Fixed-wing Unmanned Aerial Vehicle Landing Research Articles

Automatic Landing Control for Fixed-Wing UAV in Longitudinal Channel Based on Deep Reinforcement Learning

The objective is to address the control problem associated with the landing process of unmanned aerial vehicles (UAVs), with a particular focus on fixed-wing UAVs. The Proportional–Integral–Derivative (PID) controller is a widely used control method, which requires the tuning of its parameters to account for the specific characteristics of the landing environment and the potential for external disturbances. In contrast, neural networks can be modeled to operate under given inputs, allowing for a more precise control strategy. In light of these considerations, a control system based on reinforcement learning is put forth, which is integrated with the conventional PID guidance law to facilitate the autonomous landing of fixed-wing UAVs and the automated tuning of PID parameters through the use of a Deep Q-learning Network (DQN). A traditional PID control system is constructed based on a fixed-wing UAV dynamics model, with the flight state being discretized. The landing problem is transformed into a Markov Decision Process (MDP), and the reward function is designed in accordance with the landing conditions and the UAV’s attitude, respectively. The state vectors are fed into the neural network framework, and the optimized PID parameters are output by the reinforcement learning algorithm. The optimal policy is obtained through the training of the network, which enables the automatic adjustment of parameters and the optimization of the traditional PID control system. Furthermore, the efficacy of the control algorithms in actual scenarios is validated through the simulation of UAV state vector perturbations and ideal gliding curves. The results demonstrate that the controller modified by the DQN network exhibits a markedly superior convergence effect and maneuverability compared to the unmodified traditional controller.

Visual Navigation Algorithm for Night Landing of Fixed-Wing Unmanned Aerial Vehicle

In the recent years, visual navigation has been considered an effective mechanism for achieving an autonomous landing of Unmanned Aerial Vehicles (UAVs). Nevertheless, with the limitations of visual cameras, the effectiveness of visual algorithms is significantly limited by lighting conditions. Therefore, a novel vision-based autonomous landing navigation scheme is proposed for night-time autonomous landing of fixed-wing UAV. Firstly, due to the difficulty of detecting the runway caused by the low-light image, a strategy of visible and infrared image fusion is adopted. The objective functions of the fused and visible image, and the fused and infrared image, are established. Then, the fusion problem is transformed into the optimal situation of the objective function, and the optimal solution is realized by gradient descent schemes to obtain the fused image. Secondly, to improve the performance of detecting the runway from the enhanced image, a runway detection algorithm based on an improved Faster region-based convolutional neural network (Faster R-CNN) is proposed. The runway ground-truth box of the dataset is statistically analyzed, and the size and number of anchors in line with the runway detection background are redesigned based on the analysis results. Finally, a relative attitude and position estimation method for the UAV with respect to the landing runway is proposed. New coordinate reference systems are established, six landing parameters, such as three attitude and three positions, are further calculated by Orthogonal Iteration (OI). Simulation results reveal that the proposed algorithm can achieve 1.85% improvement of AP on runway detection, and the reprojection error of rotation and translation for pose estimation are 0.675∘ and 0.581%, respectively.

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.

Airport Detection for Fixed-Wing Unmanned Aerial Vehicle Landing Using a Hierarchical Architecture

Airport detection plays an important role in vision-based fixed-wing unmanned aerial vehicle landing. A large amount of image analysis is required for vision-based detection. This paper addresses the efficiency and difficulty of airport detection in the initial stage of landing. A hierarchical architecture and a decision criterion are proposed to quickly locate candidate airport regions. Then, the spatial-pyramid matching using sparse coding algorithm is used to obtain a more discriminative feature map representation of airports. A linear support vector machine is employed to recognize potential airports from feature maps. Several experiments are conducted to test the robustness and efficiency of the proposed algorithm. Promising results are obtained under challenging backgrounds and different weather conditions.

Autonomous Landing Control of Fixed-wing UAVs: from Theory to Field Experiment

This paper describes our work on autonomous landing control of fixed-wing unmanned aerial vehicles (UAVs). A hierarchical control structure is developed for autonomous landing of fixed-wing UAVs. In the hierarchical control structure, active disturbance rejection controllers are designed for attitude control. A proportional guidance law is proposed for height tracking. Other controllers, such as flight path angle controller, heading angle controller and taxiing controller, are designed using proportion integration differentiation technique. A test system is developed to validate the hierarchical structure and narrow the gap between the theory and practice. The test system includes an X-Plane based hardware-in-the-loop(HIL) simulation subsystem and a flight test subsystem. The interface and protocol between the autopilot and X-plane are designed same with that of the flight test platform. Consequently, the autopilot passed the validation in the HIL simulation can be directly moved on to the flight test platform. Finally, results of the HIL simulation and field experiment are presented to demonstrate the performance of the proposed approach and the entire test system.

Vision algorithms for fixed-wing unmanned aerial vehicle landing system

Autonomous landing has become a core technology of unmanned aerial vehicle (UAV) guidance, navigation and control system in recent years. As a novel autonomous landing approach, computer vision has been studied and applied in rotary-wing UAV landing successfully. This paper aims to fixed-wing UAV and focus on two problems: how to find runway only depending on airborne front-looking camera and how to align UAV with the designated landing runway. The paper can be divided into two parts to solve above two problems respectively. In the first part, the paper firstly presents an algorithm of region of interest (ROI) detection, which is based on spectral residual saliency map, and then an algorithm of feature vector extraction based on sparse coding and spatial pyramid matching (SPM) is proposed, finally, ROI including designated landing runway is recognized by a linear support vector machine. In the second part, the paper presents an approach of relative position and pose estimation between UAV and landing runway. Estimation algorithm firstly selects five feature points on the runway surface, and then establishes a new earth-fixed reference frame, finally uses orthogonal iteration to estimate landing parameters including three parameters of distance, height and offset, and three pose parameters of roll, yaw, pitch. The experimental results verify the effectiveness of the algorithms proposed in this paper.

Stereo Vision Guiding for the Autonomous Landing of Fixed-Wing UAVs: A Saliency-Inspired Approach

It is an important criterion for unmanned aerial vehicles (UAVs) to land on the runway safely. This paper concentrates on stereo vision localization of a fixed-wing UAV's autonomous landing within global navigation satellite system (GNSS) denied environments. A ground stereo vision guidance system imitating the human visual system (HVS) is presented for the autonomous landing of fixed-wing UAVs. A saliency-inspired algorithm is presented and developed to detect flying UAV targets in captured sequential images. Furthermore, an extended Kalman filter (EKF) based state estimation is employed to reduce localization errors caused by measurement errors of object detection and pan-tilt unit (PTU) attitudes. Finally, stereo-vision-dataset-based experiments are conducted to verify the effectiveness of the proposed visual detection method and error correction algorithm. The compared results between the visual guidance approach and differential GPS-based approach indicate that the stereo vision system and detection method can achieve the better guiding effect.