Unmanned Aerial Vehicle Navigation Research Articles

Inter-Object Navigation of Unmanned Aerial Vehicles to Increase the Efficiency and Accuracy of Inspection of Power Lines

The purpose of the work is to improve the accuracy and efficiency of a power line section inspection for a fault detection using unmanned aerial vehicles. The goal was achieved by using a unified computing and measurement platform on multicopter and aircraft drones and by simplifying the interaction between them and by using the inter-object navigation sensors. The most significant results were the development of a method of route planning by drones over different parts of the power grid and a method of inter-object navigation. The drone route planning problem was represented by a multiagent variation of the classical traveling salesman problem and was solved by the ant colony method. The method of inter-object navigation was distinguished by the representation of the power grid topology by high and low intensity graphs, involving a different number and types of drones in the inspection process. The application of the developed methods made it possible to increase the accuracy of power line inspections by 27-73%, and the efficiency by 2-8 times. Solving the problem of multicriteria optimization of the drone team flight route planning made it possible to reduce the cost of monitoring critical infrastructure facilities while improving its efficiency and accuracy. Thus, the conducted research has shown the effectiveness of the proposed approach for the monitoring of power facilities, route selection, number and composition of search teams. The direction for further research is to improve the ant algorithm.

Vision-Based Navigation Techniques for Unmanned Aerial Vehicles: Review and Challenges

In recent years, unmanned aerial vehicles (UAVs), commonly known as drones, have gained increasing interest in both academia and industries. The evolution of UAV technologies, such as artificial intelligence, component miniaturization, and computer vision, has decreased their cost and increased availability for diverse applications and services. Remarkably, the integration of computer vision with UAVs provides cutting-edge technology for visual navigation, localization, and obstacle avoidance, making them capable of autonomous operations. However, their limited capacity for autonomous navigation makes them unsuitable for global positioning system (GPS)-blind environments. Recently, vision-based approaches that use cheaper and more flexible visual sensors have shown considerable advantages in UAV navigation owing to the rapid development of computer vision. Visual localization and mapping, obstacle avoidance, and path planning are essential components of visual navigation. The goal of this study was to provide a comprehensive review of vision-based UAV navigation techniques. Existing techniques have been categorized and extensively reviewed with regard to their capabilities and characteristics. Then, they are qualitatively compared in terms of various aspects. We have also discussed open issues and research challenges in the design and implementation of vision-based navigation techniques for UAVs.

ANALYSIS OF VARIOUS TECHNIQUES FOR ENSURING AUTONOMOUS NAVIGATION OF UNMANNED AERIAL VEHICLES

Unmanned Aerial Vehicles (UAVs) have many advantages compared to other vehicle systems. UAVs are faster, cheaper, and more flexible. However, like many other systems UAVs also need navigation. But, it’s not safe to use only one navigation system for various reasons. The recent increase in the number of cyberattacks is one of these reasons. Failure of the navigation system can cause the UAV to lose control. This, in turn, can lead to serious accidents. Therefore, this work analyzes various techniques to ensure the autonomous navigation of UAVs. Also, the advantages and disadvantages of each technique are discussed. Finally, the implementation of these techniques with Kalman filters (KF), deep learning, and machine learning is demonstrated and the results of various studies on this subject are also highlighted.

Analysis of GPS/EGNOS Positioning Quality Using Different Ionospheric Models in UAV Navigation.

Unmanned aerial vehicles (UAVs) have become very popular tools for geoinformation acquisition in recent years. They have also been applied in many other areas of life. Their navigation is highly dependent on global navigation satellite systems (GNSS). The European Geostationary Navigation Overlay Service (EGNOS) is intended to support GNSSs during positioning, mainly for aeronautical applications. The research presented in this paper concerns the analysis of the positioning quality of a modified GPS/EGNOS algorithm. The calculations focus on the source of ionospheric delay data as well as on the aspect of smoothing code observations with phase measurements. The modifications to the algorithm concerned the application of different ionospheric models for position calculation. Consideration was given to the EGNOS ionospheric model, the Klobuchar model applied to the GPS system, the Klobuchar model applied to the BeiDou system, and the NeQuick model applied to the Galileo system. The effect of removing ionospherical corrections from GPS/EGNOS positioning on the results of the determination of positioning quality was also analysed. The results showed that the original EGNOS ionospheric model maintains the best accuracy results and a better correlation between horizontal and vertical results than the other models examined. The additional use of phase-smoothing of code observations resulted in maximum horizontal errors of approximately 1.3 m and vertical errors of approximately 2.2 m. It should be noted that the results obtained have local characteristics related to the area of north-eastern Poland.

Retracted: Cable tunnel unmanned aerial vehicle location and navigation method based on ultra wideband and depth camera fusion

AbstractWith the increasing demand for electric energy, more and more cities are constructing cable tunnels. In order to prevent the cable in the tunnel from being damaged, regular inspection of the cable tunnel is very important. However, due to the large scale of cable tunnel construction, the traditional manual inspection is difficult to meet the maintenance requirements. With the development of intelligent technology, the intelligent patrol mode of UAV has gradually replaced the traditional manual patrol mode and become the main patrol mode of cable tunnel. Therefore, the positioning and navigation method of cable tunnel UAV is studied in this paper. By using UWB localization algorithm and depth camera fusion, the positioning and navigation methods of UAVs are determined and compared with the positioning algorithm based on optical flow. Experimental comparison results show that the maximum and minimum yaw angles of UAV positioning based on the proposed algorithm are 2.41° and 0.16°, while the maximum and minimum yaw angles of UAV positioning based on the comparison algorithm are 3.11° and 1.51°. It can be seen that the integration of UWB and depth camera is of great significance for the research of positioning and navigation methods of cable tunnel UAVs.

Multiple UAVs Path Planning Based on Deep Reinforcement Learning in Communication Denial Environment

In this paper, we propose a C51-Duel-IP (C51 Dueling DQN with Independent Policy) dynamic destination path-planning algorithm to solve the problem of autonomous navigation and avoidance of multiple Unmanned Aerial Vehicles (UAVs) in the communication denial environment. Our proposed algorithm expresses the Q function output by the Dueling network as a Q distribution, which improves the fitting ability of the Q value. We also extend the single-step temporal differential (TD) to the N-step timing differential, which solves the problem of inflexible updates of the single-step temporal differential. More importantly, we use an independent policy to achieve autonomous avoidance and navigation of multiple UAVs without any communication with each other. In the case of communication rejection, the independent policy can achieve the consistency of multiple UAVs and avoid the greedy behavior of UAVs. In multiple-UAV dynamic destination scenarios, our work includes path planning, taking off from different initial positions, and dynamic path planning, taking off from the same initial position. The hardware-in-the-loop (HITL) experiment results show that our C51-Duel-IP algorithm is much more robust and effective than the original Dueling-IP and DQN-IP algorithms in an urban simulation environment. Our independent policy algorithm has similar effects as the shared policy but with the significant advantage of running in a communication denial environment.

Correction: Martinović et al. Mathematical Considerations for Unmanned Aerial Vehicle Navigation in the Magnetic Field of Two Parallel Transmission Lines. Appl. Sci. 2021, 11, 3323

The authors wish to make the following changes to their paper [...]

UAV's Status Is Worth Considering: A Fusion Representations Matching Method for Geo-Localization.

Visual geo-localization plays a crucial role in positioning and navigation for unmanned aerial vehicles, whose goal is to match the same geographic target from different views. This is a challenging task due to the drastic variations in different viewpoints and appearances. Previous methods have been focused on mining features inside the images. However, they underestimated the influence of external elements and the interaction of various representations. Inspired by multimodal and bilinear pooling, we proposed a pioneering feature fusion network (MBF) to address these inherent differences between drone and satellite views. We observe that UAV's status, such as flight height, leads to changes in the size of image field of view. In addition, local parts of the target scene act a role of importance in extracting discriminative features. Therefore, we present two approaches to exploit those priors. The first module is to add status information to network by transforming them into word embeddings. Note that they concatenate with image embeddings in Transformer block to learn status-aware features. Then, global and local part feature maps from the same viewpoint are correlated and reinforced by hierarchical bilinear pooling (HBP) to improve the robustness of feature representation. By the above approaches, we achieve more discriminative deep representations facilitating the geo-localization more effectively. Our experiments on existing benchmark datasets show significant performance boosting, reaching the new state-of-the-art result. Remarkably, the recall@1 accuracy achieves 89.05% in drone localization task and 93.15% in drone navigation task in University-1652, and shows strong robustness at different flight heights in the SUES-200 dataset.

UAV NAVIGATION AND MANAGEMENT SYSTEM BASED ON THE SPECTRAL PORTRAIT OF TERRAIN

This paper focuses on investigating the navigation of unmanned aerial vehicles (UAVs) using spatial-spectral representations of the terrain. This type of navigation is valuable in situations where GPS receivers and other navigation devices fail, but its implementation requires resolving several methodological challenges. One crucial aspect to consider is the impact of changes in illumination on the spectral characteristics of objects. The adoption of satellite-based solutions relying on optical patterns is not suitable for low-flying UAVs, and instead, utilizing service data from the spectral sensors on the UAV's meter display holds greater promise. This study examines an existing method that addresses illumination changes by utilizing the LightValue parameter for different cameras, both in laboratory and field conditions. Through experimentation, it has been established that the relationship between LightValue and the intensities of color components varies individually across different cameras. To correct for natural light variations, it is proposed to employ experimentally derived relationships specific to the sensory equipment brands. When designing navigation systems based on spectral representations of the terrain, it is advisable to select objects that exhibit the most consistent optical changes with respect to illumination.

РАЗРАБОТКА АЛГОРИТМА НАВИГАЦИИ И ФОРМИРОВАНИЕ БЕЗОПАСНЫХ ТРАЕКТОРИЙ АВТОНОМНОГО ПОЛЕТА БЕСПИЛОТНОГО ЛЕТАТЕЛЬНОГО АППАРАТА В УСЛОВИЯХ МНОГОЧИСЛЕННЫХ ПОМЕХ И ПРЕПЯТСТВИЙ

Relevance The problem of navigation of unmanned aerial vehicles (UAV) in monitoring forest ecosystems is considered. The relevance of this study lies in the development of navigation algorithms and the formation of safe trajectories for autonomous flight of UAVs in the face of numerous interference and obstacles. Aim of research Analysis of modern means of forest ecosystem monitoring. The objectives of the research project are: 1. When solving the navigation problem, it is proposed to use «machine vision» technologies. These technologies make it possible to find and localize dangerous scene objects on the current image in order to generate signals for controlling the movement of the UAV along safe trajectories. 2. Development of an algorithm for segmenting the image of the environment based on a statistical analysis of the histograms of the brightness of image areas. This algorithm makes it possible to perform airspace zoning according to the hazard level using the introduced set of textural features. 3. Evaluation of the efficiency of the forest image segmentation algorithm based on the calculation of statistical texture characteristics. An algorithm for forming the trajectory of low-flying UAVs to avoid danger is presented. Research method Determination of texture features based on statistical analysis of histograms of brightness of image areas. Results The algorithm for forming the trajectory of low-flying UAVs in conditions of numerous interference and obstacles makes it possible to evaluate potentially dangerous sections of the flight trajectory in order to develop recommendations for adjusting coordinates and movement parameters that ensure the required level of safety, including automatic maneuvering to avoid danger. During this research project, a navigation algorithm was developed, as a result of which the modeling results presented in the work confirm the high efficiency of the proposed algorithm for segmenting images of forest areas obtained from cameras or laser-location methods in order to form the trajectory of low-flying UAVs in tasks of monitoring forest ecosystems.

A REVIEW OF VISUAL ODOMETRY FOR UAV AUTONOMOUS NAVIGATION

Visual odometry (VO) has become a popular technique for autonomous naviga-tion of unmanned aerial vehicles (UAVs) in GPS-denied environments. Although, the concept of VO is not new, it has been successfully implemented for on-ground vehi-cles only. There are several product level solutions of autonomous navigation, which rely on VO or contain VO as an important subpart. Meanwhile, a proper system of VO for UAVs is not yet achieved and continues to be a subject of an active research. In this paper, we show the comparison of the latest VO techniques for UAV nav-igation. We begin by describing the basic principles of VO, then review the different types of VO algorithms commonly used in UAV navigation, such as monocular, stereo, and RGB-D approaches. For each type of algorithm, we discuss the advantages and limitations, and provide examples of recent research and applications. Finally, we dis-cuss some of the emerging trends and future directions in visual odometry for UAV navigation, such as Visual inertial odometry approaches and real-time implementa-tions. This review paper provides a comprehensive overview of the current state-of-the-art in visual odometry for UAV autonomous navigation, highlighting the ad-vantages and limitations of different types of algorithms and identifying key challeng-es and future research directions.

DEVELOPMENT A HARDWARE AND SOFTWARE SYSTEM TO DETERMINE THE COORDINATES OF AUTONOMOUS VEHICLES BASED ON IMAGE PROCESSING

Modern image processing techniques play an important role in solving this problem. One of the elements for control and navigation of unmanned aerial vehicles is trajectory estimation. This is seen as an important task for identifying the location of the device and objects in the environment depending on the distance between them, as well as with the help of modern consumer devices that allow image recording. Systems that provide navigation accompaniment are consists of hardware and software. The complexity of the hardware varies from very simple options. Typically, software is developed and used for a specific hardware platform. This article contains a description of the programming environment in which the modeling and evaluation of the effectiveness of algorithms were carried out, which proposes to improve the accuracy of the control system, based on image processing of the surrounding area.

Differential Framework for Submeter-Accurate Vehicular Navigation With Cellular Signals

A framework that could achieve submeter-level-accurate horizontal navigation with carrier phase differential measurements from cellular signals is developed. This framework, termed CD-cellular, is composed of a base and a rover in a cellular environment, both making carrier phase measurements to the same cellular base transceiver stations (BTSs). The base shares its carrier phase measurements with the mobile rover, which in turn employs an extended Kalman filter to obtain a coarse estimate of its states, followed by a batch weighted nonlinear least squares (B-WNLS) estimator to solve for the integer ambiguities, and finally a point-solution WNLS to estimate its own states. The framework is designed to guarantee that after some time, the rover's position error remains below a pre-defined threshold with a desired probability. This is achieved by leveraging models of the BTS positions from stochastic geometry. Experimental results on an unmanned aerial vehicle (UAV) in an open semi-urban environment with multipath-free, line-of-sight (LOS) conditions are presented, showing that the developed framework achieves a 70.48 cm position root mean-squared error (RMSE) over a trajectory of 2.24 km, measured with respect to the UAV's navigation solution from its onboard GPS-inertial navigation system (INS).

Autonomous Navigation of the UAV through Deep Reinforcement Learning with Sensor Perception Enhancement

The accuracy of autonomous navigation and obstacle avoidance of unmanned aerial vehicles (UAVs) in complex environments has become one challenging task. In this paper, an autonomous navigation and obstacle avoidance of the UAV (ANOAU) algorithm based on deep reinforcement learning (DRL) has been proposed to achieve accurate path planning in complex environments. In our work, we use an actor–critic‐based DRL framework to achieve autonomous UAV control from sensor input to the output of the UAV’s action and design a set of reward functions that can be adapted to autonomous navigation and obstacle avoidance for the UAV in the complex environment. Meanwhile, to alleviate the decision‐making bias caused by the incomplete observables of the UAV, we use a gate recurrent unit network to enhance the ability to perceive the uncertain environment, enhance the perception representation and improve the accuracy of UAV real‐time decision‐making. Experimental simulation results verify that the ANOAU algorithm achieves good UAV flight attitude adaptive adjustment in navigation and obstacle avoidance tasks and significantly improves the generalization ability and training efficiency of the UAV navigation controller in a complex environment.

Autonomous navigation for multiple unmanned aerial vehicles (UAVs) in urban environments

This paper presents an autonomous navigation system for multiple unmanned aerial vehicles (UAVs) in urban environments. The navigation system comprises a long-term route planner and a short-term cooperative collision avoidance function. A 3D model of a real urban terrain is used to represent the environment within which the UAVs operate, and the system takes the motion constraints of the UAVs into account when performing planning. The navigation system enables multiple independent UAVs to travel from their departure points to their destinations while predicting and avoiding collisions with one another and with static terrain. Monte Carlo simulations on the system indicate a worst-case route planner replan rate of 11.65 %, route planner failure rate of 9.82×10-4 %, and collision resolution failure rate of 0.12 % for the collision avoidance function.

Coregistration of Hyperspectral Imagery With Photogrammetry for Shallow-Water Mapping

In this paper we present coregistration of hyperspectral imagery with photogrammetry for shallow-water mapping from an Unmanned Aerial Vehicle (UAV). The coregistration is based on a methodology for georeferencing that utilizes outputs from the photogrammetry pipeline through three steps. First, we perform the photogrammetry pipeline. This gives camera poses from Structure-from-Motion (SfM) and a dense point cloud from Multi-View Stereo (MVS). Performing a refraction correction of the dense point cloud yields high-resolution bathymetry. Second, poses from SfM are fused with UAV navigation sensors in a Kalman smoother. Third, the geometric model of the hyperspectral imager is calibrated to align hyperspectral images with photogrammetry. Then, ray tracing is performed to georeference spectral measurements onto the bathymetry from MVS. Using the georeferenced hyperspectral imagery, we present spectral bathymetry estimation. The methods were demonstrated for a coastal site (depth < 6 m) in Norway, using a UAV with a camera and a hyperspectral imager. The georeferencing yielded a horizontal Mean Absolute Error (MAE) of 22 cm between hyperspectral and photogrammetry, equivalent to one hyperspectral pixel. The MVS bathymetry gave a MAE of 14 cm with respect to ground truth acoustic. The spectral bathymetry estimator was calibrated on ground truth acoustics with a MAE of 10 cm. Comparing the bathymetry from MVS with the spectral bathymetry yielded a MAE of 11 cm. The results suggest that coregistration with photogrammetry yields accurate georeferencing of hyperspectral imagery. The results also show that we can map bathymetry accurately using MVS with refraction correction or the spectral estimator.

System Engineering-Based Conceptual Design of Indoor Navigation System of Unmanned Aerial Vehicles

Unmanned aerial vehicles (UAVs) have been become important day by day because of advantages in our life use cases; specially, they can play important role in the industry 4.0 concept such as automation in production plants. System engineering approach is planning the design and production phases. Every system, subsystem, component, or mission of the systems are investigated before project starts and each requirement is set. The investigation of compatibility between systems and products minimizes the risks during the production phases. This paper proposes the usage of system engineering approach to the conceptual design of an UAV navigation system which aims to be used in indoor environments. In order to provide autonomous indoor flight, system requirements are set by using system engineering approach. Then step by step, the basic functions that UAV needs to complete operation are specified. The required products are listed as a tree and the relation between products and functions are investigated. Thus, the conceptual design of the UAV indoor navigation system is demonstrated by using of the functional tree, the functions and product relation matrix, the product tree, and the use cases. Moreover, the system operational requirements are investigated, and the requirement operation phases, modes and use cases are also determined. Thanks to system engineering, all these complex systems are put together systematically, and the conceptual design is achieved.

Resilient tightly coupled INS/UWB integration method for indoor UAV navigation under challenging scenarios

Based on the high positioning accuracy, low cost and low-power consumption, the ultra-wide-band (UWB) is an ideal solution for indoor unmanned aerial vehicle (UAV) localization and navigation. However, the UWB signals are easy to be blocked or reflected by obstacles such as walls and furniture. A resilient tightly-coupled inertial navigation system (INS)/UWB integration is proposed and implemented for indoor UAV navigation in this paper. A factor graph optimization (FGO) method enhanced by resilient stochastic model is established to cope with the indoor challenging scenarios. To deal with the impact of UWB non-line-of-sight (NLOS) signals and noise uncertainty, the conventional neural net-works (CNNs) are introduced into the stochastic modelling to improve the resilience and reliability of the integration. Based on the status that the UWB features are limited, a ‘two-phase’ CNNs structure was designed and implemented: one for signal classification and the other one for measurement noise prediction. The proposed resilient FGO method is tested on flighting UAV platform under actual indoor challenging scenario. Compared to classical FGO method, the overall positioning errors can be decreased from about 0.60 m to centimeter-level under signal block and reflection scenarios. The superiority of resilient FGO which effectively verified in constrained environment is pretty important for positioning accuracy and integrity for indoor navigation task.

A GNSS/INS/LiDAR Integration Scheme for UAV-Based Navigation in GNSS-Challenging Environments.

Unmanned aerial vehicle (UAV) navigation has recently been the focus of many studies. The most challenging aspect of UAV navigation is maintaining accurate and reliable pose estimation. In outdoor environments, global navigation satellite systems (GNSS) are typically used for UAV localization. However, relying solely on GNSS might pose safety risks in the event of receiver malfunction or antenna installation error. In this research, an unmanned aerial system (UAS) employing the Applanix APX15 GNSS/IMU board, a Velodyne Puck LiDAR sensor, and a Sony a7R II high-resolution camera was used to collect data for the purpose of developing a multi-sensor integration system. Unfortunately, due to a malfunctioning GNSS antenna, there were numerous prolonged GNSS signal outages. As a result, the GNSS/INS processing failed after obtaining an error that exceeded 25 km. To resolve this issue and to recover the precise trajectory of the UAV, a GNSS/INS/LiDAR integrated navigation system was developed. The LiDAR data were first processed using the optimized LOAM SLAM algorithm, which yielded the position and orientation estimates. Pix4D Mapper software was then used to process the camera images in the presence of ground control points (GCPs), which resulted in the precise camera positions and orientations that served as ground truth. All sensor data were timestamped by GPS, and all datasets were sampled at 10 Hz to match those of the LiDAR scans. Two case studies were considered, namely complete GNSS outage and assistance from GNSS PPP solution. In comparison to the complete GNSS outage, the results for the second case study were significantly improved. The improvement is described in terms of RMSE reductions of approximately 51% and 78% for the horizontal and vertical directions, respectively. Additionally, the RMSE of the roll and yaw angles was reduced by 13% and 30%, respectively. However, the RMSE of the pitch angle was increased by about 13%.

Deep-Ensemble-Learning-Based GPS Spoofing Detection for Cellular-Connected UAVs

Unmanned aerial vehicles (UAVs) are an emerging technology in the 5G-and-beyond systems with the promise of assisting cellular communications and supporting IoT deployment in remote and density areas. Safe and secure navigation is essential for UAV remote and autonomous deployment. Indeed, the opensource simulator can use commercial software-defined radio tools to generate fake global positioning system (GPS) signals and spoof the UAV GPS receiver to calculate wrong locations, deviating from the planned trajectory. Fortunately, the existing mobile positioning system can provide additional navigation for cellular-connected UAVs and verify the UAV GPS locations for spoofing detection, but it needs at least three base stations (BSs) at the same time. In this article, we propose a novel deep-ensemble-learning-based, mobile-network-assisted UAV monitoring and tracking system for cellular-connected UAV spoofing detection. The proposed method uses path losses between BSs and UAVs communication to indicate the UAV trajectory deviation caused by GPS spoofing. To increase the detection accuracy, three statistics methods are adopted to remove environmental impacts on path losses. In addition, deep ensemble learning methods are deployed on the edge cloud servers and use the multilayer perceptron (MLP) neural networks to analyze path losses statistical features for making a final decision, which has no additional requirements and energy consumption on UAVs. The experimental results show the effectiveness of our method in detecting GPS spoofing, achieving above 97% accuracy rate under two BSs, while it can still achieve at least 83% accuracy under only one BS.