UAV Flight Research Articles

An Improved Spherical Vector and Truncated Mean Stabilization Based Bat Algorithm for UAV Path Planning

Unmanned aerial vehicles have a wide range of applications. An intelligent optimization algorithm based on the traditional bat algorithm (BA) is investigated in this paper for UAV flight path planning in a static complex environment. The primary goal of this work is to develop a safer flight path while considering the feasibility of the UAV and the requirements for safe operation. This research proposes an improved spherical coordinate and truncated average stable strategy-based bat optimization algorithm (TMS-SBA). The algorithm uses the UAV’s motion space to encode the operator, and by substituting a new bat for the worst of the old one after each iteration to increase population diversity, the algorithm can converge quickly in a complex environment while maintaining stable operation. In addition, the flight path is smoothly generated by using B-spline curves to make the planned path suitable for UAV. MATLAB simulation experiments show that, compared with other traditional swarm intelligent algorithms, TMS-SBA can successfully generate feasible and effective optimal solutions in complex environments and plan shorter, safer, and more accessible flight paths for UAV.

Samolokalizacja bezzałogowego statku powietrznego uwzględniająca zmienną orientację kamery

This publication proposes a visual localization method using images from a simulated camera and a georeferenced map. The UAV model and flight simulation were made in the MATLAB Simulink package, which sent UAV orientation data to the described program. The visualization of the camera image was performed in real time using the FlightGear software, the image of which was also captured by the NW program. This method is performed by two processes in two modules: Global Positioning Component and Motion Positioning Component. The first one compares the image from the simulated camera with the orthophotomap. The second determines the position based on the assessment of the displacement of characteristic points in the image in relation to the last known location. The result of the operation of both modules is illustrated in the graphic window of the NW application, which allows for a visual comparison of the obtained results. With the global method of location, additional camera orientation correction is required to determine the position in 2D space. For this purpose, data on the current camera orientation expressed in quaternions were used. This allowed for the introduction of a position correction, which significantly improved the accuracy of the result obtained in the GPC module despite significant UAV tilts during the simulated flight.

UAV Path Planning in Dynamical Environment: A Novel ICACO-IDWA Algorithm

In this paper, a novel UAV path planning algorithm based on improved cellular ant colony algorithm and dynamic window algorithm (ICACO-IDWA) is proposed to solve the problem of dynamically changing threat during actual flight. The main innovations of this paper are as follows. (a) The hexagon grid method is proposed to model the UAV flight space, which solves the problem of inconsistent simulation time step. (b) A novel ICACO-IDWA algorithm is proposed. In the first stage, the optimal path is obtained by the improved cellular ant colony algorithm (ICACO). In the second stage, the improved dynamic window algorithm (IDWA) is used to optimize the optimal path considering dynamic threat. Through the algorithm, the UAV path planning with dynamic threat change is realized. Finally, simulation results verify the effectiveness of the proposed model and algorithm.

A dense obstacle avoidance algorithm for UAVs based on safe flight corridor

Aiming at the problem of autonomous obstacle avoidance of fixed-wing UAVs in a complex, dense and multi-obstacle environment, a path planning algorithm for fixed-wing UAVs based on a safe flight corridor is proposed. The difficulty of avoiding dense obstacles lies in the choice of obstacle circumvention and traversal: although circumvention is safer, the flight cost is greater; although the traversal cost is lower, the safety threat is higher. How to quickly solve the optimal path is the core issue. This paper firstly defines a safe flight corridor innovatively based on the maneuvering characteristics of fixed-wing UAVs and the Dubins curves. By comprehensively considering UAV flight safety and flight costs, an obstacle threat evaluation function is constructed. Secondly, in view of the computational complexity caused by the dense obstacles, an obstacle clustering algorithm based on obstacle density is proposed, and the nonlinear evaluation function in a high dynamic environment is quickly approximated by Monte Carlo sampling method. Finally, simulations verify the effectiveness of the proposed algorithm in solving dense obstacle avoidance for fixed-wing UAVs.

Optimizing FANET Lifetime for 5G Softwarized Network Provisioning

Recently, Flying Ad Hoc Networks (FANET) have been proposed to empower 5G networks to support complex missions and provide ubiquitous connectivity to heterogeneous devices. However, it is needed to cope with the limited UAV capabilities (e.g. limited available energy to supply engines and computing elements, limited computing capabilities), as well as with the need to provide network and application services as foreseen in highly dynamic and time varying 5G ecosystems. This paper presents for the first time a comprehensive framework that integrates a FANET with a 5G network, with the aim of providing services that can be even chained with each other. This model is comprehensive in the sense that it takes into account physical constraints of the devices, as well as features and requirements of traffic flows. For this framework, the paper proposes a mathematical optimization model, allowing Virtual Function (VF) placement and chaining, aimed at minimizing energy consumption and service unsatisfaction probabilities of the FANET as a whole without employing heuristics for the solution of the problem. Two placement strategies named MLP and WMP are introduced and compared with the standard placement strategy named NoShP. An extensive numerical analysis shows that MLP and WMP allow us to well catch network dynamics and to reduce the number of virtual functions needed while decreasing the power consumption, so increasing UAV flight time and network lifetime.

Fixed-Wing UAV Flight Operation under Harsh Weather Conditions: A Case Study in Livingston Island Glaciers, Antarctica

How do the weather conditions typical of the polar maritime glaciers in the western Antarctic Peninsula region affect flight operations of fixed-wing drones and how should these be adapted for a successful flight? We tried to answer this research question through a case study for Johnsons and Hurd glaciers, Livingston Island, using a fixed-wing RPAS, in particular, a Trimble UX5 UAV with electric pusher propeller by brushless 700 W motor, chosen for its ability to fly long distances and reach inaccessible areas. We also evaluated the accuracy of the point clouds and digital surface models (DSM) generated by aerial photogrammetry in our case study. The results were validated against ground control points taken by differential GNSS techniques, showing an accuracy of 0.16 ± 0.12 m in the vertical coordinate. Various hypotheses were proposed and flight-tested, based on variables affecting the flight operation and the data collection, namely, gusty winds, low temperatures, battery life, camera configuration, and snow reflectivity. We aim to provide some practical guidelines that can help other researchers using fixed-wing drones under climatic conditions similar to those of the South Shetland Islands. Performance of the drone under harsh weather conditions, the logistical considerations, and the amount of snow at the time of data collection are factors driving the necessary modifications from those of conventional flight operations. We make suggestions concerning wind speed and temperature limitations, and avoidance of sudden fog banks, aimed to improve the planning of flight operations. Finally, we make some suggestions for further research.

Signal corrector and decoupling estimations for UAV control

AbstractFor a class of uncertain systems with large-error sensing, the low-order stable signal corrector and observer are presented for signal correction and uncertainty estimation according to completely decoupling estimation. The model-free signal corrector can reject the bounded stochastic disturbance/error in global position sensing, and system uncertainty can be estimated by the observer, even the existence of large disturbance in position sensing. Furthermore, a general form of signal corrector is given. The describing function method is used to analyse the robustness of the corrector in frequency domain, and the parameter selection rules are presented. The merits of the signal corrector includes its model free, gain-bounded stable structure, sufficient rejection of bounded stochastic disturbance/error in sensing and ease of parameters’ regulation. The corrector and observer are applied to a UAV navigation and control for large disturbance/error corrections in position/attitude angle and the uncertainties estimation in the UAV flight dynamics. The control laws are designed according to the correction-estimation results. Finally, experiments demonstrate the effectiveness of the proposed method.

UAV flight path design using multi-objective grasshopper with harmony search for cluster head selection in wireless sensor networks

UAV flight path design using multi-objective grasshopper with harmony search for cluster head selection in wireless sensor networks

Model Predictive Control Based on ILQR for Tilt-Propulsion UAV

The transition flight of tilt-propulsion UAV is a complex and time-varying process, which leads to great challenges in the design of a stable and robust controller. This work presents a unified model predictive controller, which can handle the full envelope from vertical take-off and landing to cruise flight, to mean that the UAV can achieve a near-optimal transition flight under uncertainty conditions. Firstly, the nonlinear dynamic model of the tilt-propulsion UAV is developed, in which the aerodynamic/propulsion coupling effect of the ducted propeller is considered. Then, a control framework, including global trajectory planning and finite horizon control, is designed. Taking the planned global trajectory as the reference input, a controller is proposed with an inner layer based on ILQR optimization and an outer layer based on feedback correction and forward rolling of the MPC frame. The ILQR-MPC controller has high computational efficiency to deal with nonlinear problems, and has the ability to give full play to UAV’s control ability and suppress uncertainty. Finally, the simulation results show that ILQR-MPC controller obviously performs better than the ILQR feedforward controller, and gains a scheduling PID controller and MPC controller.

Peering through the thicket: Effects of UAV LiDAR scanner settings and flight planning on canopy volume discovery

Unoccupied aerial vehicle laser scanning (UAV-LS) has been increasingly used for forest structure assessment in recent years due to the potential to directly estimate individual tree attributes and availability of commercial solutions. However, standardised procedures for campaign planning are still largely missing. This study investigated scanner properties and flight planning to provide recommendations on minimising forest canopy occlusion and thereby maximise exploration of canopy volume. A flight campaign involving two UAV-LS systems was conducted over a dense, wet tropical forest at the Paracou research station (French Guiana). Four experiments on scanner properties and flight planning were conducted, analysed and recommendations derived. First, the scanner pulse repetition rate (PRR) should be at least 100 kHz per 1 m s −1 flight speed based on 360° FOV for exploration of middle canopy strata (5 m to 20 m). Higher PRR are beneficial for exploration of lower canopy ( < 5 m) but would need to be increased exponentially to achieve linear improvement. Alternatively, flight speed could be reduced within the constraints given by the inertial measurement unit (IMU), but would increase flight time. Second, the scanner maximum range was identified as a proxy for the laser pulse power, which positively impacts canopy exploration. This was particularly the case when using multi-return capabilities. No saturation could be observed when increasing the laser power, suggesting that this is currently a limiting factor. Additionally, a smaller laser beam divergence and pulse width were plausible reasons for better exploration of the upper canopy just below the top of canopy. Third, off-nadir scanning angles up to 20° were found to result in similar occlusions, suggesting a practical FOV of 40° in the investigated dense forest. This number might be larger for open canopies. UAV-LS systems with viewing geometries that focus laser pulses downwards and within optimal ranges should be preferred. Fourth, using different horizontal flight directions in the mission planning favours minimisation of occlusion. A minimum of two different flight directions is suggested. However, specific optimal yaw angles were not possible to predict before flight. Therefore, including multiple directions ensures coverage of all possible configurations. Many of these investigated features can be optimised independently from each other, and should be considered before acquisition of new UAV-LS systems and flight mission planning. These results support the establishment of general guidelines for the investment in UAV-LS systems and optimal mission planning for forest structure assessment. • Introduction of absolute, geometrical and turbid occlusion concepts. • Longer range scanners explore more canopy volume facilitated by multiple returns. • Scanner wavelength possibly affects detection of woody elements vs foliage. • Scan angles ±20°off-nadir exhibited lowest occlusion effects. • Combining multiple flight directions supports canopy discovery.

МОДЕЛЮВАННЯ АВТОМАТИЧНОГО ПОЛЬОТУ МАЛОГО БЕЗПІЛОТНОГО ЛІТАЛЬНОГО АПАРАТУ НАД ЛІНІЄЮ-МАРКЕРОМ

The subject of study is the process of automating the behavior of an unmanned aerial vehicle during a flight following a given marker inside a room. The goal is to reduce the distance of deviation from the course and reduce the time to complete the flight along the trajectory set by the contrasting marker on the ground. The task: to conduct an analysis of existing libraries capable of performing automatic flight simulation and adaptation of the received code to the hardware platform of modern unmanned aerial vehicles; review the structure of the selected library, and modify the components of the library, which are necessary for the formation of automatic flight; create a model of the video processing and control system, check its operational parameters in a simulation environment. The methods used are: system analysis used to compare existing libraries of UAV flight simulation information systems within the framework of the assigned tasks, artificial intelligence methods for the development of a video stream data analysis subsystem and recognition of the provided marker during UAV flight simulation, system programming methods for automatic control of UAV flight based on the data obtained from the visual information analysis system, a method of simulation modeling to check the correctness of the developed algorithms in collaborative work. The following results were obtained. The choice of the library for the development and modeling of subsystems of automatic flight of a small unmanned aerial vehicle in a room is justified, a subsystem for processing video information obtained from the built-in camera of a UAV is created, an automatic flight control subsystem is developed taking into account the data of the video information analysis subsystem; a series of experiments was conducted on a simulation model to check the system's performance. Conclusions. In the course of the study, a model of the autonomous flight of a small unmanned aerial vehicle capable of flying over a contrasting color marker placed on the surface over which the UAV flies was improved. Unlike existing models, the system does not require the use of additional equipment, all computing operations must be performed on board the UAV, only the data of the video stream of the built-in camera and the coordinates of the UAV are transmitted to the control station, in addition, the minimum number of additional libraries necessary for creating a simulation model was used simulation modeling. This will reduce the time it takes to create a model, and, accordingly, conduct more experiments on improving parameters to achieve the goal of research.

Simulating a Hybrid Acquisition System for UAV Platforms

Currently, there is a rapid trend in the production of airborne sensors consisting of multi-view cameras or hybrid sensors, i.e., a LiDAR scanner coupled with one or multiple cameras to enrich the data acquisition in terms of colors, texture, completeness of coverage, accuracy, etc. However, the current UAV hybrid systems are mainly equipped with a single camera that will not be sufficient to view the facades of buildings or other complex objects without having double flight paths with a defined oblique angle. This entails extensive flight planning, acquisition duration, extra costs, and data handling. In this paper, a multi-view camera system which is similar to the conventional Maltese cross configurations used in the standard aerial oblique camera systems is simulated. This proposed camera system is integrated with a multi-beam LiDAR to build an efficient UAV hybrid system. To design the low-cost UAV hybrid system, two types of cameras are investigated and proposed, namely the MAPIR Survey and the SenseFly SODA, integrated with a multi-beam digital Ouster OS1-32 LiDAR sensor. Two simulated UAV flight experiments are created with a dedicated methodology and processed with photogrammetric methods. The results show that with a flight speed of 5 m/s and an image overlap of 80/80, an average density of up to 1500 pts/m2 can be achieved with adequate facade coverage in one-pass flight strips.

Path Planning of Electric VTOL UAV Considering Minimum Energy Consumption in Urban Areas

As a new mode of transportation in the future, electric vertical take-off and landing unmanned aerial vehicles (eVTOL UAV) can undertake the task of logistics distribution and carry people in urban areas. It is challenging to carry out research designed to plan the path of eVTOL UAVs which can have a safe and sustainable operation mode in urban areas. Therefore, this work proposes a method for planning an obstacle-free path for eVTOL UAVs in urban areas with the goal of minimizing energy consumption. It aims to improve the safety and sustainability of eVTOL UAV operations. Based on variations of air density with height, a more accurate formula for calculating battery energy consumption of eVTOL UAV is derived. It is used in the vertical takeoff and landing phase and horizontal flight phase, respectively. Considering the influence of buildings on eVTOL UAV operation, a path planning method applicable to complex urban environments is proposed. The safe nodes of eVTOL UAV flight are obtained by using Voronoi diagrams based on building locations. Then, the complete shortest and obstacle-free path is obtained by using a Dubins geometric path and Floyd algorithm. After obtaining the obstacle-free paths for all flight height zones, the battery energy consumption of the eVTOL UAV in each flight height zone is calculated. Then, the flight height with the minimum energy consumption is obtained. The simulation results show that the path length obtained by the proposed path planning method is shorter than that obtained by particle swarm optimization; the total battery energy consumption changes in the same pattern in the low-altitude areas and high-altitude areas; the difference between the maximum and minimum energy consumption in the small area enables the eVTOL UAV to cover about 350 m more, and about 420 m more in the large area. Therefore, in future high-frequency UAV mission flights, choosing the altitude with the lowest energy consumption can reduce UAV operator costs. It can also significantly increase UAV transport range and make UAVs operate more sustainably.

CALIBRATION OF THE LOW-COST UAV CAMERA ON A SPATIAL TEST FIELD

For several years, the widening range of applications of unmanned aerial vehicles can be noticed not only in the literature review but also in the market of services offered – also in the geodetic sector. While there is a wide range of professional UAVs for aerial mapping tasks, these platforms are expensive. In this study, it was checked whether the calibration of a low-cost drone camera allows for obtaining an accuracy acceptable for photogrammetric studies. For this purpose, a spatial test field was designed on which a multivariate calibration of the UAV camera and control of the obtained results were carried out. Using the elements of the camera’s internal orientation obtained during the calibration process, it was not possible to achieve high accuracy of photogrammetric measurements on control images. This may indicate a problem with the repeatability of determining the elements of internal orientation of the analyzed camera, and thus with the instability of the autofocus system. Nevertheless, the use of the obtained results from the camera calibration as precise approximations of the elements of the camera’s internal orientation had a positive effect on the solution of the image network using the bundle adjustment and the fitting of the spatial model to the ground control points. In addition, the UAV flight over the created spatial test field allowed for a reliable assessment of the possibilities and accuracy that can be obtained on the basis of images from a low-cost drone.

Improving Dual-UAV Aided Ground-UAV Bi-Directional Communication Security: Joint UAV Trajectory and Transmit Power Optimization

This paper investigates a dual-unmanned aerial vehicle (UAV) aided communication system to improve the security of the communication between ground devices and UAVs. Different from the existing works which ignored ground devices mobility and just considered one-way communication security between ground devices and UAVs, we allow the devices to be mobile and consider bi-directional ground-UAV communication security. Specifically, one UAV server communicates with mobile ground devices, and the other UAV jammer is invoked to confuse eavesdroppers. Our objective is to maximize the worst-case average secrecy rate by the joint optimization of UAV trajectory and sender transmit power. To achieve it, we first formulate the worst-case average secrecy rate maximization problem as a constrained Markov decision process (CMDP) under the constraints of UAV flight space, flight speed, energy capacity, anti-collision, and peak transmit power. Then, we design a Deep Deterministic Policy Gradient (DDPG) based algorithm to solve the CMDP. Experiment results demonstrate that our joint optimization scheme can enhance the communication security in terms of the secrecy rate in both UAV-to-ground (U2G) case and ground-to-UAV (G2U) case. Besides, it is observed that UAV trajectory and sender transmit power have different impacts on the communication security in U2G case and G2U case.

Double-layer fuzzy adaptive NMPC coordinated control method of energy management and trajectory tracking for hybrid electric fixed wing UAVs

The energy management and trajectory tracking control are crucial to realize long-endurance autonomous flight for hybrid electric UAVs. This study aims to comprehensively consider energy management and trajectory tracking for hybrid electric fixed wing UAVs with photovoltaic panel/fuel cell/battery. A double-layer fuzzy adaptive nonlinear model predictive control method (DFNMPC) is proposed. Separated by the surplus demand power, energy management and trajectory tracking problem are decoupled into the high-layer fuzzy adaptive nonlinear model predictive controll problem (H-FNMPC) and low-layer fuzzy adaptive nonlinear model predictive controll problem (L-FNMPC). H-FNMPC solves the trajectory tracking and navigation control probelm for the greatest benefit of solar energy. L-FNMPC solves the power allocation problem of hybrid energy system for minimum equivalent hydrogen consumption. A fuzzy adaptive prediction horizon adjustment method based on UAV maneuvering degree is proposed to effectively improve proposed method adaptability to different mission profiles. Analogously, a fuzzy adaptive equivalent hydrogen consumption factor adjustment method in L-FNMPC is proposed to ensure the flexible utilization of battery. In addition, an equivalent hydrogen flow rate calculation method based on the real-time current ratio is proposed for PV/FC/Battery hybrid energy system. Numerical simulation results including a spiral trajectory tracking and a quadrilateral trajectory tracking, demonstrate that DFNMPC can simultaneously handle energy management and trajectory tracking problem for hybrid electric UAVs. Compared to hierarchical fuzzy state machine strategy, DFNMPC can save 13.3% hydrogen for the spiral trajectory tracking, and 56.9% for the quadrilateral trajectory tracking. It indicates that the energy efficiency can be improved from both levels of energy management and flight motion. The proposed method prospected for exploring high-energy-efficiency autonomous flight of hybrid electric UAVs in the future.

Suppressing UAV payload swing with time-varying cable length through nonlinear coupling

This paper introduces a method to navigate the UAV flight and to suppress the payload swing by a nonlinear coupling control augmented with time-varying cable length. Special error signals are introduced that bring further coupling of dynamics among various states of the UAV and enable the flight control to effectively suppress the payload swing. The control is designed with the help of the dynamic model of the UAV in lift and transport operation. The stability of the control is proven in the Lyapunov sense. Extensive simulations and experiments have been carried out to demonstrate the effectiveness of the proposed nonlinear coupling control. Two popular control approaches in the literature are chosen to compare with the current work. It is found that the proposed control is quite effective in suppressing the payload swing while tracking the pre-determined path at the same time.

Battery management system of UAV Based on IOT

Lithium ion battery is a new type of high-energy battery developed successfully in the 20th century. Because of its high energy, high battery voltage, wide operating temperature range, long storage life and other advantages, it has been widely used in military and civil micro unmanned aerial vehicles. In order to monitor the charge and discharge and flight of UAV in real time, this paper designs a UAV battery management system based on the Internet of things, which transmits the battery data to the Internet of things platform in real time through the wireless transmission module. After testing, this design can achieve the effect of real-time monitoring of UAV battery pack.

Multiscale Parallel Heading Error Processing Model for Polarization Compass

A multiscale parallel processing model of heading error for the polarization compass (PC) is developed in this article. Existing state-of-the-art schemes of raising heading measurement precision are implemented relying on not only ideal assumption in noiseless heading output from the PC but also the polynomial method employed to calculate the heading error caused by the varied attitude angles of PC, which leads to fall sharply in the PC heading accuracy in real applications. Moreover, the aforementioned denoising and heading error compensation are conducted independently. Here we specifically concentrate on a novel and promising parallel processing model for heading error as a way to improve the PC heading accuracy, where a comprehensive analysis of heading error sources is first carried out for the PC. Subsequently, the proposed multiscale parallel error processing model is presented to improve heading accuracy for the PC by repressing noise as well as compensating heading errors that are implemented synchronously. Specifically, variational mode decomposition (VMD) is exploited to decompose the original heading data output from PC into multiple intrinsic mode functions, which characterizes binned modes (BIMFs) with high-to-low frequencies by seeking the ensemble of the decomposed modes and their respective center frequencies. An adaptive multiscale principle component analysis (MS-PCA) scheme denoising approach exploiting the principle contribution rate is performed to attenuate the noise in the high-frequency BIMFs. An effective compensation approach with a long short-term memory (LSTM) modeling is introduced straightforward to compensate for the heading error for the low-frequency BIMF concurrently. The rotational test is carried out to verify that the heading error processed by VMD-PCA denoising and LSTM compensating, which are the most effective among the comparison algorithms and the RMSE of 0.3505 and 0.2668, increased by 80.10% and 82.00%, respectively. The UAV flight test demonstrates that the RMSE of the parallel scheme using VMD-PCA and LSTM performed simultaneously is the minimum values of 0.1282 and 71% higher than the original navigation error. It can be seen from various experimental results that the proposed multiscale parallel processing model of denoising and error compensation procedure performed synchronously shows attractive performance gains with respect to emerging single processing in terms of increasing the accuracy of the PC heading measurement.

Automatic Landing of Unmanned Aerial Vehicles via Wireless Positioning System with Pseudo-Conical Scanning.

In this work, a wireless UAV unmanned landing system is considered, using the principles of pseudo conical scanning with a phased antenna array (PAA). The basic requirements for the characteristics and parameters of the system as a whole and of its components are defined. Special attention is paid to the primary sensor of the system—PAA with electronic scanning. A variant with a minimum number of four states on the radiation pattern of a low-budget patch PAA was studied. A linear regression of the difference characteristics of the measured radiation beams is proposed, which allows the practical application of the recommended landing algorithm with low computational complexity. Systematic and random positioning errors, both by measurement and by Monte Carlo simulation, were studied. Obtained statistical results prove the algorithm convergence and acceptable accuracy for the system implementation. They are applied if necessary to adjust the Kalman filter parameters. The proposed wireless system can be used for unmanned landing, tracking, and navigating the UAV in flight, or wireless navigation of other mobile objects.