Method For Unmanned Aerial Vehicles Research Articles

Radar-optical fusion detection of UAV based on improved YOLOv7-tiny

This study presents a radar-optical fusion detection method for unmanned aerial vehicles (UAVs) in maritime environments. Radar and camera technologies are integrated to improve the detection capabilities of the platforms. The proposed method involves generating regions of interest (ROI) by projecting radar traces onto optical images through matrix transformation and geometric centroid registration. The generated ROI are matched with YOLO detection boxes using the intersection-over-union (IoU) algorithm, enabling radar-optical fusion detection. A modified algorithm, called SPN-YOLOv7-tiny, is developed to address the challenge of detecting small UAV targets that are easily missed in images. In this algorithm, the convolutional layers in the backbone network are replaced with a space-to-depth convolution, and a small object detection layer is added. In addition, the loss function was replaced with a normalized weighted distance loss function. Experimental results demonstrate that compared to the original YOLOv7-tiny method, SPN-YOLOv7-tiny achieves an improved mAP@0.5 (mean average precision at an IoU threshold of 0.5) from 0.852 to 0.93, while maintaining a high frame rate of 135.1 frames per second. Moreover, the proposed radar-optical fusion detection method achieves an accuracy of 96.98%, surpassing the individual detection results of the radar and camera. The proposed method effectively addresses the detection challenges posed by closely spaced overlapping targets on a radar chart.

Relative positioning method for unmanned aerial vehicles in complex electromagnetic environment

In view of the problem that satellite navigation and positioning systems may fail or be unreliable in complex electromagnetic environments, which may lead to divergence of UAV positioning, the inertial navigation systems working for a long time may lead to increasing errors. This paper adopts a relative navigation algorithm that combines airborne inertial navigation systems and inter-aircraft relative information measurement. The algorithm requires each UAV to load relative navigation equipment and inertial navigation systems and acquire and fuse relative navigation information through inter-aircraft communication. In this relative navigation algorithm, the host and wing plane can transmit their respective navigation information and relative navigation information to each other, and fuse information from different sources through a Kalman filtering algorithm to enhance the accuracy of relative positioning data. With the help of corrected and feedback relative positioning data, the relative location of the host and wingman is achieved. The actual simulation results show that the relative position RMSE of the host, Wing Plane 1, and Wing Plane 2 in the X, Y, and Z dimensions are 1.345 m, 0.7388 m, 1.645 m, 2.167 m, 1.201 m, and 2.069 m, respectively. It is apparent that this scheme improves the relative navigation performance and robustness of the UAV group.

A path planning method for unmanned aerial vehicle based on improved wolf pack algorithm

SummaryIn the rapidly developing field of unmanned aerial vehicle (UVA) technology, solving local optimal problems and achieving efficient smooth planning are crucial for improving the operational efficiency and safety of UVA systems. To address these needs, our study introduces a novel optimization algorithm, called IWPA‐APF, which aims to improve path planning efficiency. This algorithm is a fusion of the artificial potential field (APF) method and the improved wolf‐pack algorithm (IWPA) to solve common problems such as local optima and inefficient planning in the path planning process. The IWPA‐APF algorithm improves search efficiency and accuracy by incorporating an adaptive step size that significantly refines the key behaviors of the traditional IWPA, which is based on the wolf pack algorithm (WPA). In addition, the algorithm incorporates specific planning constraints, such as turn angles and tolerance limits, to minimize getting stuck in local optima. The process involves generating initial plans through multiple iterations of the IWPA, followed by further refinement using the method of integrating APF's repulsive force field. Simulation results show that the IWPA‐APF algorithm outperforms traditional methods, offering shorter flight distances and improved safety, thereby establishing itself as a robust solution for UAV path planning in obstacle‐rich environments.

Optimal camera focal length detection method for GPS-supported bundle adjustment in UAV photogrammetry

The global positioning system (GPS) supported bundle adjustment (BA) method of unmanned aerial vehicle (UAV) photogrammetry significantly minimises the number of ground control points (GCPs); however, there are often systematic errors in the vertical axis direction of the 3D reconstruction results. In this study, a method that relies only on a small number of GCPs for detecting and estimating the optimal focal length of the camera is proposed. First, the scene structure information is obtained based on the conventional GPS-supported BA optimisation method, and the camera focal length is redefined based on a single GCP. Second, a second-order polynomial is used to fit the redefined camera focal lengths of multiple GCPs and the corresponding positioning accuracy. The highest accuracy position is selected as the optimal focal length of the camera. Finally, the optimal focal length of the camera is taken as a constant, and the conventional GPS-supported BA method is used to complete the optimisation again. The experimental results show that under the same number and distribution of GCPs, the camera detection method’s total positioning accuracy proposed in this paper is improved by an average of 37.64%. The optimal camera focal length obtained by the method can correct the systematic error in the vertical direction while keeping the vertical accuracy consistent with the horizontal accuracy.

A Supervised Reinforcement Learning Algorithm for Controlling Drone Hovering

The application of drones carrying different devices for aerial hovering operations is becoming increasingly widespread, but currently there is very little research relying on reinforcement learning methods for hovering control, and it has not been implemented on physical machines. Drone’s behavior space regarding hover control is continuous and large-scale, making it difficult for basic algorithms and value-based reinforcement learning (RL) algorithms to have good results. In response to this issue, this article applies a watcher-actor-critic (WAC) algorithm to the drone’s hover control, which can quickly lock the exploration direction and achieve high robustness of the drone’s hover control while improving learning efficiency and reducing learning costs. This article first utilizes the actor-critic algorithm based on behavioral value Q (QAC) and the deep deterministic policy gradient algorithm (DDPG) for drone hover control learning. Subsequently, an actor-critic algorithm with an added watcher is proposed, in which the watcher uses a PID controller with parameters provided by a neural network as the dynamic monitor, transforming the learning process into supervised learning. Finally, this article uses a classic reinforcement learning environment library, Gym, and a current mainstream reinforcement learning framework, PARL, for simulation, and deploys the algorithm to a practical environment. A multi-sensor fusion strategy-based autonomous localization method for unmanned aerial vehicles is used for practical exercises. The simulation and experimental results show that the training episodes of WAC are reduced by 20% compared to the DDPG and 55% compared to the QAC, and the proposed algorithm has a higher learning efficiency, faster convergence speed, and smoother hovering effect compared to the QAC and DDPG.

Path planning method for unmanned aerial vehicles

Unmanned aerial vehicles, commonly known as drones, have seen tremendous growth and wide-spread use in the last ten years, largely because of unheard-of technological improvements. It is currently one of the most significant study subjects since it involves many different areas of robotics and control systems. This study aims to thoroughly assess the most popular approaches for unmanned aerial vehicle path planning, while also outlining their benefits, drawbacks, potential uses, and overall effectiveness. The objective is to present the academic community with a summary of the existing environment and the development direction of future unmanned aerial vehicle path planning algorithms to encourage more research for quickly evolving planning of intelligent systems.

A Lightweight Reinforcement Learning-Based Real-Time Path Planning Method for Unmanned Aerial Vehicles

A Lightweight Reinforcement Learning-Based Real-Time Path Planning Method for Unmanned Aerial Vehicles

An Autonomous Tracking and Landing Method for Unmanned Aerial Vehicles Based on Visual Navigation

In this paper, we examine potential methods for autonomously tracking and landing multi-rotor unmanned aerial vehicles (UAVs), a complex yet essential problem. Autonomous tracking and landing control technology utilizes visual navigation, relying solely on vision and landmarks to track targets and achieve autonomous landing. This technology improves the UAV’s environment perception and autonomous flight capabilities in GPS-free scenarios. In particular, we are researching tracking and landing as a cohesive unit, devising a switching plan for various UAV tracking and landing modes, and creating a flight controller that has an inner and outer loop structure based on relative position estimation. The inner and outer nested markers aid in the autonomous tracking and landing of UAVs. Optimal parameters are determined via optimized experiments on the measurements of the inner and outer markers. An indoor experimental platform for tracking and landing UAVs was established. Tracking performance was verified by tracking three trajectories of an unmanned ground vehicle (UGV) at varying speeds, and landing accuracy was confirmed through static and dynamic landing experiments. The experimental results show that the proposed scheme has good dynamic tracking and landing performance.

Research on the thermal performance of the condenser in a loop heat pipe anti-icing system

Loop Heat Pipes (LHPs) are efficient heat transfer devices, providing a highly efficient and energy-saving thermal control integrated management method for Unmanned Aerial Vehicles (UAVs) with their flexible structural design. In this paper, a conceptual design of LHP anti-icing system was proposed, using LHP to transfer waste heat to wing’s leading edge. The outer surface temperature of wing skin is one of the main indicators to measure the anti-icing effect. As a result, this paper mainly focused on the heat transfer process of the condenser section by experimental and numerical simulation methods. A stainless steel-nickel LHP was fabricated and tested at different conditions. Volume of Fluid (VOF) method and Lee model were adopted to simulate the condensation process. Results showed that increasing the heating power (from 35 W to 60 W) slowed down the condensation and lengthened the two-phase zone. However, when the angle of attack changes within the range of 0°–10°, the liquid-vapor distribution in the tube remains almost unchanged. The average error of surface temperature between experiment and simulation results is 3.6% and 2.5% for the heating power of 180 W and 60 W respectively. Additionally, because the volume of water droplets collected is the largest at the leading edge of the wing, we recommend arranging the LHP condenser tube inlet near the leading edge of the wing and increasing the density of pipe arrangement to achieve better anti-icing effect.

Improved path planning method for unmanned aerial vehicles based on artificial potential field

With the advancement of unmanned aerial vehicle (UAV) technology and its widespread use in many facets of manufacturing and daily life, the need for UAV mission automation is becoming more and more practical. In order to improve the automatic obstacle avoidance and path planning performance of UAVs, this essay proposes an optimized route planning algorithm based on the artificial potential field (APF) method, which have solved the typical issue of the APF. This method chooses to conduct a pre planning trajectory of the UAV based on a rapidly expanding random tree (RRT). The pre-planned path will be split into continuous particles, and then generating intermediate waypoints. A nearby waypoint offers a gravitational force to aid the UAV in escaping the local minimum when it enters it. At the same time, taking the distance from the UAV to the obstacle and the radius of influence of the obstacle itself into consideration, dynamically adjust the gravitational and repulsive coefficients, set up a non-gravitational zone around the obstacle, which is beneficial for the UAV to elude the obstacle. And set up a repulsive limited action zone to reduce unnecessary turns in the trajectory to achieve the effect of path optimization. Considering the difficulty of single UAV missions in most cases, this paper discusses cooperative flight path planning for multiple UAVs into consideration.

Uav intelligent recognition and tracking method based on deep learning

In the era of artificial intelligence, the traditional industry has undergone great changes under the guidance of modern technology theory. Enterprises or institutions in various fields have begun to develop and innovate UAV equipment with the application value of The Times with the help of technologies such as deep learning or machine vision to meet the increasing business processing needs. Since the unmanned aerial vehicle will transmit the captured images to the ground, and the encoding and decoding during transmission will affect the real-time performance of the overall system, scientific researchers have proposed an intelligent recognition and tracking method for unmanned aerial vehicles, in order to improve the work efficiency and quality of the system equipment. On the basis of understanding the current research status of UAV and target recognition technology, this paper mainly studies the UAV target recognition algorithm with deep learning as the core according to the intelligent UAV platform design scheme. The final result shows that the deep learning recognition algorithm can further improve the efficiency of UAV intelligent recognition and tracking.

A MULTI-MODAL INTENTION INTEGRATION METHOD FOR UNMANNED AERIAL VEHICLES BASED ON THE COMBINATION OF BIOELECTRICAL SIGNALS AND CONVENTIONAL HUMAN–COMPUTER INTERACTION

With the rapid development of intelligent information technology, human–computer interaction for unmanned aerial vehicle (UAV) systems has gradually developed from single-mode interaction to multi-modal interaction. Due to the problems of information ambiguity and operational redundancy in multi-modal interaction, and the inability to effectively realize the coupling of human–computer interaction of different modalities, a priority strategy for multi-modal interaction for UAV systems is proposed. Living cells or tissues, whether in a static or active state, will produce regular electrical phenomena called bioelectricity. Human–computer interaction intention recognition technology based on bioelectrical signals can significantly enhance the real-time performance and flexibility of human–computer interaction and has great development potential. The characteristics of human–computer interaction intention recognition methods based on bioelectrical signals, such as electroencephalograms (EEGs), electromyograms (EMGs), electrooculograms (EOGs), and other conventional methods are introduced and analyzed, and then the basic characteristics of single-mode interaction and multi-modal interaction are compared. Furthermore, the priority strategies for UAV systems under different interaction modes are studied based on the analysis of task value evaluation parameters. The cumulative value, urgency, and task complexity are comprehensively analyzed and evaluated, and a set of priority strategies for multi-modal interaction is integrated. Through the simulation and verification of the multi-modal human–computer interaction software of the UAV system, it is determined that the proposed priority strategy conforms to the operation intention of the user, which improves the interaction efficiency and the reliability. In noisy environments, the UAV system control can be combined with the complementary information of various interaction methods to achieve precise control, which has obvious advantages.

End-Cloud Collaboration Navigation Planning Method for Unmanned Aerial Vehicles Used in Small Areas.

Unmanned aerial vehicle (UAV) collaboration has become the main means of indoor and outdoor regional search, railway patrol, and other tasks, and navigation planning is one of the key, albeit difficult, technologies. The purpose of UAV navigation planning is to plan reasonable trajectories for UAVs to avoid obstacles and reach the task area. Essentially, it is a complex optimization problem that requires the use of navigation planning algorithms to search for path-point solutions that meet the requirements under the guide of objective functions and constraints. At present, there are autonomous navigation modes of UAVs relying on airborne sensors and navigation control modes of UAVs relying on ground control stations (GCSs). However, due to the limitation of airborne processor computing power, and background command and control communication delay, a navigation planning method that takes into account accuracy and timeliness is needed. First, the navigation planning architecture of UAVs of end-cloud collaboration was designed. Then, the background cloud navigation planning algorithm of UAVs was designed based on the improved particle swarm optimization (PSO). Next, the navigation control algorithm of the UAV terminals was designed based on the multi-objective hybrid swarm intelligent optimization algorithm. Finally, the computer simulation and actual indoor-environment flight test based on small rotor UAVs were designed and conducted. The results showed that the proposed method is correct and feasible, and can improve the effectiveness and efficiency of navigation planning of UAVs.

YOLO-ViT-Based Method for Unmanned Aerial Vehicle Infrared Vehicle Target Detection

The detection of infrared vehicle targets by UAVs poses significant challenges in the presence of complex ground backgrounds, high target density, and a large proportion of small targets, which result in high false alarm rates. To alleviate these deficiencies, a novel YOLOv7-based, multi-scale target detection method for infrared vehicle targets is proposed, which is termed YOLO-ViT. Firstly, within the YOLOV7-based framework, the lightweight MobileViT network is incorporated as the feature extraction backbone network to fully extract the local and global features of the object and reduce the complexity of the model. Secondly, an innovative C3-PANet neural network structure is delicately designed, which adopts the CARAFE upsampling method to utilize the semantic information in the feature map and improve the model’s recognition accuracy of the target region. In conjunction with the C3 structure, the receptive field will be increased to enhance the network’s accuracy in recognizing small targets and model generalization ability. Finally, the K-means++ clustering method is utilized to optimize the anchor box size, leading to the design of anchor boxes better suited for detecting small infrared targets from UAVs, thereby improving detection efficiency. The present article showcases experimental findings attained through the use of the HIT-UAV public dataset. The results demonstrate that the enhanced YOLO-ViT approach, in comparison to the original method, achieves a reduction in the number of parameters by 49.9% and floating-point operations by 67.9%. Furthermore, the mean average precision (mAP) exhibits an improvement of 0.9% over the existing algorithm, reaching a value of 94.5%, which validates the effectiveness of the method for UAV infrared vehicle target detection.

Autonomous balance control method of unmanned aerial vehicle with manipulator based on artificial intelligence algorithm

Autonomous balance control method of unmanned aerial vehicle with manipulator based on artificial intelligence algorithm

Rotor Noise-Aware Noise Covariance Matrix Estimation for Unmanned Aerial Vehicle Audition

A noise covariance matrix (NCM) estimation method for unmanned aerial vehicle (UAV) audition is proposed with rotor noise reduction as its primary focus. The proposed NCM estimation method could be incorporated into audio processing algorithms using UAV-mounted microphone array systems. The NCM is formed through accurate estimation of the microphone array input signal's amplitude and phase by using a multi-sensory rotor noise power spectral density (PSD) estimator and a filter formed by exploiting the acoustical relationship between the microphone array and the rotor noise sources, respectively. The estimated NCM aims to be readily incorporable into several source enhancement algorithms to reduce the effects of rotor noise and improve the resultant audio quality. Experiment evaluation using real in-flight UAV rotor noise recordings shows that the estimated NCM significantly improves rotor noise reduction (of up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim 28$</tex-math></inline-formula> dB) and the quality of the target sound.

Coverage Path Planning Method of Unmanned Aerial Vehicle for Aircraft Surface Detection Task

Coverage Path Planning Method of Unmanned Aerial Vehicle for Aircraft Surface Detection Task

A Deployment Method for Unmanned Aerial Vehicles Based on Improved Neural Network

The time for drones to complete their missions is limited. When sensors detect targets or perform multiple tasks, the pressure of insufficient resources can be quite high, and even task overload may lead to task failure. This paper focuses on a multi-UAV target assignment method. Compared with the classical model-solving method, which can find the satisfactory solution in a short time. Finally, through MATLAB simulation and verification, we find that the method in this paper has better dynamic adaptability to the complex and changeable environments.

Transformer-based moving target tracking method for Unmanned Aerial Vehicle

Unmanned Aerial Vehicle (UAV) moving target tracking is one of the fundamental implementations in remote sensing and has been widely applied in monitoring, search and rescue, pursuit-escapes, and other fields. Currently, most UAV tracking algorithms merely establish the local relationship between the template and search region without fully using the global context information, leading to problems such as target loss and misclassification, and imprecise bounding boxes. This paper innovatively proposes a UAV tracker, TransUAV, overcoming the above challenge by a feature correlation network based on the self-attention mechanism. The method efficiently combines global features between the search region and the template to reduce the influence of external interference, enhancing the precision and robustness of the tracking algorithm. Moreover, the global spatio-temporal features are acquired by learning query embedding and temporal update strategies to make predictions, enhancing the adaptability to rapid changes in the appearance of target object. There is no proposal or predetermined anchor in this method to satisfy the requirements of onboard operational speed, therefore, no post-processing procedure is required, and the entire approach is end-to-end. The superiority of the proposed TransUAV is verified by an exhaustive evaluation of six challenging target tracking video datasets benchmarks, and the accuracy and robustness of the proposed TransUAV are compared with state-of-the-art methods.

3D trajectory planning based on the Rapidly-exploring Random Tree–Connect and artificial potential fields method for unmanned aerial vehicles

This research proposes a multifaceted approach of three-dimensional trajectory planning based on the combination of Rapidly-exploring Random Tree–Connect algorithm and artificial potential field method to improve the path search ability and dynamic obstacles avoidance capability of unmanned aerial vehicles. Firstly, an improved method of the target gravity is developed by controlling the sampling range to reduce invalid sampling and speed up the convergence speed of the algorithm so as to lessen the restriction of low efficiency and random sampling of the Rapidly-exploring Random Tree–Connect algorithm. Moreover, the regulation factor is introduced into the artificial potential field method to deal with the problem of target unreachable in the trajectory planning. Then the improved Rapidly-exploring Random Tree–Connect algorithm is implemented to plan the global path in a complex environment. This step is carried out via selecting the local target point on the global path found in the global plan, dividing the complex environment into simple environment and utilizing the artificial potential field method to achieve the effect of avoiding unknown dynamic obstacles in the simple environment. Finally, cubic B-spline is employed to smoothing of the planned trajectory. The simulation results demonstrate that the combination of two improved algorithms improves the path search ability and dynamic barrier avoidance capability of the unmanned aerial vehicles.