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.
This study presents a new approach to modeling the study area, which involves discretizing its space into rectangular segments. A method for optimizing the coverage of the study area by unmanned aerial vehicle sensors by forming a minimal set of such rectangular segments is described. The sgeometric parameters (size, orientation, location) of the segments are taken into account to reduce the overall survey time. A typical unmanned aerial vehicle route is considered as a sequence of straight segments defined by control points. An algorithm for constructing an optimal unmanned aerial vehicle route, based on the ant colony principle, has been developed, along with controls for its movement along a given path. An unmanned aerial vehicle route consists of segments that can be classified as «active» (for scanning the area) and «idle» (for moving over areas not subject to survey). In most cases, such a route consists of alternating active and idle segments. A methodology and algorithm for unmanned aerial vehicle movement between control points corresponding to specified route segments are proposed. The process of finding the optimal solution is implemented using two interconnected algorithms: an ant colony behavior model and a control agent behavior algorithm. The applied control method involves continuous replanning of the unmanned aerial vehicle trajectory in real time, taking into account its parallel movement relative to the vector that specifies the direction.
Manual control of an unmanned aerial vehicle (UAV) with simultaneous video stream analysis imposes excessive cognitive load on the operator, leading to control and observation errors. Additionally, disturbances such as forested areas, urban infrastructure, and complex terrain hinder reliable object surveillance. This paper proposes a system in which the operator provides an initial indication of the object of interest, after which the visual tracking system autonomously maintains target tracking and generates control commands without further human intervention. All computations are performed on an onboard computer additionally installed on the UAV, which does not require communication with a ground station. To ensure compatibility with the output of the PID controller, an algorithm has been developed that allows the use of a standard flight controller without modifying its internal control loops. Experimental validation of the proposed solution was conducted in a custom-developed simulation environment based on Unity. PID controller coefficients were tuned empirically, and the configuration of the CSRT tracker was selected for operation with a low-contrast object with poorly defined boundaries. For the final system evaluation, a satellite antenna mounted on a communication tower was used as the object of interest. The tracking accuracy achieved was 0.05
Interest in bio-inspired robotics has grown considerably in recent years, driven largely by the potential for lightweight construction and high energy efficiency. In line with this trend, the present study employs numerical simulations to investigate dynamic deformation in a bio-inspired robotic structure. Three-dimensional dynamic simulations are performed using Ansys Mechanical (Transient Structural) to examine deformation behavior in the wing material under operational conditions. Numerical simulations are carried out over five periods to capture the deformation response. Although dynamic deformation can be described using various constitutive models – such as elastic, elastoplastic, and elastoviscoplastic frameworks – this work applies a linear elastic model to characterize the deformation behavior. The computational results obtained from this simplified approach demonstrate strong agreement with experimental data reported in the literature. This high degree of correlation not only validates the numerical model employed but also suggests that, under specific loading conditions, a linear elastic approximation may be sufficient to capture the essential dynamic characteristics of certain bio-inspired systems. These findings provide a foundational step toward understanding more complex deformation mechanisms and support the continued development of efficient, lightweight, bio-inspired robotic platforms for future engineering applications.
Development of biomorphic underwater robots employing locomotion principles of aquatic organisms represents an actively advancing area of modern robotics. Of particular interest are robots implementing thunniform locomotion, characteristic of highly efficient swimmers. This paper presents a fish-like robot whose design, based on the morphology of the yellowfin tuna, is optimized for experimental studies of swimming kinematics under laboratory conditions. The robot is equipped with a caudal propulsion system that generates forward motion through periodic oscillations of the tail fin, mimicking natural swimming kinematics. The article describes the robot’s structural features, internal layout, sensors, and principles of three-dimensional maneuvering. Experimental swimming kinematics studies were conducted in a pool using video recording followed by computer vision data processing. Swimming speed was determined under different operating modes of the tail actuator based on marker coordinates on the robot’s body. The dependence of swimming speed and energy efficiency on tail beat frequency at constant amplitude was investigated. Swimming speed increases approximately linearly with increasing frequency. The minimum Cost of Transport (COT) value was 6.22 J·kg⁻¹·m⁻¹, which is comparable to biological prototypes and the most efficient biomorphic robots. These results can inform the further design of energy-efficient fish-like underwater vehicles
The paper considers a method of forming and tracking trajectories of underwater moving objects (trajectory processing) by means of a small unmanned surface vessel, taking into account external disturbing effects from the marine environment. The proposed method is based on the processing of data received from a hydroacoustic station mounted on an unmanned surface vessel, and can be applied when it is not possible or impractical to deploy stationary hydroacoustic stations or when it is necessary to promptly complete a mission to track underwater objects in the water area where there is no appropriate infrastructure. Fuzzy logic methods are used to track moving objects, in particular to associate their measurements, an extended Kalman filter is used to estimate the position and velocity of underwater objects, and elementary rotation matrices are used to account for the varying orientation of a small unmanned surface vessel. In addition, this method takes into account the time delay in the propagation of the hydroacoustic signal. The simulation results confirmed the operability and effectiveness of the proposed method of tracking underwater objects.
Current real-world events are exposing a critical weakness in our standard operational frameworks. Against the backdrop of escalating hybrid conflicts, the biological protection of water resources demands a radical overhaul of our fundamental containment strategies. The resulting diagnostic vacuum - where no testing can occur- transforms water into an ideal vector for the mass transmission of pathogens. Targets and methods. Development, validation, and field testing of the «VirBac-1» robotic universal mobile complex was the main goal of research group. The architecture of the complex integrates colloid-chemical principle into an automated membrane concentration cycle operating under strictly controlled pH levels and ionic strength of the solution to ensure maximum capture efficiency. Results. Crucially, the total analytical cycle time has been radically compressed (to two hours): whereas standard regulatory frameworks require forty-eight to seventy-two hours. Conclusion. Consequently, the «VirBac-1» robotic complex establishes a fundamentally new technological architecture for continuous environmental monitoring of water bodies.
This paper is devoted to the development and analysis of control algorithms for an autonomous underwater vehicle (AUV) with an undulating fin propulsor. A short review is given of studies addressing the dynamic and kinematic models of underwater robots with flexible fins. A mathematical model of the vehicle interacting with the environment is implemented in the Simscape-MATLAB library. To solve the problem of motion control of the robot in course and depth, the Active Disturbance Rejection Control (ADRC) method is proposed. This approach extends the system model by introducing an additional state variable that accounts for factors not included in the mathematical description. The virtual state is estimated online by an observer and is used in generating the control signal, thereby compensating the external disturbances. Fuzzy logic methods based on physical and hydrodynamic laws and on human experience are applied. The advantage of the method is that it does not require an exact analytical model of AUV dynamics, particularly the relation between fin motion parameters and vehicle dynamics. Simulation results of AUV motion under rapidly varying external disturbances confirm the effectiveness of the proposed model and control strategy
Maintenance of agricultural spraying drones (UAVs) currently requires manual dosing of highly concentrated and toxic products (with dilution ratios of 1:100 and higher), which is not always performed with sufficient accuracy. The developed multi-channel modular system for mixing working solutions is designed for the automated preparation of liquids and refueling of UAVs. The developed system ensures versatility due to the ability to prepare solutions in a wide range of required dilution ratios from 1:10 to 1:1000 and higher while maintaining high dosing accuracy. A fundamental distinction of the developed system is the inclusion of dosing modules of two different designs, each optimized for a specific range of dosing volumes and concentrate dilution ratios. The system fully automates the preparation and refueling process, eliminating the need for the operator to be constantly present in the hazardous area near operating UAVs and minimizing their exposure to highly concentrated toxic substances. The system is designed for use as part of mobile or stationary autonomous service platforms for servicing UAV fleets.
The problem of target positions allocation in a formation of autonomous unmanned underwater vehicles (AUVs) under conditions of limited underwater communication is considered. A consensus-based auction algorithm (CBAA) is used to solve this problem, combining decentralized task selection and an assignment approval process between agents. A drawback of classical CBAA is the significant time required to execute multiple consensus phases, especially in an underwater environment where acoustic communication channels are characterized by low bandwidth and significant data transmission delays. A modification of the CBAA is proposed, making it need only a single consensus phase followed by local calculation of the final allocation by each agent based on the full set of bids from all participants. This approach eliminates the need for repeated consensus phases and shifts some of the workload from the communication system to the AUV’s onboard computing system. A comparative analysis of the communication complexity of the original and modified algorithms was conducted. The results confirm the potential of the proposed approach for rapid target positions allocation in large groups of AUVs.
This paper presents a review of state-of-the-art deep learning methods for video analytics in aquaculture over the period 2025–2026, complemented by an empirical evaluation of two lightweight YOLO-family neural network models for the fish detection task. Four key research directions are analyzed: fish disease detection, segmentation and phenotyping, tracking and biomass estimation, and the development of lightweight models. Based on contradictions identified between existing studies, an experimental comparison of two architectural configurations – the original YOLOv26 and YOLOv11 integrated with the Reparameterized Multi-Head attention module (RepMHD) – was conducted on the public DeepFish dataset, which contains images of fish in natural tropical marine habitats. The results show that the YOLOv11 + RepMHD configuration achieves higher Precision (0.978 vs. 0.960) and a comparable mAP@0.5 value (0.991 vs. 0.990) with 2.28 million parameters and 5.6 GFLOPs, whereas the original YOLOv26 demonstrates a higher mAP@0.5:0.95 value (0.816 vs. 0.812) with 2.38 million parameters and 5.2 GFLOPs. These findings indicate that the choice between these two strategies should be guided by the specifics of the application – particularly the priority assigned to minimizing false positive versus false negative detections.
This paper examines a class of telecommunication networks with mobile nodes, an important subclass within which is the so-called geography-aware networks. Their distinguishing feature is the availability of information about the geographic coordinates of all nodes in the network to each individual node. The purpose of this article is to investigate multipath routing algorithms. To this end, we propose a method for the automatic synthesis of adequate test models capable of representing networks of virtually unlimited complexity. The proposed solution is based on a compositional approach, in which a relatively simple network fragment that satisfies given constraints is first formed, and then the resulting model is constructed as a composition of copies of this fragment. To investigate the efficiency of multipath routing algorithms, we propose a compositional method for the random synthesis of test models of complex networks that satisfy constraints on distances between the vertices. This method was applied to the investigation of two routing algorithms, resulting in a large body of illustrative model data. The obtained results, presented in the form of graphs, demonstrate an increase in the gain in message transmission time as the queue length in the target flow grows.
Autonomous navigation in dynamic environments is a critical challenge, particularly when spaces are shared with other mobile agents whose future trajectories are known. While traditional grid-based planners efficiently find collision-free paths, their reliance on stop-and-turn mechanics over 2k-connected grids produces piecewise-linear trajectories that are kinodynamically highly sub-optimal for differentially constrained robots. In this paper, we present an adaptation of Safe Interval Path Planning (SIPP) that operates on state lattices, utilizing precomputed, kinodynamically smooth motion primitives. To efficiently handle dynamic environments, we rasterize the spatiotemporal swept volumes of moving obstacles directly onto the grid, treating grid cells as atomic units of space, whose resolution is typically dictated by inherent localization noise. We perform a comprehensive comparative analysis between our lattice-based approach and 2k-connected grid planners across diverse topological environments. Our evaluation considers a broad spectrum of performance metrics, including planning time, path angularity, cumulative heading change (angle-over-length), and bending energy. The results demonstrate that while the expanded state space of lattice-based search increases computational overhead, it yields trajectories with significantly superior kinodynamic properties. Specifically, our method achieves a reachability comparable to highly connected grids while ensuring smooth, continuous, and physically executable paths ready for real-world deployment.
This paper proposes an adaptive nonlinear neural-network-based control law for a line-following and obstacle-avoidance robot under dynamic model uncertainties. First, the mathematical model of the line-following and obstacle-avoidance robot (LF OAR) is unified with the conventional line-following model by employing LiDAR and camera sensors to construct a virtual line along the obstacle boundary and reconnect it with the main line, while the robot dynamics are represented in a general form. The proposed controller is developed based on a hierarchical control structure. At the kinematic level, the desired linear and angular velocities are generated using the Lyapunov approach to ensure smooth line-tracking performance, even when the reference path has large curvature. At the dynamic level, a controller is designed based on the general dynamic model of a differential-drive mobile robot using the super-twisting sliding mode control (STSMC) technique. The unknown nonlinear functions in the general dynamic model are approximated by radial basis function neural networks (RBFNNs), and an adaptive law is derived to update the network weights online. The results indicate improved tracking performance under the tested simulation and experimental conditions.
Autonomous mobile robots operating in rough terrain environments encounter considerable challenges due to irregular surfaces and complex terrain conditions. This paper presents a terrain-aware navigation framework that combines traversability assessment and motion planning to enhance navigation safety and robustness. A 2.5D elevation grid map is employed to model the geometric properties of the terrain. Based on this representation, a traversability assessment model is established using three terrain attributes: slope, height difference, and surface roughness. The resulting traversability cost map is integrated into a Traversability-A* (TA*) algorithm, which extends the conventional A* planner by jointly considering travel distance and terrain traversability during global path planning. In addition, a Timed Elastic Band (TEB) planner is adopted to refine the global path and generate feasible local trajectories. The proposed framework is evaluated through Gazebo based simulations using a Clearpath Warthog mobile robot navigating on uneven terrain. Experimental results demonstrate that the proposed framework generates safer trajectories by reducing terrain difficulty while maintaining navigation efficiency, thereby enhancing path feasibility and overall navigation performance in rough terrain environments.
The paper considers the problem of generating a navigation solution for a robotic system operating under changing environmental conditions, degradation of measurement channels, and incomplete a priori information about the current situation. It is shown that single-mode navigation algorithms with a fixed measurement processing structure do not always provide the required state estimation quality when the availability and reliability of navigation sources change. An adaptive navigation method based on discrete switching of operating modes depending on an integral uncertainty estimate is proposed. Within the method, diagnostic features are formed to characterize the consistency of measurements with the predicted state, the level of estimation uncertainty, and the structural availability of navigation channels. Based on these features, an integral uncertainty index is calculated and used to select one of the predefined processing modes: nominal data fusion, partial degradation of measurement information, or limited correction. To prevent false transitions, the selected mode is confirmed over several consecutive steps. Numerical simulation results show that the proposed approach reduces the influence of degraded measurements on state estimation and decreases the position error compared with a base line single-mode algorithm.
