Landing Of Unmanned Aerial Vehicle Research Articles

Practical Distributed Control for VTOL UAVs to Pass a Virtual Tube

Unmanned Aerial Vehicles (UAVs) are now becoming increasingly accessible to amateur and commercial users alike. An air traffic management (ATM) system is needed to help ensure that this newest entrant into the skies does not collide with others. In an ATM, airspace can be composed of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">airways, intersections</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nodes.</i> In this paper, for simplicity, distributed coordinating the motions of Vertical TakeOff and Landing (VTOL) UAVs to pass an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">airway</i> is focused. This is formulated as a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">tube passing problem</i> , which includes passing a virtual tube, inter-agent collision avoidance and keeping within the virtual tube. Lyapunov-like functions are designed elaborately, and formal analysis based on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">invariant set theorem</i> is made to show that all UAVs can pass the virtual tube without getting trapped, avoid collision and keep within the virtual tube. What is more, by the proposed distributed control, a VTOL UAV can keep away from another VTOL UAV or return back to the virtual tube as soon as possible, once it enters into the safety area of another or has a collision with the virtual tube during it is passing the virtual tube. Simulations and experiments are carried out to show the effectiveness of the proposed method and the comparison with other methods.

Nonlinear Control of a Single Tail Tilt Servomotor Tri-Rotor Ducted VTOL-UAV

This paper explores a novel single tail tilt servomotor tri-rotor ducted vertical takeoff and landing unmanned aerial vehicle system (VTOL-UAV), and proposes a compound control method combining disturbance observer, model predictive control (MPC) and sliding mode nonlinear dynamic inversion (NDI), and realizes the robust tracking control of the VTOL-UAV trajectory under compound disturbance. Specifically, the inner loop adopts nonlinear dynamic inverse which improved by sliding mode to realize the pseudo linearization of the system. The outer loop adopts the model predictive control based on the E-SSPC (State Space Predictive Controller based on the Error model) method, on this basis, the sliding mode disturbance observer based on fast Super-twisting algorithm is introduced into the position loop to observe and compensate the disturbance in real time, which improves the robustness of the outer loop system. Numerical simulation experiments verify the effectiveness and robustness of the control method. Finally, the flight test of the VTOL-UAV is carried out.

A Robust and Accurate Landing Methodology for Drones on Moving Targets

This paper presents a framework for performing autonomous precise landing of unmanned aerial vehicles (UAVs) on dynamic targets. The main goal of this work is to design the methodology and the controlling algorithms that will allow multi-rotor drones to perform a robust and efficient landing in dynamic conditions of changing wind, dynamic obstacles, and moving targets. Unlike existing GNSS-based vertical landing solutions, the suggested framework does not rely on global positioning and uses adaptive diagonal approaching angle visual landing. The framework was designed to work on existing camera-drone platforms, without any need for additional sensors, and it was implemented using DJI’s API on Android devices. The presented concept of visual sliding landing (VSL) was tested on a wide range of commercial drones, performing hundreds of precise and robust autonomous landings on dynamic targets, including boats, cars, RC-boats, and RC-rovers.

Justification of the choice of radiation sources for a computer vision system in the problem of automatic landing of unmanned aerial vehicles

An expedient spectral range of radiation sources for use in the automatic landing system of unmanned aerial vehicles has been substantiated. A method is proposed for synchronizing the photoexposure of the computer vision system and radiation from landmarks for the unambiguous determination of their mutual position. Results of the experimental studies are presented.

Homography-Based Visual Servoing for Underactuated VTOL UAVs Tracking a 6-DOF Moving Ship

This paper develops a novel homography-based visual servo control method for the vertical take-off and landing (VTOL) unmanned aerial vehicle (UAV) tracking the trajectory of a 6 degrees of freedom (6-DOF) moving ship. Different from the classical homography-based visual servoing which is only suitable for the trajectory tracking of a planar moving target, an extended homography-based visual servoing framework is proposed to track the trajectory of a 6-DOF moving target regardless of its time-varying rotational motion. Taking entries in the homography matrix as feedback, the visual dynamics of UAV is established and decoupled into a translational motion sub-dynamics and an attitude sub-dynamics based on the hierarchical control strategy. In the translational motion subsystem, the robust controller is designed by embedding the nonlinear differentiator, the adaptive method, and a smooth saturation model in the backstepping control scheme. The saturation model is introduced to generate the constrained control inputs to ensure the nonsingular attitude extraction and help the visual points remain in the camera’s field of view (FoV) simultaneously. In the attitude subsystem, a robust state-constrained control method is proposed by utilizing a robust control barrier function (RCBF), the quadratic programming (QP) and a saturation model to guarantee the visibility, where RCBF can accommodate unknown dynamics. The theoretical analysis demonstrates the asymptotic stability of the closed-loop system. Simulations are carried out to further validate the controller performance.

Local Control of Unmanned Air Vehicles in the Mountain Area

The task of increasing the accuracy and stabilization of the flight of unmanned aerial vehicles (UAV) in the alpine environment is a complex problem. It is related to the evaluation of UAV flight parameters and control conditions for the operator’s place. The purpose of the UAV’s autonomous flight control is to ensure stable control of the UAV’s flight parameters. Flight control systems are affected by various disturbances caused by both internal and external conditions. In general, the number of autonomous control systems corresponds to the number of degrees of freedom, which determines the flight of an autonomous vehicle. An important factor in assessing the quality of such a UAV is its readiness for an autonomous flight together with the level of its safe guidance on the route. The presented article focuses on the analysis of UAV flight control and the quality of prediction and elimination of errors that exist during maneuvers toward the place of a successful UAV landing. The aim of the article is to point out the solvability of the complexities of such a flight procedure with the evaluation of the readiness for the descent phase of the autonomous UAV. The given problem is caused by the social demand for the creation of a way of providing health care in the mountain area of the High Tatras in Slovakia. The existing database of data obtained from the flying vehicles used in Slovakia was compared with the data obtained from the simulated flights, with their subsequent evaluation in the MATLAB software (Version R2021b) environment.

Adaptive Fault-Tolerant Control of a Hybrid VTOL UAV against Actuator Faults and Model Uncertainties under Fixed-Wing Mode

This paper proposes an adaptive fault-tolerant control strategy for a hybrid vertical take-off and landing (VTOL) unmanned aerial vehicle (UAV) to simultaneously compensate actuator faults and model uncertainties. With the proposed adaptive control schemes, both actuator faults and model uncertainties can be accommodated without the knowledge of fault information and uncertainty bounds. The proposed control scheme is constructed with two separate control modules. The low-level control allocation module is used to distribute the virtual control signals among the available redundant actuators. The high-level control module is constructed with an adaptive sliding mode controller, which is employed to maintain the overall system tracking performance in both faulty and uncertain conditions. In the case of actuator faults and model uncertainties, the adaptive scheme will be triggered to generate more virtual control signals to compensate the virtual control error and maintain the desired system tracking performance. The effectiveness of the proposed control strategy is validated through comparative simulation tests under different faulty and uncertain scenarios.

Proactive Guidance for Accurate UAV Landing on a Dynamic Platform: A Visual-Inertial Approach.

This work aimed to develop an autonomous system for unmanned aerial vehicles (UAVs) to land on moving platforms such as an automobile or a marine vessel, providing a promising solution for a long-endurance flight operation, a large mission coverage range, and a convenient recharging ground station. Unlike most state-of-the-art UAV landing frameworks that rely on UAV onboard computers and sensors, the proposed system fully depends on the computation unit situated on the ground vehicle/marine vessel to serve as a landing guidance system. Such a novel configuration can therefore lighten the burden of the UAV, and the computation power of the ground vehicle/marine vessel can be enhanced. In particular, we exploit a sensor fusion-based algorithm for the guidance system to perform UAV localization, whilst a control method based upon trajectory optimization is integrated. Indoor and outdoor experiments are conducted, and the results show that precise autonomous landing on a 43 cm × 43 cm platform can be performed.

High Speed Safe Autonomous Landing Marker Tracking of Fixed Wing Drone Based on Deep Learning

Most of the current research on autonomous landing of Unmanned Aerial Vehicles (UAVs) focus on rotorcrafts, which can fly horizontally over the landing site and then land vertically, with less landing risk. However, some of the unmanned fixed-wing aircrafts take off and land in the sliding mode of wheel landing gears, and their landing stage is difficult to control and easier to failure. In this paper, the autonomous landing of the fixed wing UAVs based on the visual navigation is studied. We propose a deep learning based Vision Transformer Particle Region-based Convolutional Neural Network (VitP-RCNN). Our VitP-RCNN uses Mobile Vision Transformers (MobileViT) as the backbone to accelerate feature extraction. Regarding the innovation of the present study, it is noticed that candidate boxes are chosen as the particles in our methodology, where the confidence is defined as the particle weight, the spatio-temopral correlation between adjacent image is used in video tracking and the Particle Filter theory is employed into the two-stage detection network to construct the present VitP-RCNN. Based on the spatial-temporal correlation, Our VitP-RCNN is efficient in predicting the particle state in the next frame through the state of the previous frame, so as to realize the continuous tracking of the landing marker. The experimental results show that on the Jetson AGX Xavier drone platform, our speed is up to 51FPS, enabling us to perform the landing marker tracking for the autonomous landing of the fixed-wing drones.

Development of algorithmic support for detecting an aircraft carrier using machine vision technology for automatic landing of UAV

The problem of detecting an aircraft carrier to measure the position and orientation of an unmanned aerial vehicle (UAV) is considered. An algorithm for selecting ROI regions by using motion detection and significance analysis via the spectral residual method is proposed. Based on the BoW model, a classification algorithm is constructed that combines SIFT feature extraction, K-means clustering, and the SVM classifier. The test results confirmed the high performance and adequate accuracy of aircraft carrier detection by using the proposed set of algorithms. Keywords aircraft carrier landing, UAV, object detection, motion detection, spectral residual method, SIFT feature, K-means clustering, support vector method

OpenCV Implementation of Grid-based Vertical Safe Landing for UAV using YOLOv5

The challenge of proving autonomous landing in practical situations is difficult and highly risky. Adopting autonomous landing algorithms substantially minimizes the probability of human-involved mishaps, which may enable the use of drones in populated metropolitan areas to their full potential. This paper proposes an Unmanned Aerial Vehicles (UAV) vertical safe landing & navigation pipeline that relies on lightweight computer vision modules, able to execute on the limited computational resources on-board a typical UAV. In this work, a grid-based mask technique is proposed for selecting the safe landing zones where each grid is parameterizable based on the size of the UAVs, which is implemented using OpenCV. A custom trained YOLOv5 model is the underlying building block for safe landing algorithm which is trained for aerial views of pedestrians, cars & bikes to identify as obstacles. The nearest obstacle-free zone algorithm is applied over the YOLOv5 output where boundary box locations are identified using Hue Saturation Value (HSV) filtering and then split into grids for safe landing zones where maximum coverage is taken into account while analyzing each scene. It performs a 2-level operation to prevent collisions while descending at different altitudes. Since UAV is expected to be processing only at predetermined altitudes, which will shorten the processing time, generating a PID signal for UAV actuators to navigate to the required safe zone with utmost safety and accuracy.

Methodology for assessing the accuracy of UAV landing on the runway

The problem of formalizing the accuracy criteria for landing an unmanned aerial vehicle (UAV) is considered. A review of existing approaches to solving this problem is carried out. A linear dependence of the allowable heading and glide path deviations of the distance from the UAV to the point of the beginning of the glide path is derived. Graphs of permissible deviations, taking into account the class of the runway are obtained. Keywords unmanned aerial vehicle, landing, runway

Autonomous capture of agile flying objects using UAVs: The MBZIRC 2020 challenge

In this paper, a novel approach for autonomously catching fast flying objects is presented, as inspired by the Mohamed Bin Zayed International Robotics Challenge (MBZIRC) 2020. In this competition, an autonomous Unmanned Aerial Vehicle (UAV) was used to intercept a ball carried by a fast flying drone. The presented solution utilizes a 3D LiDAR sensor for quick and robust target detection. The trajectory of the target is estimated and predicted to select a suitable interception position. The interceptor UAV is navigated into the interception position to safely approach the target. The interception position is frequently being adjusted based on the updated estimation and prediction of the target’s motion to ensure that the ball is caught in the dedicated onboard net. After a successful interception is detected, the UAV lands in a designated landing area. The proposed concept was intensively tested and refined in demanding outdoor conditions with strong winds and varying perception conditions to achieve the robustness required by both the demanding application and the competition. In the MBZIRC 2020 competition, our solution scored second place in Challenge 1 and first place in a combined Grand Challenge. This manuscript will provide a detailed description of the applied methods and an evaluation of our approach with data collected from real-world experiments. In addition, we present achievements of our R&D towards the transition from the MBZIRC competition to an autonomous drone interceptor, which was the main motivation of this challenge.

Control of Attitude Dynamics of an Unmanned Aerial Vehicle with Reinforcement Learning Algorithms

In this study, some applications of model-dependent and model-free learning based control techniques are presented for the control of attitude dynamics of vertical take-off and landing unmanned aerial vehicle. Towards this goal, reinforcement learning control algorithms are examined. Control algorithms are discussed and the main differences are presented. A number of numerical simulations are carried out on the attitude control of the system and the results are discussed. Performance evaluation of the proposed learning-based control method has been carried out.

Semi-empiric noise modeling of a Cargo eVTOL UAV by means of system identification from flight noise measurement data

This publication shows the semi-empiric noise modeling of an electric-powered vertical takeoff and landing (eVTOL) unmanned aerial vehicle (UAV) by means of system identification from flight noise measurement data. This work aims to provide noise models with a compact analytical ansatz for horizontal and vertical flight which are suited for integration into a geographical information system. Therefore, flight noise measurement campaigns were conducted and evaluated. An existing noise model ansatz is adapted to the eVTOL UAV under consideration and noise models are computed from the measurement data using the output error method. The resulting models are checked for plausibility by comparing them to technical literature. The horizontal flight noise model is subjected to a correlation analysis and the influence of meteorological effects are examined. To achieve a higher level of accuracy in future noise modelings, an optimization of the microphone positions as well as the flight trajectory is carried out.

Nonparametric Method for Predicting the Trajectory of an Actively Maneuvering Vessel for Unmanned Aerial Vehicle Landing

The article is devoted to the development of algorithms for predicting the trajectory of maneuvering objects based on nonparametric systems theory. The analysis of uncertainties affecting the modeling of the movement maneuvering water objects is presented. An overview of parametric, nonparametric and combined methods for predicting maneuvering water objects trajectory is given. The problem of high-precision autonomous control of the landing unmanned aerial vehicles on the landing vessel in the conditions of its irregular movement caused by meteorological conditions and active maneuvering is being solved. The method for predicting the trajectory of a vessel’s movement based on solving direct problems of dynamics using nonparametric systems theory is proposed. The advantages of the proposed method are that it’s not affected by model errors, due to the fact that it is based only on a retrospective analysis of several consecutive values of the spatial vessel coordinates. The proposed method differs from similar nonparametric methods in that it does not require statistical calculations, own training, or time-consuming tuning. The method does not imply the solution of identification model parameters, state and control actions problems and can be applied with any unknown linearizable input control actions, including when the model of the vessel’s motion dynamics is not identifiable. The results of numerical modeling for solution the problem of predicting the trajectory of an actively maneuvering small-sized landing vessel using a full nonlinear dynamic model with six degrees of freedom are presented. The studies carried out confirm the efficiency, adequacy and very fast adjustment of the developed method under conditions of complete parametric and nonparametric uncertainty. The proposed method can be used to predict the trajectory of any vehicle under the condition of linearizability of its model and control signals over the observed time interval.

Analysis of the UAV Landing Quality Evaluation Using the TOPSIS Method

The era of automation has arrived. A few decades ago, almost all flying machines were operated by humans. Nowadays, practically all aircraft are either fully automated or on their way there. The advent of features like autopilot is the result of the drive toward complete automation. Unmanned aerial vehicles (UAVs) are powered aircraft without a crew. It employs a gas or electric engine to fly and adheres to the fundamentals of aerodynamics. UAVs are easier to create and cost less to produce than manned aircraft. Compared to traditional manned aircraft, UAVs offer greater flexibility, safety, and mobility. While human aircraft are required to use an airfield, certain UAVs can take off and land without one. We can tell those landing incidents are likely to occur when UAVs are landed thanks to the extensive expert experimentation and historical data on UAV landings. Currently, determining the quality of a UAV landing is primarily based on the experience of the experts or a single metric exceeding the limit, with unavoidably subjective and unscientific findings. This study analyses and improves the unmanned aerial vehicle's landing quality using the MCDM approach TOPSIS. LQ1 is ranked eight, LQ2 is ninth rank, LQ3 is third-ranked, LQ4 is ranked fourth, LQ5 is second-ranked, LQ6 is ranked fifth, LQ7 is ranked sixth, LQ8 is ranked first and LQ9 is seventh-ranked. The result of the landing performance of the unmanned aerial vehicle by using TOPSIS is eighth landing is best followed by the fifth and third.

Experimental tests of hybrid VTOL unmanned aerial vehicle designed for surveillance missions and operations in maritime conditions from ship‐based helipads

AbstractThis article presents research on a hybrid vertical take‐off and landing (VTOL) unmanned aerial vehicle (UAV) supported by a relative positioning system, enabling the deployment of autonomous missions from ship‐based helipads in maritime conditions. A crucial issue to be solved is ensuring precise positioning of the UAV relative to the landing pad in the take‐off and landing phases. To achieve this, an extended Kalman filter (EKF) is implemented on the UAV's onboard computer, which integrates the positioning data from the UAV's global navigation satellite system receiver and the positioning data of the landing pad broadcast by the landing pad navigation station (LNS). The EKF estimates both the absolute and relative position of the UAV, which are required for autonomous take‐off and landing on the moving landing pad. In unfavorable weather conditions, EKF also uses data from an optional local positioning system to keep the accuracy within the range 1–3 m. The research was concluded by experimental verification during a ferry cruise over the Baltic Sea. During the research, the VTOL UAV performed two fully autonomous flight missions at the range about 1 km from the moving ferry in international waters. Each of them ended with a successful landing back on the helipad with an accuracy matching its required level, which was already achieved in the previous research carried out in inland conditions. Data recorded during real flights confirm that the developed system, consisting of a hybrid VTOL UAV and a LNS, is ready to be utilized in autonomous missions at sea. Factors having a critical impact on the safety of use of the VTOL UAV in marine conditions were also identified, which were not observed during the research in inland conditions, and are related directly to turbulence around the landing pad.

On the Visual-Based Safe Landing of UAVs in Populated Areas: A Crucial Aspect for Urban Deployment

Autonomous landing of Unmanned Aerial Vehicles (UAVs) in crowded scenarios is crucial for successful deployment of UAVs in populated areas, particularly in emergency landing situations where the highest priority is to avoid hurting people. In this work, a new visual-based algorithm for identifying Safe Landing Zones (SLZ) in crowded scenarios is proposed, considering a camera mounted on a UAV, where the people in the scene move with unknown dynamics. To do so, a density map is generated for each image frame using a Deep Neural Network, from where a binary occupancy map is obtained aiming to overestimate the people's location for security reasons. Then, the occupancy map is projected to the head's plane, and the SLZ candidates are obtained as circular regions with a minimum security radius, within the head's plane. Finally, to keep track of the SLZ candidates, a multiple instance tracking algorithm is implemented using Kalman Filters along with the Hungarian algorithm for data association. Several scenarios were studied to prove the validity of the proposed strategy, including public datasets and real uncontrolled scenarios with people moving in public squares, taken from a UAV in flight. The study showed promising results in the search of preventing the UAV from hurting people during emergency landing.

VIAE-Net: An End-to-End Altitude Estimation through Monocular Vision and Inertial Feature Fusion Neural Networks for UAV Autonomous Landing.

Altitude estimation is one of the fundamental tasks of unmanned aerial vehicle (UAV) automatic navigation, where it aims to accurately and robustly estimate the relative altitude between the UAV and specific areas. However, most methods rely on auxiliary signal reception or expensive equipment, which are not always available, or applicable owing to signal interference, cost or power-consuming limitations in real application scenarios. In addition, fixed-wing UAVs have more complex kinematic models than vertical take-off and landing UAVs. Therefore, an altitude estimation method which can be robustly applied in a GPS denied environment for fixed-wing UAVs must be considered. In this paper, we present a method for high-precision altitude estimation that combines the vision information from a monocular camera and poses information from the inertial measurement unit (IMU) through a novel end-to-end deep neural network architecture. Our method has numerous advantages over existing approaches. First, we utilize the visual-inertial information and physics-based reasoning to build an ideal altitude model that provides general applicability and data efficiency for neural network learning. A further advantage is that we have designed a novel feature fusion module to simplify the tedious manual calibration and synchronization of the camera and IMU, which are required for the standard visual or visual-inertial methods to obtain the data association for altitude estimation modeling. Finally, the proposed method was evaluated, and validated using real flight data obtained during a fixed-wing UAV landing phase. The results show the average estimation error of our method is less than 3% of the actual altitude, which vastly improves the altitude estimation accuracy compared to other visual and visual-inertial based methods.