Navigation System For Unmanned Aerial Vehicles Research Articles

UAVS ENHANCED NAVIGATION IN OUTDOOR GNSS DENIED ENVIRONMENT USING UWB AND MONOCULAR CAMERA SYSTEMS

Abstract. The demand for small Unmanned Aerial Vehicles (UAVs) is massively increasing these days, due to the wide variety of applications utilizing such vehicles to perform tasks that may be dangerous or just to save time, effort, or cost. Small UAVs navigation system mainly depends on the integration between Global Navigation Satellite Systems (GNSS) and Inertial Measurement Unit (INS) to estimate the Positions, Velocities, and Attitudes (PVT) of the vehicle. Without GNSS such UAVs cannot navigate for long periods of time depending on INS alone, as the low-cost INS typically exhibits massive accumulation of errors during GNSS absence. Given the importance of ensuring full operability of the UAVs even during GNSS signals unavailability, other sensors must be used to bound the INS errors and enhance the navigation system performance. This paper proposes an enhanced UAV navigation system based on integration between monocular camera, Ultra-Wideband (UWB) system, and INS. In addition to using variable EKF weighting scheme. The paper also investigates this integration in the case of low density of UWB anchors, to reduce the cost required for such UWB system infrastructure. A GoPro Camera and UWB rover were attached to the belly of a quadcopter, an on the shelf commercial drone (3DR Solo), during the experimental flight. The velocity of the vehicle is estimated with Optical Flow (OF) from camera successive images, while the range measurements between the UWB rover and the stationary UWB anchors, which were distributed on the field, were used to estimate UAV position.

An autonomous navigation system integrated with air data and bionic polarization information

One of the most significant challenges for an unmanned aerial vehicle (UAV) is to autonomously navigate in complex environments, as the signals from the global positioning system (GPS) are subject to disturbance and interference. To improve the autonomy and availability of the UAV navigation system without GPS, we design a new autonomous navigation system and implement it for real applications in this paper, in which one integrates the inertial measurement unit (IMU), the bionic polarization sensor (BPS), and the air data system (ADS). The BPS can provide effective heading angle measurement, and the ADS is used to output information for continuous velocity and height. The combination of BPS and ADS is a solution the inertial error drift. Kalman filter is selected to estimate the error state of the integrated navigation system based on the measurements from the BPS and ADS, and then the estimation is used to correct the navigation system error in real time. The simulation and experimental results have shown that the new integrated navigation system can perform with high precision and autonomy without GPS signal.

User-Configurable Timing and Navigation for UAVs.

As the use of unmanned aerial vehicles (UAVs) for industrial use increases, so are the demands for highly accurate navigation solutions, and with the high dynamics that UAVs offer, the accuracy of a measurement does not only depend on the value of the measurement, but also the accuracy of the associated timestamp. Sensor timing using dedicated hardware is the de-facto method to achieve optimal sensor performance, but the solutions available today have limited flexibility and requires much effort when changing sensors. This article presents requirements and suggestions for a highly accurate, reconfigurable sensor timing system that simplifies integration of sensor systems and navigation systems for UAVs. Both typical avionics sensors, like GNSS receivers and IMUs, and more complex sensors, such as cameras, are supported. To verify the design, an implementation named the SenTiBoard was created, along with a software support package and a baseline sensor-suite. With the solution presented in this paper we get a measurement resolution of 10 nanoseconds and we can transfer up to 7.6 megabytes per second. If the sensor suite includes a GNSS receiver with a pulse-per-second (PPS) reference, the sensor measurements can be related to an absolute time reference (UTC) with a clock drift of 1.9 microseconds per second RMS. An experiment was carried out, using a Mini Cruiser fixed-wing UAV, where errors in georeferencing infrared images were reduced with a factor of 4 when compared to a software synchronization method.

Design and performance analysis of landmark-based INS/Vision Navigation System for UAV

This paper focuses mainly on the system design and performance analysis of the landmark-based INS/Vision Navigation System (INS/VNS). Firstly, the state and observation equations of the landmark-based INS/VNS are established. Secondly, the observability of the landmark-based INS/VNS with different number of landmarks is analyzed according to the rank of observability matrix. And Kalman Filter (KF) is designed for the landmark-based INS/VNS to estimate the errors of the navigation parameters for Unmanned Aerial Vehicle (UAV). Finally, the performance of the landmark-based INS/VNS is analyzed and compared with that of the landmark-based IMU/Vision Navigation System (IMU/VNS) via simulations.

Design of a fault detection and diagnose system for intelligent unmanned aerial vehicle navigation system

A secure control system is of great importance for unmanned aerial vehicles, especially in the condition of fault data injection. As the source of the feedback control system, the Inertial navigation system/Global position system (INS/GPS) is the premise of flight control system security. However, unmanned aerial vehicles have the requirement of lightweight and low cost for airborne equipment, which makes redundant device object unrealistic. Therefore, the method of fault detection and diagnosis is desperately needed. In this paper, a fault detection and diagnosis method based on fuzzy system and neural network is proposed. Fuzzy system does not depend on the mathematical model of the process, which overcomes the difficulties in obtaining the accurate model of unmanned aerial vehicles. Neural network has a strong self-learning ability, which could be used to optimize the membership function of fuzzy system. This paper is structured as follows: first, a Kalman filter observer is introduced to calculate the residual sequences caused by different sensor faults. Then, the sequences are transmitted to the fault detection and diagnosis system and fault type can be obtained. The proposed fault detection and diagnosis algorithm was implemented and evaluated with real datasets, and the results demonstrate that the proposed method can detect the sensor faults successfully with high levels of accuracy and efficiency.

AR-Marker/IMU Hybrid Navigation System for Tether-Powered UAV

Electrically powered unmanned aerial vehicles (UAV) are useful in performing inspection at various infrastructures or plants. A power supply through a tether cable is effective in extending flight time. During inspection activities, some or all satellites may be occluded. UAVs for inspection must be operated even in GPS-denied areas; therefore, a navigation system for GPS-denied areas is required. Depth information cannot be obtained correctly by a monocular camera. The ARToolkit, which is widely applied in augmented reality (AR), is not sufficient as a UAV navigation system. We have proposed a hybrid navigation method that integrates the ARToolkit and an inertial measurement unit (IMU). An analytic solution for both the worst and best estimation of yaw angle can be obtained by simple computation and helps remove outliers in measurements. From experimental results, it was proven that position estimation using the proposed method corresponded reasonably; however, it was necessary to correct the difference between the camera origin and the body’s center of gravity.

UAV navigation system using line-based sensor pose estimation

This work presents a mapping and tracking system based on images to enable a small Unmanned Aerial Vehicle (UAV) to accurately navigate in indoor and GPS-denied outdoor environments. A method is proposed to estimate the UAV’s pose (i.e., the 3D position and orientation of the camera sensor) in real-time using only the on-board RGB camera as the UAV travels through a known 3D environment (i.e., a 3D CAD model). Linear features are extracted and automatically matched between images collected by the UAV’s onboard RGB camera and the 3D object model. The matched lines from the 3D model serve as ground control to estimate the camera pose in real-time via line-based space resection. The results demonstrate that the proposed model-based pose estimation algorithm provides sub-meter positioning accuracies in both indoor and outdoor environments. It is also that shown the proposed method can provide sparse updates to correct the drift from complementary simultaneous localization and mapping (SLAM)-derived pose estimates.

Observability analysis and filter design for a vision inertial absolute navigation system for UAV using landmarks

This paper focuses mainly on the research of the vision inertial absolute navigation system (VIANS) for Unmanned Aerial Vehicle (UAV) using landmarks. Firstly, the model of the VIANS is established. Secondly, Extend Kalman Filter (EKF) and Unscented Kalman Filter (UKF) are designed, respectively, to get the estimations of absolute position, absolute velocity and attitude of the UAV. And the nonlinear degree of the VIANS is analyzed to design a more suitable navigation filter for the VIANS. Thirdly, the observability matrix of the VIANS is derived based on the Differential Geometric theory, and the influence of the landmark distribution on the degree of observability of the VIANS is analyzed based on the condition number of the observability matrixes. Finally, simulations are carried out to validate the correctness of observability analysis and the effectiveness of the designed navigation filter.

An Integrated Unmanned Aerial Vehicle Navigation System Capable of Fault Detection and Isolation

An Integrated Unmanned Aerial Vehicle Navigation System Capable of Fault Detection and Isolation

Employing smartphone as on-board navigator in unmanned aerial vehicles: implementation and experiments

Purpose – This study aims to investigate if smartphone sensors can be used in an unmanned aerial vehicle (UAV) localization system. With the development of technology, smartphones have been tentatively used in micro-UAVs due to their lightweight, inexpensiveness and flexibility. In this study, a Samsung Galaxy S3 smartphone is selected as an on-board sensor platform for UAV localization in Global Positioning System (GPS)-denied environments and two main issues are investigated: Are the phone sensors appropriate for UAV localization? If yes, what are the boundary conditions of employing them? Design/methodology/approach – Efficient accuracy estimation methodologies for the phone sensors are proposed without using any expensive instruments. Using these methods, one can estimate his phone sensors accuracy at any time without special instruments. Then, a visual-inertial odometry scheme is introduced to evaluate the phone sensors-based path estimation performance. Findings – Boundary conditions of using smartphone in a UAV navigation system are found. Both indoor and outdoor localization experiments are carried out and experimental results validate the effectiveness of the boundary conditions and the corresponding implemented scheme. Originality/value – With the phone as a payload, UAVs can be further realized in smaller scale at lower cost, which will be used widely in the field of industrial robots.

Distributed Filtering-Based Autonomous Navigation System of UAV

In this paper, we propose a systematic framework for the autonomous navigation system based on distributed filtering for an Unmanned Aerial Vehicle (UAV). The proposed framework consists of the design and algorithm of the autonomous navigation. Therein, the camera mounted on the UAV functions as a navigation sensor targeted for navigation and positioning. In order to reduce the computational complexity and exclude the risk caused by Global Positioning System (GPS) outage, an autonomous navigation system based on distributed filtering is designed and realized. When GPS is available by monitoring the GPS integrity, sensor information from Strapdown Inertial Navigation System (SINS) and GPS is fused using a 7-state Conventional Kalman Filter (CKF) to estimate the full UAV state (position, velocity and attitude); when GPS is unavailable, sensor information from gyroscopes, accelerometers and magnetometer is fused using a 4-state Extended Kalman Filter (EKF) to estimate the attitude and heading of the UAV, and sensor information from SINS and vision positioning system is fused using a 7-state Incoordinate Interval Kalman Filter (IIKF) to estimate the position and velocity of the UAV. In addition, the second-order vertical channel damping loop is adopted to fuse measurements from a barometer with those of SINS, which suppresses the divergence of the vertical channel error and makes the altitude information calculated by SINS trustable. Both ground and flight experiments of the autonomous navigation system have been carried out. The test results show that the system can provide stabilized attitude information in long durations, and can realize the automatic flight control of UAV.

Vision-aided terrain referenced navigation for unmanned aerial vehicles using ground features

A vision-aided terrain referenced navigation (VATRN) approach is addressed for autonomous navigation of unmanned aerial vehicles (UAVs) under GPS-denied conditions. A typical terrain referenced navigation (TRN) algorithm blends inertial navigation data with measured terrain information to estimate vehicle’s position. In this paper, a low-cost inertial navigation system (INS) for UAVs is supplemented with a monocular vision-aided navigation system and terrain height measurements. A point mass filter based on Bayesian estimation is employed as a TRN algorithm. Homograpies are established to estimate the vehicle’s relative translational motion using ground features with simple assumptions. And the error analysis in homography estimation is explored to estimate the error covariance matrix associated with the visual odometry data. The estimated error covariance is delivered to the TRN algorithm for robust estimation. Furthermore, multiple ground features tracked by image observations are utilized as multiple height measurements to improve the performance of the VATRN algorithm.

Switching algorithm for data fusion of small low-cost UAV navigation system

The switching algorithm for fusion data of satellite navigation system and standard onboard sensors for low-cost unmanned aerial vehicle (UAV) is proposed and numerically studied by the example of the hypothetical small jet-propelled UAV.

AERIAL IMAGES FROM AN UAV SYSTEM: 3D MODELING AND TREE SPECIES CLASSIFICATION IN A PARK AREA

Abstract. The use of aerial imagery acquired by Unmanned Aerial Vehicles (UAVs) is scheduled within the FoGLIE project (Fruition of Goods Landscape in Interactive Environment): it starts from the need to enhance the natural, artistic and cultural heritage, to produce a better usability of it by employing audiovisual movable systems of 3D reconstruction and to improve monitoring procedures, by using new media for integrating the fruition phase with the preservation ones. The pilot project focus on a test area, Parco Adda Nord, which encloses various goods' types (small buildings, agricultural fields and different tree species and bushes). Multispectral high resolution images were taken by two digital compact cameras: a Pentax Optio A40 for RGB photos and a Sigma DP1 modified to acquire the NIR band. Then, some tests were performed in order to analyze the UAV images' quality with both photogrammetric and photo-interpretation purposes, to validate the vector-sensor system, the image block geometry and to study the feasibility of tree species classification. Many pre-signalized Control Points were surveyed through GPS to allow accuracy analysis. Aerial Triangulations (ATs) were carried out with photogrammetric commercial software, Leica Photogrammetry Suite (LPS) and PhotoModeler, with manual or automatic selection of Tie Points, to pick out pros and cons of each package in managing non conventional aerial imagery as well as the differences in the modeling approach. Further analysis were done on the differences between the EO parameters and the corresponding data coming from the on board UAV navigation system.

Development of a low-cost agricultural remote sensing system based on an autonomous unmanned aerial vehicle (UAV)

To provide and improved remote sensing a system based on an autonomous UAV was developed. The system was based on an easily transportable helicopter platform weighing less than 14 kg. Equipped with a multi-spectral camera and autonomous system, the UAV system was capable of acquiring multi-spectral images at the desired locations and times. An extended Kalman filter (EKF) based UAV navigation system was designed and implemented using sensor fusion techniques. A ground station was designed to be the interface between a human operator and the UAV to carry out mission planning, flight command activation, and real-time flight monitoring. Based on the navigation data, and the waypoints generated by the ground station, the UAV could be automatically navigated to the desired waypoints and hover around each waypoint to collect field image data. An experiment using the UAV system to monitor turf grass glyphosate application demonstrated the system, which indicated the UAV system provides a flexible and reliable method of sensing agricultural field with high spatial and temporal resolution of image data.

Particle Swarm Guidance System for Autonomous Unmanned Aerial Vehicles in an Air Defence Role

This work investigates the utilisation of Particle Swarm Optimisation (PSO) for the non-deterministic navigation of Unmanned Aerial Vehicles (UAVs), allowing them to work cooperatively toward the goal of protecting a wide area against airborne attack. To negate the PSO's inherent weakness in dynamic environments, a neighbourhood scheme is proposed that not only enables the efficient interception of targets several times faster than the UAVs but also facilitates the maintenance of effective airspace coverage. Empirical results suggest that these techniques may indeed be of use in autonomous navigation systems for UAVs in air defence roles.