse weather conditions of the sea, highlands, deep forest, high latitudes, etc. In such conditions, navigation and communication with the ground control station may be difficult. Group control strategy «leader-followers» reduces the risks to the equipment and simplifies the creation of the flight task. To do this, a UWB beacon is placed on the leader, which allows the followers to determine the distance to him and receive data on his movement parameters. As a result, all the followers can provide navigation and maintain a set formation relative to the leader, without communication with the ground control station. The simulation results confirmed the operability and effectiveness of the proposed method UAV group control method.
