Explore the vast range of titles available.

Klemas, V.V., 2015. Coastal and environmental remote sensing from unmanned aerial vehicles: An overview. Unmanned aerial vehicles (UAVs) offer a viable alternative to conventional platforms for acquiring high-resolution remote-sensing data at lower cost and increased operational flexibility. UAVs include various configurations of unmanned aircraft, multirotor helicopters (e.g., quadcopters), and balloons/blimps of different sizes and shapes. Quadcopters and balloons fill a gap between satellites and aircraft when a stationary monitoring platform is needed for relatively long-term observation of an area. UAVs have advanced designs to carry small payloads and integrated flight control systems, giving them semiautonomous or fully autonomous flight capabilities. Miniaturized sensors are being developed/adapted for UAV payloads, including hyperspectral imagers, LIDAR, synthetic aperture radar, and thermal infrared sensors. UAVs are now used for a wide range of environmental applications, such as coastal wetland mapping, LIDAR bathymetry, flood and wildfire surveillance, tracking oil spills, urban studies, and Arctic ice investigations.

This article deals with quadcopter modeling using a system for the measurement of unmanned aerial vehicle (UAV) parameters. UAVs are often equipped with various measurement devices and equipment for measurement, which significantly affects their weight. The currently available technical solutions and inventions do not allow corrections to be made to the on-board control electronics settings without the need to perform a test flight, or without the need to create complex and time-consuming mathematical models of the unmanned aerial vehicle; therefore, it is desirable to create a new method for modeling the characteristics of an UAV based on static laboratory measurements. The goal of this paper is to create a dynamic model of a quadcopter that will be adapted to a system for measuring UAV parameters, specifically the thrust of individual motors, which will be the next step to creating a new method for modeling UAV characteristics. This method can be used in the future for tuning flight control algorithms, based on static laboratory measurements.

This study focuses on designing a controller for trajectory tracking of quadcopters using advanced sliding-mode techniques. Specifically, an integral terminal sliding-mode control based on an adaptive barrier function with a super-twisting reaching law is employed to achieve precise trajectory tracking. The performance of the controller is enhanced by applying Particle Swarm Optimization to fine-tune the gain values. The nonlinear dynamics of the quadcopter are modeled using the Euler–Lagrange approach, followed by a Lyapunov stability analysis to verify the stability of the controller. The adaptive barrier function is used to prevent control signal saturation, while the third-order sliding-mode controller effectively reduces the chattering. Additionally, a saturation function is introduced to further mitigate the chattering effect. The effectiveness of the proposed approach is demonstrated through numerical simulations, and its performance is further validated through controller-in-the-loop implementation. The results show that the proposed method significantly improves trajectory-tracking accuracy.

Constrained by fixed frame dimensions, conventional drones usually demonstrate insufficient capabilities to accommodate complex environments. However, the reconfigurable drone can address this limitation through its deformable frame equipped with actuators or passive interaction mechanisms. Nevertheless, these additional components may introduce an excessive weight burden, which conflicts with the lightweight objective in aircraft design. In this work, we propose a novel reconfigurable quadrotor inspired by the swimming morphology of jellyfish, with only one actuator placed at the centre of the frame to achieve significant morphological reconfiguration. In the design of the morphing mechanism, three telescopic sleeves are driven by the actuator, enabling arms’ rotation to achieve a maximum projected area reduction of 55%. The nested design of sleeves ensures a sufficient morphing range while maintaining structural compactness in the fully deployed mode. Furthermore, key structural dimensions are optimized, reducing the central motor load by up to 65% across configurations. After deriving parameter variations during morphing, Proportion–Integration–Differentiation (PID) controllers are implemented and flight simulations are conducted in MATLAB. Results confirm the drone’s sustained controllability during and after reconfiguration, with an “8”-shaped trajectory tracking root mean square error (RMSE) of 0.109 m and successful traversal through long narrow slits, reducing mission duration under certain conditions.

Unmanned Aerial Vehicles (UAVs) are increasingly utilized for urban patrol and defense owing to their low cost, high mobility, and rapid deployment. This paper proposes a multi-UAV mission planning model that takes into account mission execution rates, flight energy consumption costs, and impact costs. A kinematics and dynamics model of a quadcopter UAV is established, and the UAV’s flight state is analyzed. Due to the difficulties in addressing 3D UAV kinematic constraints and poor uniformity using traditional optimization algorithms, a lightning search algorithm (LSA) based on multi-layer nesting and random walk strategies (MNRW-LSA) is proposed. The convergence performance of the MNRW-LSA algorithm is demonstrated by comparing it with several other algorithms, such as the Golden Jackal Optimization (GJO), Hunter–Prey Optimization (HPO), Pelican Optimization Algorithm (POA), Reptile Search Algorithm (RSA), and the Golden Eagle Optimization (GEO) using optimization test functions, Friedman and Nemenyi tests. Additionally, a greedy strategy is added to the Rapidly-Exploring Random Tree (RRT) algorithm to initialize the trajectories for simulation experiments using a 3D city model. The results indicate that the proposed algorithm can enhance global convergence and robustness, shorten convergence time, improve UAV execution coverage, and reduce energy consumption. Compared with other algorithms, such as Particle Swarm Optimization (PSO), Simulated Annealing (SA), and LSA, the proposed method has greater advantages in addressing multi-UAV trajectory planning problems.

The capabilities of a quadcopter in the hover mode for low-altitude sensing of atmospheric turbulence with high spatial resolution in urban areas characterized by complex orography are investigated. The studies were carried out in different seasons (winter, spring, summer, and fall), and the quadcopter hovered in the immediate vicinity of ultrasonic weather stations. The DJI Phantom 4 Pro quadcopter and AMK-03 ultrasonic weather stations installed in different places of the studied territory were used in the experiment. The smoothing procedure was used to study the behavior of the longitudinal and lateral spectra of turbulence in the inertial and energy production ranges. The longitudinal and lateral turbulence scales were estimated by the least-square fit method with the von Karman model as a regression curve. It is shown that the turbulence spectra obtained with DJI Phantom 4 Pro and AMK-03 generally coincide, with minor differences observed in the high-frequency region of the spectrum. In the inertial range, the behavior of the turbulence spectra shows that they obey the Kolmogorov–Obukhov “5/3” law. In the energy production range, the longitudinal and lateral turbulence scales and their ratio measured by DJI Phantom 4 Pro and AMK-03 agree to a good accuracy. Discrepancies in the data obtained with the quadcopter and the ultrasonic weather stations at the territory with complex orography are explained by the partial correlation of the wind velocity series at different measurement points and the influence of the inhomogeneous surface.

This paper provides a comprehensive investigation of nonlinear robust control methodologies, with a specific emphasis on the development of sliding mode controllers (SMCs) for quadcopter unmanned aerial vehicles (UAVs). Quadcopters are highly interconnected and underactuated and, thus, pose challenges in controlling them, especially in the presence of disturbances like wind. SMC is a widely employed approach that proves practical for managing the intricate nonlinear dynamics of UAVs with substantial coupling. The principal merit of SMC lies in its remarkable capability to reject external perturbations and uncertainties. This paper offers an extensive survey on robust control design techniques, specifically focusing on SMC design for quadcopter UAVs. This paper also delves into different SMC design approaches, such as classical SMC, super-twisting SMC (ST-SMC), terminal SMC(TSMC), adaptive SMC, backstepping SMC, event-triggered SMC, and neural network-based SMCs for quadcopters. This paper provides a detailed study of the different SMC designs to achieve various objectives for the UAV in the presence of uncertainties and disturbances. Simulations of the various SMCs are presented that demonstrate the comparative performance of the UAVs for different objectives. Finally, this article serves as an information foundation that covers various aspects of the SMC design for quadcopters.

This paper aims to address the trajectory tracking problem of quadrotors under complex dynamic environments and significant fluctuations in system states. An adaptive trajectory tracking control method is proposed based on an improved Model Predictive Path Integral (MPPI) controller and a Multilayer Perceptron (MLP) neural network. The technique enhances control accuracy and robustness by adjusting control inputs in real time. The Multilayer Perceptron neural network can learn the dynamics of a quadrotor by its state parameter and then the Multilayer Perceptron sends the model to the Model Predictive Path Integral controller. The Model Predictive Path Integral controller uses the model to control the quadcopter following the desired trajectory. Experimental data show that the improved Model Predictive Path Integral–Multilayer Perceptron method reduces the trajectory tracking error by 23.7%, 34.7%, and 10.9% compared to the traditional Model Predictive Path Integral, MPC with MLP, and a two-layer network, respectively. These results demonstrate the potential application of the method in complex environments.

Unmanned aerial vehicles (UAVs) face significant challenges in maintaining stability when subjected to external wind disturbances and internal noise. This paper addresses these issues by introducing a real-time wind speed fitting algorithm and a wind field model that accounts for varying wind conditions, such as wind shear and turbulence. To improve control in such conditions, a deep reinforcement learning (DRL) strategy is developed and tested through both simulations and real-world experiments. The results indicate a 65% reduction in trajectory tracking error with the DRL controller. Additionally, a UAV built for testing exhibited enhanced stability and reduced angular deviations in wind conditions up to level 5. These findings demonstrate the effectiveness of the proposed DRL-based control strategy in increasing UAV resilience to wind disturbances.

This paper presents a robust approach for achieving accurate indoor positioning and autonomous navigation of quadcopters through the fusion of multiple radars and an inertial measurement unit (IMU) named hybrid-filtered Radar–Inertial Odometry (Hybrid-RIO). The Hybrid-RIO system integrates four-directional Frequency-Modulated Continuous-Wave (FMCW) millimeter-wave (mmWave) radars simultaneously with a high-performance IMU to continuously estimate the quadcopters’ position, velocity, and orientation even in low-light and indoor environments. The autonomous flight commands from the system further enable indoor navigation without requiring human intervention. Experimental results reveal notable advancements in both the accuracy and consistency of positioning. The integration of the proposed Hybrid-RIO approach holds promise in a wide spectrum of domains, including cave exploration, tunnel rescue operations, and indoor navigation solutions.