Unmanned Aerial Vehicle Mission Research Articles

Offline and Online UAV-Enabled Data Collection in Time-Constrained IoT Networks

Recently, unmanned aerial vehicle (UAV) technology is endorsed to enable applications in domains such as Internet of Things (IoT), wireless sensor networks, and cellular networks. Particularly, time-sensitive and energy-limited IoT networks located in hard-to-reach areas require efficient/cost-effective data collection solution. To address this matter, we consider a multi-UAV enabled IoT network, where several UAVs collect data from time-constrained sensor nodes (SNs). In our framework, SNs are managed by cluster heads (CHs), then UAVs collect data from them. We formulate the problem of minimizing system’s deployment costs and operating energy to collect data within deadlines, subject to communication, UAVs mission time, and battery capacity constraints. To solve it, we propose a two-step approach. In the first step, an efficient K-means based method groups SNs and deploys CHs. Then, UAV-based offline and online data collection methods are proposed. In the offline setting where the system’s status is known beforehand, UAV paths are determined using near-optimal meta-heuristics. In simulations, the nearest-neighbor and Tabu search provided best offline performances, conditioned on the system’s parameters. In the online setting where no system information is available, deep reinforcement learning (DRL) based approaches are proposed. Results demonstrate the superiority of the actor-critic solution.

The Multi-Gas Sensor for Remote UAV and UGV Missions-Development and Tests.

In this article, we present a versatile gas detector that can operate on an unmanned aerial vehicle (UAV) or unmanned ground vehicle (UGV). The device has six electrochemical modules, which can be selected to measure specific gases, according to the mission requirements. The gas intake is realized by a miniaturized vacuum pump, which provides immediate gas distribution to the sensors and improves a fast response. The measurement data are sent wirelessly to the operator’s computer, which continuously stores results and presents them in real time. The 2 m tubing allows measurements to be taken in places that are not directly accessible to the UGV or the UAV. While UAVs significantly enhanced the versatility of sensing applications, point gas detection is challenging due to the downwash effect and gas dilution produced by the rotors. In our work, we demonstrated the method of downwash effect reduction at aerial point gas measurements by applying a long-distance probe, which was kept between the UAV and the examined object. Moreover, we developed a safety connection protecting the UAV and sensor in case of accidental jamming of the tubing inside the examined cavity. The methods presented provide an effective gas metering strategy using UAVs.

Modelling Robust Delivery Scenarios for a Fleet of Unmanned Aerial Vehicles in Disaster Relief Missions

Besides commercial and military applications, unmanned aerial vehicles (UAVs) are now used more commonly in disaster relief operations. This study proposes a novel model for proactive and reactive planning (different scenarios) that allow for a higher degree of realism, thus a higher likelihood for a mission of being executed according to the plan even when weather forecasts are changing. The novelty of this study results from the addition of a function of resistance of UAVs mission to changes in weather conditions. We link the influence of weather conditions on the UAV’s energy consumption. The goal is to ensure the completion of planned deliveries by a fleet of UAVs under changing weather conditions before their batteries discharge and to identify the emergency route for returned if the mission cannot be completed. An approach based on constraint programming is proposed, as it has proven to be effective in various contexts, especially related to the nonlinearity of the system’s characteristics. The proposed approach has been tested on several instances, which have allowed for analyzing how the plan of mission is robust to the changing weather conditions with different parameters, such as the fleet size, battery capacity, and distribution network layout.

UAV trajectory optimisation with limited onboard energy budget

PurposeThe purpose of this paper is to generate optimised trajectories for an unmanned aerial vehicle (UAV) during a forest fire detection mission. It is assumed that the UAV flies 3D curvature-constrained Dubins manoeuvres and has a limited amount of battery energy that prevents it from covering the entire search area in a single trip.Design/methodology/approachIn this paper, the search area is discretised into a grid of multiple targets, and each target assigned with a score that is proportional to the time elapsed since the last UAV visit. This problem, known as Dubins Airplane Orienteering Problem, consists of finding the number and order of targets to visit and the UAV heading at each target that maximises the total trip score without exceeding the UAV battery energy. The solution is found using the Randomised Variable Neighbourhood Search metaheuristic. All target scores are updated after each trajectory generation according to the elapsed time since the last UAV visit.FindingsThe proposed approach produced feasible results when generating optimised trajectories for a fire detection mission context where energy battery constraints are important.Practical implicationsThe authors carry out the planning of UAV missions with limited amounts of onboard energy such as a real fire detection mission using a single electric propulsion and fixed-wing UAV.Originality/valueThis paper introduces an energy-based approach to the Dubins Airplane Orienteering Problem, which takes into account the UAV performance and energy budget when generating optimised trajectories.

Improved contract network protocol algorithm based cooperative target allocation of heterogeneous UAV swarm

For the target attack mission of Unmanned Aerial Vehicle (UAV) swarm, the heterogeneous and large-scale characters usually bring about the uneven allocation problem. In the present work, an improved contract network protocol (CNP) based cooperative target allocation scheme is proposed to deal with heterogeneous overloading and time sequences problem. Firstly, the cooperative target allocation model aimed at heterogeneous UAVs and targets is established, including the total flight distance, flight time, target survival rate and load rate. Secondly, situation-based advantage value is calculated as the winning criterion to assign the UAV. Furthermore, two improved CNP assignment algorithms, are respectively designed for the one-to-many and many-to-one modes. Finally, the simulation results exhibit the superiority on better cooperative and more even assignment of the improved CNP scheme, when comparing with the basic CNP scheme.

An enhanced genetic algorithm for path planning of autonomous UAV in target coverage problems

Unmanned aerial vehicles (UAV) have become an important and integral part of military and civilian operations in recent years. In many UAV missions, the main purpose is to visit some predetermined checkpoints in operational space. If the number of checkpoints and constraints increases, finding a feasible solution may take up too much time. In this paper; the path planning problem of autonomous UAV in target coverage problems is solved by using artificial intelligent methods including genetic algorithm (GA), ant colony optimizer (ACO), Voronoi diagram, and clustering methods. The main contribution of this article is to propose initial population enhancement methods in GA, and thus accelerate convergence process. The first common enhancement to basic GA structure is to generate a sub-optimal path by implementing ACO. A sub-optimal path can be used to generate initial individuals. However, sub-optimal paths may have the problem that is collision with terrain. To avoid a UAV from any crash three approaches are integrated into an initial population phase of genetic algorithm. The first approach includes Voronoi vertices as additional waypoints to keep clear of trouble. The second approach consists of cluster centers which forms Voronoi vertices as supplemental waypoints. The final proposal comprises again cluster centers but based on a set of collision points. The proposed methods are tested in different three dimensional (3D) environments and the results are compared. Performance results show that collision with terrain surface is a local phenomenon and solving this issue by using the cluster center of collision points provides the best result including at least 70% or much more decrease in the required number of objective function evaluations.

Benthic habitat classification using multiscale GEOBIA on orthophoto images of Karimunjawa waters

The scale value is an important part of the segmentation stage which is part of Object-Based Image Analysis (OBIA). Selection of scale value can determine the size of the object which affects the results of classification accuracy. In addition to setting the scale value (multiscale), selection of machine learning algorithm applied to classify shallow water benthic habitat objects can also determine the success of the classification. Combination of setting scale values and classification algorithms are aimed to get optimal results by examining classification accuracies. This study uses orthophoto images processed from Unmanned Aerial Vehicle (UAV) mission intended to capture benthic habitat in Karimunjawa waters. The classification algorithms used are Support Vector Machine (SVM), Bayes, and K-Nearest Neighbors (KNN). The results of the classification of combination are then tested for accuracy based on the sample and Training Test Area (TTA) masks. The result shows that SVM algorithm with scale of 300 produces the best level of accuracy. While the lowest accuracy is achieved by using SVM algorithm with scale of 100. The result shows that the optimal scale settings in segmenting objects sequentially are 300, 200, and 100

Robust Environmental Sensing Using UAVs

In this article, we first investigate the quality of aerial air pollution measurements and characterize the main error sources of drone-mounted gas sensors. To that end, we build ASTRO+, an aerial-ground pollution monitoring platform, and use it to collect a comprehensive dataset of both aerial and reference air pollution measurements. We show that the dynamic airflow caused by drones affects temperature and humidity levels of the ambient air, which then affect the measurement quality of gas sensors. Then, in the second part of this article, we leverage the effects of weather conditions on pollution measurements’ quality in order to design an unmanned aerial vehicle mission planning algorithm that adapts the trajectory of the drones while taking into account the quality of aerial measurements. We evaluate our mission planning approach based on a Volatile Organic Compound pollution dataset and show a high-performance improvement that is maintained even when pollution dynamics are high.

GEOVISUALIZATION OF AERIAL PHOTOGRAMMETRIC FLIGHTS FOR DATA QUALITY ASSESSMENT

Abstract. Spatiotemporal data visualization plays an important role for simulating the changes over time and representing dynamic geospatial phenomena. In aerial photogrammetry, image acquisition is the most important stage for obtaining high-quality products; and can be affected by various factors such as the weather and illumination conditions, imaging geometry, etc. 3D simulation of the aircraft trajectories at the planning stage helps the flight planners to make better decisions especially for unmanned aerial vehicle (UAV) missions in areas with mixed land use land cover, such as rugged topography, water bodies, restricted areas, etc.; since images with poor texture or large differences in scale may deteriorate the quality of the final products. In this study, a geovisualization approach for photogrammetric flights carried out with UAVs or airplane platforms was implemented using CesiumJS Virtual Globe. The measured flight trajectory parameters, such as image perspective centre coordinates and the camera rotations, the time of acquisition, and the interior orientation parameters (IOPs) of the camera were used for spatiotemporal visualization. In the developed approach, the EOPs and IOPs of the images were utilized to reconstruct the flight paths, the camera position, the footprints of the acquired images on the ground, and the rotation of the aircraft; and to present them on a 3D web environment precisely. The approach was demonstrated by using two case studies, one from a UAV flight mission and the other one from an airplane carried out with a large-format aerial camera.

On-design and off-design operation performance assessmentsof an aero turboprop engine used on unmanned aerial vehicles (UAVs) in terms of aviation, thermodynamic, environmental and sustainability perspectives

In this study, a turboprop engine (TPE) of an unmanned aerial vehicle (UAV) is investigated for “on-design” and “off-design” conditions at maximum operation mode. One project point is chosen for on-design case for the cycle analysis. Other constraints and requirements are considered at off-design analysis in which cycle analysis is not affected. The off-design condition is generally used in emergency situation such as one-engine take-off, extreme flight speed, extreme altitude, and hot day operation. The aviation, energy, exergy, environmental, and sustainability analyses are performed to the TPE. For on-design and off-design conditions of the UAV TPE, the specific fuel consumptions are 0.287 kg/kW h and 0.282 kg/kW h, specific powers are 0.067 kW h/kg and 0.069 kW h/kg, thermal limiting ratios are 3.735 and 3.785, entalphy ratios are 4.482 and 4.555, energy efficiencies are 29.067% and 29.652%, exergy efficiencies are 27.38% and 27.93%, exergetic improvement potentials are 159.26 kW and 182.23 kW, ecological objective functions are −436.366 kW and −488.78 kW, environmental effect factors are 2.652 and 2.580, ecological effect factors are 3.652 and 3.580, exergetic sustainability indexes are 0.38 and 0.39, sustainable efficiency factors are 1.38 and 1.39, respectively. The results show that off-design condition has better fuel consumption, power, efficiency, improvement potential, environmental damage and sustainability than on-design condition. Also, the combustion chamber is the first component to be handled for the improvement of the overall system. UAVs are used for very deligant and important duties and their efficiency and sustainability play important role for their missions. UAV engines such as TPEs are the major power units to operate the overall system. Even a slight increase in efficiency and sustainability for the TPE engine can cause important feedback for the UAV mission. It is also important to assess the TPE for its environmental effects, because UAVs will play important role in the future for the transportation. As a result, this study is useful to assess turboprop engines of unmanned aerial vehicles in efficiency, sustainability and environmental aspects.

IADA*-RL: Anytime Graph-Based Path Planning with Deep Reinforcement Learning for an Autonomous UAV

Path planning algorithms are of paramount importance in guidance and collision systems to provide trustworthiness and safety for operations of autonomous unmanned aerial vehicles (UAV). Previous works showed different approaches mostly focusing on shortest path discovery without a sufficient consideration on local planning and collision avoidance. In this paper, we propose a hybrid path planning algorithm that uses an anytime graph-based path planning algorithm for global planning and deep reinforcement learning for local planning which applied for a real-time mission planning system of an autonomous UAV. In particular, we aim to achieve a highly autonomous UAV mission planning system that is adaptive to real-world environments consisting of both static and moving obstacles for collision avoidance capabilities. To achieve adaptive behavior for real-world problems, a simulator is required that can imitate real environments for learning. For this reason, the simulator must be sufficiently flexible to allow the UAV to learn about the environment and to adapt to real-world conditions. In our scheme, the UAV first learns about the environment via a simulator, and only then is it applied to the real-world. The proposed system is divided into two main parts: optimal flight path generation and collision avoidance. A hybrid path planning approach is developed by combining a graph-based path planning algorithm with a learning-based algorithm for local planning to allow the UAV to avoid a collision in real time. The global path planning problem is solved in the first stage using a novel anytime incremental search algorithm called improved Anytime Dynamic A* (iADA*). A reinforcement learning method is used to carry out local planning between waypoints, to avoid any obstacles within the environment. The developed hybrid path planning system was investigated and validated in an AirSim environment. A number of different simulations and experiments were performed using AirSim platform in order to demonstrate the effectiveness of the proposed system for an autonomous UAV. This study helps expand the existing research area in designing efficient and safe path planning algorithms for UAVs.

A Hybrid Differential Symbiotic Organisms Search Algorithm for UAV Path Planning.

Unmanned aerial vehicle (UAV) path planning is crucial in UAV mission fulfillment, with the aim of finding a satisfactory path within affordable time and moderate computation resources. The problem is challenging due to the complexity of the flight environment, especially in three-dimensional scenarios with obstacles. To solve the problem, a hybrid differential symbiotic organisms search (HDSOS) algorithm is proposed by combining the mutation strategy of differential evolution (DE) with the modified strategies of symbiotic organism search (SOS). The proposed algorithm preserves the local search capability of SOS, and at the same time has impressive global search ability. The concept of traction function is put forward and used to improve the efficiency. Moreover, a perturbation strategy is adopted to further enhance the robustness of the algorithm. Extensive simulation experiments and comparative study in two-dimensional and three-dimensional scenarios show the superiority of the proposed algorithm compared with particle swarm optimization (PSO), DE, and SOS algorithm.

Three-Dimensional Autonomous Obstacle Avoidance Algorithm for UAV Based on Circular Arc Trajectory

This paper proposes an innovative and efficient three-dimensional (3D) autonomous obstacle algorithm for unmanned aerial vehicles (UAVs) which works by generating circular arc trajectories to avoid obstacles. Firstly, information on irregular obstacles is obtained by an onboard detection system; this information is then transformed into standard convex bodies, which are used to generate circular arc avoidance trajectories, and the obstacle avoidance problem is turned into a trajectory tracking strategy. Then, on the basis of the geometric relationship between a UAV and obstacle modeling, the working mechanism of the avoidance algorithm is developed. The rules of obstacle detection, avoidance direction, and the criterion of avoidance success are defined for different obstacle types. Finally, numerical simulations of different obstacle scenarios show that the proposed algorithm can avoid static and dynamic obstacles effectively and can implement obstacle avoidance missions for UAVs well.

Experimental Heat Loads for Electrothermal Anti-Icing and De-Icing on UAVs

Atmospheric in-flight icing on unmanned aerial vehicles (UAVs) is a significant hazard. UAVs that are not equipped with ice protection systems are usually limited to operations within visual line of sight or to weather conditions without icing risk. As many military and commercial UAV missions require flights beyond visual line of sight and into adverse weather conditions, energy-efficient ice protection systems are required. In this experimental study, two electro-thermal ice protection systems for fixed-wing UAVs were tested. One system was operated in anti-icing and de-icing mode, and the other system was designed as a parting strip de-icing system. Experiments were conducted in an icing wind tunnel facility for varying icing conditions at low Reynolds numbers. A parametric study over the ice shedding time was used to identify the most energy-efficient operation mode. The results showed that longer intercycle durations led to higher efficiencies and that de-icing with a parting strip was superior compared to anti-icing and de-icing without a parting strip. These findings are relevant for the development of energy-efficient systems in the future.

Development of a Vision-Based Recognition and Position Measurement System for Cooperative Missions of Multiple Heterogeneous Unmanned Vehicles

Research on cooperation missions of multiple unmanned vehicles (UVs) is growing to overcome the limitations of conventional missions and to increase the variety of missions. In this study, a vision-based recognition and position measurement system was developed to cooperate with an unmanned aerial vehicle (UAV) in case an unmanned ground vehicle (UGV) is unable to drive due to terrain obstacles. Phase, mode, and requirements were derived to develop the vision system. After installing the developed vision system on UAV, several flight tests were conducted, which proved that the system satisfies all requirements. The vision system showed an average measurement error within 1 m for 80 measurements. It is expected that the vision-based system developed in this study can be applied for the cooperative mission of UAVs and UGVs.

An Industrial Quadrotor UAV Control Method Based on Fuzzy Adaptive Linear Active Disturbance Rejection Control

In this paper, a fuzzy adaptive linear active disturbance rejection control (Fuzzy-LADRC) is proposed for strong coupling and nonlinear quadrotor unmanned aerial vehicle (UAV). At present, UAV conveys new opportunities in the industry, such as power line inspection, petroleum conduit patrolling, and defects detection for the wind turbine, because of its advantages in flexibility, high efficiency, and economy. Usually, the scene of the UAV mission has a high risk, and there are internal sensor noise and unknown external disturbance. Thus, the attitude stability and anti-interference ability of UAV are especially essential. To solve the strong coupling problem of UAV, the dynamics model of UAV is established via the Newton-Euler method, and the coupling part of dynamics is modeled as an internal disturbance. According to the function of linear active disturbance rejection control (LADRC) parameters, a Fuzzy-LADRC is proposed to improve the dynamic performance of the system. The proposed control method makes full use of the adaptive ability of the fuzzy controller and the anti-interference ability of LADRC to the nonlinear and strong coupling systems. As we know, this is the first time that Fuzzy-LADRC has been used in UAV control. In the simulation, the performance indicators of four controllers, including Fuzzy-LADRC, LADRC, PID, and Fuzzy-PID are compared and analyzed. The results indicate that the average response speed of Fuzzy-LADRC is 12.65% faster than LADRC, and it is 29.25% faster than PID. The average overshoot of Fuzzy-LADRC is 17% less than LADRC and 77.75% less than PID. The proposed control method can significantly improve the response speed and anti-interference ability of UAV.

Multi-Purpose Drones for Coverage and Transport Applications

Unmanned aerial vehicles (UAVs) have become important in many applications including last-mile deliveries, surveillance and monitoring, and wireless networks. This paper aims to design UAV trajectories that simultaneously perform multiple tasks. We aim to design UAV trajectories that efficiently perform some transportation operation (e.g., package delivery), and at the same time provide uniform coverage over a neighborhood area which is needed for applications such as network coverage, Internet of Things (IoT) devices data collection, wireless power transfer, and surveillance. We first consider multi-task UAVs for a simplified scenario where the neighborhood area is a circular region where UAV missions start from the center and the destinations are assumed to be uniformly distributed on the circle boundary. We propose a trajectory process such that if according to which the UAV's move, a uniform coverage can be achieved while the transport (delivery) efficiency is still preserved. We then consider a more practical scenario in which the transport destinations are arbitrarily distributed in an arbitrarily-shaped region. We show that simultaneous uniform coverage and efficient transport trajectory (e.g. package delivery) is possible for such realistic scenarios. This is shown using both rigorous analysis as well as simulations.

An efficient trajectory planning for cellular-connected UAV under the connectivity constraint

Unmanned Aerial Vehicles (UAVs) acting as aerial users to access the cellular network form a promising solution to guarantee its safe and efficient operations via the high-quality communication. Due to the flexible mobility of UAVs and the coverage range limits of ground base station (GBS), the signal-to-noise ratio (SNR) of the communication link between UAVs and GBS will fluctuate. It is an important requirement to maintain the UAV's cellular connection to meet a certain SNR requirement during the mission for UAV flying from take off to landing. In this paper, we study an efficient trajectory planning method that can minimize a cellular-connected UAV's mission completion time under the connectivity requirement. The conventional method to tackle this problem adopts graph theory or a dynamic programming method to optimize the trajectory, which generally incurs high computational complexities. Moreover, there is a non-negligible performance gap compared to the optimal solution. To this end, we propose an iterative trajectory optimizing algorithm based on geometric planning. Firstly, we apply graph theory to obtain all the possible UAV-GBS association sequences and select the candidate association sequences based on the topological relationship among UAV and GBSs. Next, adopting the triangle inequality property, an iterative handover location design is proposed to determine the shortest flight trajectory with fast convergence and low computation complexity. Then, the best flight trajectory can be obtained by comparing all the candidate trajectories. Lastly, we revealed the tradeoff between mission completion time and flight energy consumption. Numerical results validate that our proposed solution can obtain the effectiveness with set accuracy and outperform against the benchmark schemes with affordable computation time.

Relevant experience learning: A deep reinforcement learning method for UAV autonomous motion planning in complex unknown environments

Unmanned Aerial Vehicles (UAVs) play a vital role in military warfare. In a variety of battlefield mission scenarios, UAVs are required to safely fly to designated locations without human intervention. Therefore, finding a suitable method to solve the UAV Autonomous Motion Planning (AMP) problem can improve the success rate of UAV missions to a certain extent. In recent years, many studies have used Deep Reinforcement Learning (DRL) methods to address the AMP problem and have achieved good results. From the perspective of sampling, this paper designs a sampling method with double-screening, combines it with the Deep Deterministic Policy Gradient (DDPG) algorithm, and proposes the Relevant Experience Learning-DDPG (REL-DDPG) algorithm. The REL-DDPG algorithm uses a Prioritized Experience Replay (PER) mechanism to break the correlation of continuous experiences in the experience pool, finds the experiences most similar to the current state to learn according to the theory in human education, and expands the influence of the learning process on action selection at the current state. All experiments are applied in a complex unknown simulation environment constructed based on the parameters of a real UAV. The training experiments show that REL-DDPG improves the convergence speed and the convergence result compared to the state-of-the-art DDPG algorithm, while the testing experiments show the applicability of the algorithm and investigate the performance under different parameter conditions.

Detecting Sorghum Plant and Head Features from Multispectral UAV Imagery.

In plant breeding, unmanned aerial vehicles (UAVs) carrying multispectral cameras have demonstrated increasing utility for high-throughput phenotyping (HTP) to aid the interpretation of genotype and environment effects on morphological, biochemical, and physiological traits. A key constraint remains the reduced resolution and quality extracted from “stitched” mosaics generated from UAV missions across large areas. This can be addressed by generating high-quality reflectance data from a single nadir image per plot. In this study, a pipeline was developed to derive reflectance data from raw multispectral UAV images that preserve the original high spatial and spectral resolutions and to use these for phenotyping applications. Sequential steps involved (i) imagery calibration, (ii) spectral band alignment, (iii) backward calculation, (iv) plot segmentation, and (v) application. Each step was designed and optimised to estimate the number of plants and count sorghum heads within each breeding plot. Using a derived nadir image of each plot, the coefficients of determination were 0.90 and 0.86 for estimates of the number of sorghum plants and heads, respectively. Furthermore, the reflectance information acquired from the different spectral bands showed appreciably high discriminative ability for sorghum head colours (i.e., red and white). Deployment of this pipeline allowed accurate segmentation of crop organs at the canopy level across many diverse field plots with minimal training needed from machine learning approaches.