UAV Autonomous Landing Research Articles

Autonomous Multirotor UAV Search and Landing on Safe Spots Based on Combined Semantic and Depth Information From an Onboard Camera and LiDAR

Autonomous Multirotor UAV Search and Landing on Safe Spots Based on Combined Semantic and Depth Information From an Onboard Camera and LiDAR

A Robust Strategy for UAV Autonomous Landing on a Moving Platform under Partial Observability

A Robust Strategy for UAV Autonomous Landing on a Moving Platform under Partial Observability

A UAV Autonomous Landing System Integrating Locating, Tracking, and Landing in the Wild Environment

A UAV Autonomous Landing System Integrating Locating, Tracking, and Landing in the Wild Environment

Integrated Learning-based Framework for Autonomous Quadrotor UAV Landing on a Collaborative Moving UGV

Integrated Learning-based Framework for Autonomous Quadrotor UAV Landing on a Collaborative Moving UGV

A UAV Localization System Based on Double UWB Tags and IMU for Landing Platform

The successful landing of UAVs on unmanned vehicle landing platforms is critical for resolving the problem of insufficient range of UAVs in practical missions. Existing localization techniques for autonomous UAV landing are primarily vision-based, but this method is limited due to reliance on external lighting conditions. In this paper, we propose a localization system for UAV landing on a mobile platform, which can be conveniently deployed on an unmanned vehicle landing platform. Four UWB anchors were deployed on the 0.8m×1.4m rectangular unmanned vehicle landing platform. The spacing of the two tags installed on the UAV and the ranging information were used as constraints, and the linear least squares method was used to calculate the iterative initial coordinates of the gradient descent method to solve for the positions of the two UWB tags and fuse them with the IMU data to provide accurate coordinate for the safe landing of the UAV. By comparing the experiments with the linear least squares method in five experiments, it is demonstrated that the average RMSE of the localization system proposed in this paper is reduced by 61%.

Autonomous landing algorithm of UAV based on Target tracking

The target tracking technology in the UAV autonomous landing process is studied. Based on the analysis of the occlusion, background change, and variable scale problems in the target tracking process, a scale adaptive method for UAV target tracking based on KCF (Kernelized Correlation Filter) algorithm is proposed. The original KCF target tracking framework is adopted to achieve the target position prediction function. Secondly, to address the variable scale problem of moving targets and the target loss problem caused by scale changes, the scale filter of the DSST target tracking algorithm is used. The algorithm is for estimating the scale of moving targets and generating a small-scale filter estimate to improve the tracking efficiency of the UAV for moving targets and reduce the target loss rate. The final test on the OTB dataset showed a 4% improvement in variable scale accuracy and a 2.9% improvement in variable scale success rate. A Gazebo 3D environment scene was set up under the Linux system, and UAV autonomous landing simulation experiments were conducted to verify the improved algorithm’s real-time effectiveness in the UAV autonomous landing application. The simulation results show that the improved KCF target tracking algorithm has real-time performance in the UAV autonomous landing trajectory tracking process.

Comparison of Deep Learning and Cascade Classifiers Approaches for Object Detection in Autonomous UAV Landing

فرود اتوماتیک یکی از موضوعات و چالش‌های مهم در حوزة‌ کنترل و اتوماسیون پهپادهای بدون سرنشین است. توسعة الگوریتم‌های فرود اتوماتیک نیازمند تعیین موقعیت پهپاد نسبت‌به محل فرود است که این کار، در حوزه‌های پردازش تصویر، به تشخیص دقیق و سریع محل فرود نیاز دارد. ازجمله روش‌های معمول، در این زمینه، طبقه‌بندی‌کنندة آبشاری و تناظریابی و قطعه‌بندی تصویر است که به‌نظر می‌رسد، با تغییرات آب‌و‌هوایی و مقیاس متفاوت، این الگوریتم‌ها با چالش مواجه شوند. از طرف دیگر، در سال‌های اخیر شبکه‌های کانولوشنی عمیق به‌منزلة مدل‌هایی قوی به‌منظور شناسایی و تشخیص اشیا در تصاویر به‌کار رفته‌اند؛ بااین‌حال با توجه به بار محاسباتی زیاد، این مدل‌ها هنوز در حوزة پرنده‌های بدون سرنشینی که از لحاظ سخت‌افزاری سبک‌اند و قدرت پردازش ضعیفی دارند، کاربرد جدی نیافته‌اند. هدف این مقاله مقایسة دو روش شبکه‌های عمیق کانولوشنی و طبقه‌بندی‌کنندة آبشاری برای تشخیص آنی محل فرود است. نتایج عملی‌کردن روش‌ ارائه‌شده روی یک پرندة Parrot AR Drone2.0 نشان می‌دهد که شبکه‌های کانولوشنی در مقابل دوران، مقیاس، انتقال و حتی پنهان‌شدگی پایداری بسیار زیادی دارند. دقت تشخیص در این روش 1/99 است که، در قیاس با روش طبقه‌بندی‌کنندة آبشاری، 3% بیشتر است و درعین‌حال‌ از لحاظ سرعت نیز، مناسب کاربردهای آنی است.

Visual Servoing Approach to Autonomous UAV Landing on a Moving Vehicle.

Many aerial robotic applications require the ability to land on moving platforms, such as delivery trucks and marine research boats. We present a method to autonomously land an Unmanned Aerial Vehicle on a moving vehicle. A visual servoing controller approaches the ground vehicle using velocity commands calculated directly in image space. The control laws generate velocity commands in all three dimensions, eliminating the need for a separate height controller. The method has shown the ability to approach and land on the moving deck in simulation, indoor and outdoor environments, and compared to the other available methods, it has provided the fastest landing approach. Unlike many existing methods for landing on fast-moving platforms, this method does not rely on additional external setups, such as RTK, motion capture system, ground station, offboard processing, or communication with the vehicle, and it requires only the minimal set of hardware and localization sensors. The videos and source codes are also provided.

Real-Time Ellipse Detection for Robotics Applications

We propose a new algorithm for real-time detection and tracking of elliptic patterns suitable for real-world robotics applications. The method fits ellipses to each contour in the image frame and rejects ellipses that do not yield a good fit. The resulting detection and tracking method is lightweight enough to be used on robots' resource-limited onboard computers, can deal with lighting variations and detect the pattern even when the view is partial. The method is tested on an example application of an autonomous UAV landing on a fast-moving vehicle to show its performance indoors, outdoors, and in simulation on a real-world robotics task. The comparison with other well-known ellipse detection methods shows that our proposed algorithm outperforms other methods with the F1 score of 0.981 on a dataset with over 1500 frames. The videos of experiments, the source codes, and the collected dataset are provided with the paper at https://theairlab.org/landing-on-vehicle .

Ground- and ship-based guidance approaches for autonomous landing of UAV

Ground- and ship-based guidance approaches for autonomous landing of UAV

Vision Positioning method for Autonomous Precise Landing of UAV Based on Square Landing Mark

Rotor-craft is a kind of VTOL UAV and is widely used in multiple fields. Among relative researches, vision guided autonomous landing of rotor-craft has been a hot spot, where vision positioning is the most crucial. The core of the algorithm is to calculate the position and attitude information according to the change of the visual image of the same object at different time or frame. Based on the real-time self calibration technology of airborne monocular vision, this paper puts forward a method that takes the designed landing mark composed of black and white squares as the cooperative target, solving the relative pose of camera and cooperative target to carry out the landing positioning of UAV, and realizes the full-automatic sub-pixel precision linear edge detection to ensure the accuracy of visual positioning. A series of simulation experiments show that for 768 * 576 pixel image, when the camera is about 12m away from the target and the noise deviation reaches 3 pixels, the total time of edge extraction and calibration is 0.511s, and the position and attitude estimation are still satisfactory, which shows that the proposed algorithm can effectively realize the autonomous landing of UAV.

Detection and localization of helipad in autonomous UAV landing: a coupled visual-inertial approach with artificial intelligence

Autonomous landing of rotary wing type unmanned aerial vehicles is a challenging problem and key to autonomous aerial fleet operation. We propose a method for localizing the UAV around the helipad, that is to estimate the relative position of the helipad with respect to the UAV. This data is highly desirable to design controllers that have robust and consistent control characteristics and can find applications in search – rescue operations. AI-based neural network is set up for helipad detection, followed by optimization by the localization algorithm. The performance of this approach is compared against fiducial marker approach, demonstrating good consensus between two estimations

Detection and localization of helipad in autonomous UAV landing: a coupled visual-inertial approach with artificial intelligence

Autonomous landing of rotary wing type unmanned aerial vehicles is a challenging problem and key to autonomous aerial fleet operation. We propose a method for localizing the UAV around the helipad, that is to estimate the relative position of the helipad with respect to the UAV. This data is highly desirable to design controllers that have robust and consistent control characteristics and can find applications in search – rescue operations. AI-based neural network is set up for helipad detection, followed by optimization by the localization algorithm. The performance of this approach is compared against fiducial marker approach, demonstrating good consensus between two estimations

A software platform for vision-based UAV autonomous landing guidance based on markers estimation

The paper concentrated on descripting the methods of UAV autonomous landing on moving target. GPS navigation and vision-based navigation were employed during different stage of autonomous landing in the simulation environment and virtual reality. Uncertain markers estimation is the main step for UAV autonomous landing. It contains the convex hull transformation, interference preclusion, ellipse fitting and specific feature matching. Furthermore, the complete visual measurement program and guidance strategy were proposed in this paper. Considerable comprehensive experiments indicated the significance and feasibility of method of vision-based UAV autonomous landing on moving target.

Design for Guidance Method of UAV Autonomous Landing on Mobile Platform Based on Prediction of Intersection Points

The development of the technology for UVAs (Unmanned Aerial Vehicle) autonomous landing on mobile platform is introduced in this article. And the existing problems of UAVs autonomous landing on mobile platforms are also analysed. In order to solve these problems, a guidance method of UAV autonomous landing on mobile platform based on prediction of intersection points is proposed in this paper. In this method, the intersection point of the trajectory of UAV and the trajectory of the mobile platform is predicted based on the speed plan for UAV and the motion state of mobile platform. Then, the UAV will be guided to the intersection point by proportional guidance law. And at each time of the landing of the UAV, the location of the intersection point will be corrected in real time. In order to verify this method, a mathematical simulation experiment was designed. The result of this experiment shows that UAVs are able to land more accurately on the mobile platform with less overload using this method.

Errors of UAV Autonomous Landing System for Different Radio Beacon Configurations

Errors of UAV Autonomous Landing System for Different Radio Beacon Configurations

A Deep Reinforcement Learning Strategy for UAV Autonomous Landing on a Moving Platform

The use of multi-rotor UAVs in industrial and civil applications has been extensively encouraged by the rapid innovation in all the technologies involved. In particular, deep learning techniques for motion control have recently taken a major qualitative step, since the successful application of Deep Q-Learning to the continuous action domain in Atari-like games. Based on these ideas, Deep Deterministic Policy Gradients (DDPG) algorithm was able to provide outstanding results with continuous state and action domains, which are a requirement in most of the robotics-related tasks. In this context, the research community is lacking the integration of realistic simulation systems with the reinforcement learning paradigm, enabling the application of deep reinforcement learning algorithms to the robotics field. In this paper, a versatile Gazebo-based reinforcement learning framework has been designed and validated with a continuous UAV landing task. The UAV landing maneuver on a moving platform has been solved by means of the novel DDPG algorithm, which has been integrated in our reinforcement learning framework. Several experiments have been performed in a wide variety of conditions for both simulated and real flights, demonstrating the generality of the approach. As an indirect result, a powerful work flow for robotics has been validated, where robots can learn in simulation and perform properly in real operation environments. To the best of the authors knowledge, this is the first work that addresses the continuous UAV landing maneuver on a moving platform by means of a state-of-the-art deep reinforcement learning algorithm, trained in simulation and tested in real flights.

LightDenseYOLO: A Fast and Accurate Marker Tracker for Autonomous UAV Landing by Visible Light Camera Sensor on Drone.

Autonomous landing of an unmanned aerial vehicle or a drone is a challenging problem for the robotics research community. Previous researchers have attempted to solve this problem by combining multiple sensors such as global positioning system (GPS) receivers, inertial measurement unit, and multiple camera systems. Although these approaches successfully estimate an unmanned aerial vehicle location during landing, many calibration processes are required to achieve good detection accuracy. In addition, cases where drones operate in heterogeneous areas with no GPS signal should be considered. To overcome these problems, we determined how to safely land a drone in a GPS-denied environment using our remote-marker-based tracking algorithm based on a single visible-light-camera sensor. Instead of using hand-crafted features, our algorithm includes a convolutional neural network named lightDenseYOLO to extract trained features from an input image to predict a marker’s location by visible light camera sensor on drone. Experimental results show that our method significantly outperforms state-of-the-art object trackers both using and not using convolutional neural network in terms of both accuracy and processing time.

Supplementary Video: Monocular Vision SLAM-Based UAV Autonomous Landing in Emergencies and Unknown Environments

Supplementary Video: Monocular Vision SLAM-Based UAV Autonomous Landing in Emergencies and Unknown Environments

Monocular Vision SLAM-Based UAV Autonomous Landing in Emergencies and Unknown Environments

With the popularization and wide application of drones in military and civilian fields, the safety of drones must be considered. At present, the failure and drop rates of drones are still much higher than those of manned aircraft. Therefore, it is imperative to improve the research on the safe landing and recovery of drones. However, most drone navigation methods rely on global positioning system (GPS) signals. When GPS signals are missing, these drones cannot land or recover properly. In fact, with the help of optical equipment and image recognition technology, the position and posture of the drone in three dimensions can be obtained, and the environment where the drone is located can be perceived. This paper proposes and implements a monocular vision-based drone autonomous landing system in emergencies and in unstructured environments. In this system, a novel map representation approach is proposed that combines three-dimensional features and a mid-pass filter to remove noise and construct a grid map with different heights. In addition, a region segmentation is presented to detect the edges of different-height grid areas for the sake of improving the speed and accuracy of the subsequent landing area selection. As a visual landing technology, this paper evaluates the proposed algorithm in two tasks: scene reconstruction integrity and landing location security. In these tasks, firstly, a drone scans the scene and acquires key frames in the monocular visual simultaneous localization and mapping (SLAM) system in order to estimate the pose of the drone and to create a three-dimensional point cloud map. Then, the filtered three-dimensional point cloud map is converted into a grid map. The grid map is further divided into different regions to select the appropriate landing zone. Thus, it can carry out autonomous route planning. Finally, when it stops upon the landing field, it will start the descent mode near the landing area. Experiments in multiple sets of real scenes show that the environmental awareness and the landing area selection have high robustness and real-time performance.