Unmanned Aerial Vehicle Mission Research Articles

Perception-Aware Tag Placement Planning for Robust Localization of UAVs in Indoor Construction Environments

Tag-based visual-inertial localization is a lightweight method for enabling autonomous data collection missions of low-cost unmanned aerial vehicles (UAVs) in indoor construction environments. However, finding the optimal tag configuration (i.e., number, size, and location) on dynamic construction sites remains challenging. This work proposes a perception-aware genetic algorithm-based tag placement planner (PGA-TaPP) to determine the optimal tag configuration using four-dimensional (4D) building information models (BIM), considering the project progress, safety requirements, and UAV’s localizability. The proposed method provides a 4D plan for tag placement by maximizing the localizability in user-specified regions of interest (ROIs) while limiting the installation costs. Localizability is quantified using the Fisher information matrix (FIM) and encapsulated in navigable grids. The experimental results show the effectiveness of our method in finding an optimal 4D tag placement plan for the robust localization of UAVs on under-construction indoor sites.

A KG-Based Integrated UAV Approach for Engineering Semantic Trajectories in the Cultural Heritage Documentation Domain

Data recordings of the movement of vehicles can be enriched with heterogeneous and multimodal data beyond latitude, longitude, and timestamp and enhanced with complementary segmentations, constituting a semantic trajectory. Semantic Web (SW) technologies have been extensively used for the semantic integration of heterogeneous and multimodal movement-related data, and for the effective modeling of semantic trajectories, in several domains. In this paper, we present an integrated solution for the engineering of cultural heritage semantic trajectories generated from unmanned aerial vehicles (UAVs) and represented as knowledge graphs (KGs). Particularly, this work is motivated by, and evaluated based on, the application domain of UAV missions for documenting regions/points of cultural heritage interest. In this context, this research work extends our previous work on UAV semantic trajectories, contributing (a) an updated methodology for the engineering of semantic trajectories as KGs (STaKG), (b) an implemented toolset for the management of KG-based semantic trajectories, (c) a refined ontology for the representation of knowledge related to UAV semantic trajectories and to cultural heritage documentation, and (d) the application and evaluation of the proposed methodology, the developed toolset, and the ontology within the domain of UAV-based cultural heritage documentation. The evaluation of the integrated UAV solution was achieved by exploiting real datasets collected during three UAV missions to document sites of cultural interest in Lesvos, Greece, i.e., the UNESCO-protected petrified forest of Lesvos Petrified Forest/Geopark, the village of Vrissa, and University Hill.

Path Planning of Multiple Unmanned Aerial Vehicles Covering Multiple Regions Based on Minimum Consumption Ratio

Investigating the path planning of multiple unmanned aerial vehicles (UAVs) covering multiple regions, this work proposes an effective heuristic method of region coverage path planning to reduce the complexity of the problem. The proposed method decomposes the solution process into two stages. First of all, the two most important parameters affecting the performance of UAV missions were considered, namely, the flying speed and the scan width. According to these two parameters of UAVs, a new multi-regional allocation scheme based on the minimum consumption ratio was proposed. With this allocation scheme, the coverage task allocation and path pre-planning of UAVs were obtained. Then, the UAVs’ trajectory routes were optimized based on the dynamic planning algorithm to reduce the time consumption of UAVs on the transfer path between regions. The method was evaluated with numerical experiments. The results showed that the proposed method can effectively solve the path planning problem of multiple UAVs covering multiple regions. Compared with an advanced algorithm, the time consumption for homogeneous and heterogenous UAV performance was reduced by 5.1% and 3%, respectively.

Integrating unmanned and manned UAVs data network based on combined Bayesian belief network and multi-objective reinforcement learning algorithm

This paper presents and assesses the feasibility and potential of a novel concept: the operation of multiple Unmanned Aerial Vehicles (UAVs) commanded and supported by a manned “Tender” air vehicle carrying a pilot and flight manager(s). The “Tender” is equipped to flexibly and economically monitor and manage multiple diverse UAVs over otherwise inaccessible terrain through wireless communication. The proposed architecture enables operations and analysis supported by the means to detect, assess, and accommodate change and hazards on the spot with effective human observation and coordination. Further, this paper seeks to find the optimal trajectories for UAVs to collect data from sensors in a predefined continuous space. We formulate the path-planning problem for a cooperative, and a diverse swarm of UAVs tasked with optimizing multiple objectives simultaneously with the goal of maximizing accumulated data within a given flight time within cloud data processing constraints as well as minimizing the probable imposed risk during UAVs mission. The risk assessment model determines risk indicators using an integrated Specific Operation Risk Assessment—Bayesian belief network approach, while its resultant analysis is weighted through the analytic hierarchy process ranking model. To this end, as the problem is formulated as a convex optimization model, and we propose a low complexity multi-objective reinforcement learning (MORL) algorithm with a provable performance guarantee to solve the problem efficiently. We show that the MORL architecture can be successfully trained and allows each UAV to map each observation of the network state to an action to make optimal movement decisions. This proposed network architecture enables the UAVs to balance multiple objectives. Estimated MSE measures show that the algorithm produced decreasing errors in the learning process with increasing epoch number.

Communication Delay in UAV Missions: A Controller Gain Analysis to Improve Flight Stability

In real-world applications involving unmanned aerial vehicles (UAVs) the presence of communication delays can deteriorate the performance of flight control system or even cause instabilities. However, it is possible to improve the performance and ensure flight stability in the task execution by properly controlling the UAV considering the transport delay. This work analyzes the asymptotic convergence of a quadrotor, under time-delay in the communication with a ground control station. The effects of the communication delay, as well as the response-signal behavior of the quadrotors in the accomplishment of positioning missions are presented and analyzed by numerical simulations. The performance indexes (IAE and ITAE) assist the estimation of the acceptable time-delay limit. The results show that the adopted controller, without any adaptive tuning, can handle a delay of up to 1.2 seconds, which means a blind time of 40 packets of information. As expected, the longer the delay, the lower the gains. Consequently, as the delay increases, the quadrotor takes longer to accomplish the mission carefully and successfully.

MEC-Assisted Real-Time Data Acquisition and Processing for UAV With General Missions

As a promising technology, unmanned aerial vehicles (UAVs)-enabled information gathering has received intensive interest in both academia and industry in recent years. However, the familiar paradigm, namely, data collection can hardly fulfill the active and real-time information gathering demands of many thriving smart applications, such as smart grid, where the UAV needs to serve as an intelligent aerial entity, gathering and analysing the information of surroundings actively and timely. To this aim, this article studies the paradigm <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">data acquisition</i> , where the UAV dynamically gathers information by on-board sensors. We consider a general mission scenario where a UAV acquires data in real time, which needs to be timely processed with the assistance of a mobile edge computing server. To address the real-time characteristics, we construct a novel theoretical <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">data acquisition rate</i> model, based on which the real-time processing quality of requirement (QoR) is defined. We respectively minimize the UAV energy consumption and mission completion time, under the constraints including real-time processing QoR and UAV mobility, by jointly optimizing the UAV trajectory, resources allocation, and time duration. To tackle these highly complex problems, we propose efficient algorithms by means of successive convex approximation and block coordinates descent techniques, where a closed-form solution for the UAV transmission and computation resources allocation is rigorously derived. Moreover, we consider a practical multi-location trajectory optimization where the UAV needs to traverse multiple target locations or areas in addition to the initial and final locations. The simulation results demonstrate the significant system performance gains brought by the proposed design and the effectiveness of the algorithms.

Cybersecurity in Unmanned Aerial Vehicles: a Review

Abstract Context With the rapid advancement of unmanned aerial vehicle (UAV) technology, ensuring these autonomous systems’ security and integrity is paramount. UAVs are susceptible to cyberattacks, including unauthorized access, control, or manipulation of their systems, leading to potential safety risks or unauthorized data retrieval. Moreover, UAVs encounter limited computing resources, wireless communication and physical vulnerabilities, evolving threats and techniques, necessity for compliance with regulations, and human factors. Methods This review explores the potential cyberthreats faced by UAVs, including hacking, spoofing, and data breaches, and highlights the critical need for robust security measures. It examines various strategies and techniques used to protect UAVs from cyberattacks, e.g., encryption, authentication, and intrusion detection systems using cyberthreat analysis and assessment algorithms. The approach to assess the UAVs’ cybersecurity hazards included STRIDE (a model for identifying computer security-related threats) connected with the threats considered. Findings Emphasis was laid on the evaluation highly depending on the accuracy of UAV mission definition, potential intruders, and social and other human-related situations. The review discovered that most studies focused on possible intruders’ portraits, which can be crucial when conducting a cybersecurity assessment. Based on a review, future research directions to mitigate cybersecurity risks are presented. Significance Protecting UAVs from cyberthreats ensures safe operations and data integrity and preserves public trust in autonomous systems.

Connectivity Guarantee Within UAV Cluster: A Graph Coalition Formation Game Approach

Unmanned Aerial Vehicle (UAV) clustering is promising for performing large-scale missions because of high mobility and easy deployment. However, the connectivity problems caused by the high mobility of UAVs and the interferences are mostly ignored in existing UAV mission executions. Rapid restoration of UAV network connectivity is essential for preventing communication disruption and improving the overall network performance. Thus, a graph coalition formation game that integrates the UAV time-varying topology graph with the coalition formation game is proposed to rapidly restore the connectivity of the UAV network in need. The method improves the utility of UAV clustering and ensures the network connectivity. Then, a graph coalition formation game algorithm based on the shortest path tree (SPT-GCF) rapidly forms an approximately optimal coalition structure. The simulation results show that the proposed approach increases the average utility of UAV clustering by 6.5% and 14.5% respectively when compared with the existing non-overlapping coalition formation game (NOCFG) and the coalition formation game without considering the cluster connectivity.

Phoenix: Aerial Monitoring for Fighting Wildfires

Wildfires have become a global problem in recent years. Authorities are looking for various technological and auxiliary solutions to deal with this environmental crisis. One of the advances being utilized in the forest fire field and its aftermath is unmanned aerial vehicles (UAVs). UAVs play a fundamental role in wildfire-fighting solutions due to their ease of use and high accessibility. However, the energy constraints of a single UAV and the fire areas make monitoring challenging. Therefore, to address these issues, we propose a monitoring application called Phoenix. We make three main contributions with the Phoenix application. Firstly, we implement a monitoring application consisting of path planning, graph engine, and modified TSP algorithms to help the UAV’s fire tracking and shorten its route. Secondly, we develop a network architecture to transfer the tracking data we obtained to provide information to the fire brigade and other firefighting units. Thirdly, we provide energy optimization for a single UAV mission. The first part of the application uses the elliptical fire model and simulation. In addition, Phoenix utilizes fuel moisture content (fmc) data of the fire zone to analyze the critical fire regions. The simulation results show that our Phoenix application reduces energy consumption by 38 % and enhances coverage by up to 51%.

UAV4PE: An Open-Source Framework to Plan UAV Autonomous Missions for Planetary Exploration

Autonomous Unmanned Aerial Vehicles (UAV) for planetary exploration missions require increased onboard mission-planning and decision-making capabilities to access full operational potential in remote environments (e.g., Antarctica, Mars or Titan). However, the uncertainty introduced by the environment and the limitation of available sensors has presented challenges for planning such missions. Partially Observable Markov Decision Processes (POMDPs) are commonly used to enable decision-making and mission-planning processes that account for environmental, perceptional (extrinsic) and actuation (intrinsics) uncertainty. Here, we propose the UAV4PE framework, a testing framework for autonomous UAV missions using POMDP formulations. This framework integrates modular components for simulation, emulation, UAV guidance, navigation and mission planning. State-of-the-art tools such as python, C++, ROS, PX4 and JuliaPOMDP are employed by the framework, and we used python data-science libraries for the analysis of the experimental results. The source code and the experiment data are included in the UAV4PE framework. The POMDP formulation proposed here was able to plan and command a UAV-based planetary exploration mission in simulation, emulation and real-world experiments. The experiments evaluated key indicators such as the mission success rate, the surface area explored and the number of commands (actions) executed. We also discuss future work aimed at improving the UAV4PE framework, and the autonomous UAV mission planning formulation for planetary exploration.

Optimization of power system for UAV ground control station in Natuna Island

The Unmanned Aerial Vehicle (UAV) is controlled remotely by the operator at the Ground Control Station (GCS) during the mission. Reliable electrical energy is needed to supply energy needs in GCS, such as radio communications, workstations, weather stations, HVAC systems, and lamps when the UAV is operating. GCS is located in Ranai City, Natuna Islands. The UAV mission in Ranai can assist the military in monitoring border surveillance, disaster management, and mapping the area. GCS power source in the initial design, using a diesel generator as the main power source with a power of 12 kW for 192.3 kWh per day energy consumption and 10.8 kW load peak. This paper has shown the study on a hybrid system with 3 scenarios, consisting of a PV system, wind turbine, generator, and battery. The system is simulated by using HOMER for accounting the technical and economic aspects. The results showed that the NPC and LCOE of the third scenario, which consisted of PV/wind/battery, were $181,157.38 and $0.15 per kWh, respectively. It has the lowest cost compared to the first scenario with the usage of diesel generator only. The average fuel consumption is 7.97 litre per day, down 87% compared to the existing condition. Thus, this configuration can overcome the costs of delivering fuel supplies for GCS operations and complement the government’s efforts to achieve emission reduction targets.

Bayes risk-based mission planning of Unmanned Aerial Vehicles for autonomous damage inspection

Unmanned Aerial Vehicle (UAV)-based autonomous damage inspection has become a promising technique to replace certain human-performed inspections due to both safety and cost considerations. While current UAV-based damage inspection methods have been successful in minimizing the length of the flying path considering potential defect locations, the impact of flying path on the quality of structural health monitoring (SHM) has been overlooked. This paper proposes a novel Bayes risk-based mission planning method for UAV-based damage inspection by minimizing not only the UAV path length, but also the associated SHM costs. A functional relationship is first established between the UAV flying path length and damage inspection parameters, such as overlap ratio and inspection distance between the UAV and the target structure. The impact of UAV inspection parameters on associated SHM costs is then analyzed based on Bayes risk. Building upon the formulated functions, a multi-objective optimization model is developed to optimize the inspection parameters and thereby achieve a tradeoff between path length of UAV flight and associated SHM costs (e.g., consequence costs of SHM decisions resulting from the UAV inspection). An example of damage detection on a miter gate is employed to demonstrate the proposed method. The effect of different weighting factors on flying path and SHM costs is also evaluated. The results show that the proposed approach can effectively perform UAV mission planning while accounting for the impact of UAV flying path on SHM costs.

Modeling and Simulation of Flight Profile and Power Spectrum for Near-Space Solar-Powered UAV

Currently, several solar-powered unmanned aerial vehicles (UAVs) have achieved 24 h uninterrupted cruise. However, models that can cruise for weeks or even months without interruption are in the minority. The technological progress requires the improvement of subsystems and also depends on the accurate planning of flight profile and power spectrum in a long working cycle. Combined with the test data obtained during the development of a solar-powered UAV, this paper establishes systematic mathematical and physical models of aerodynamic, energy, and propulsion systems, which can reflect the change in performance parameters with flight conditions and the performance attenuation with time. Further, a track control strategy based on the principle of maximum energy utilization is proposed, and the energy balance model of each flight stage is established. On the basis of the strategy, the typical flight profile and power spectrum of a solar-powered UAV are analyzed. Finally, the input parameters are decomposed into task parameters (takeoff time window, flight season, flight latitude, takeoff weight) and performance parameters (lift–drag ratio, secondary battery density), and their effects on mission feasibility are studied respectively. The research methods and conclusions of this paper have reference significance for the mission and track planning of solar-powered UAVs.

Optimal path planning of Unmanned Aerial Vehicles (UAVs) for targets touring: Geometric and arc parameterization approaches.

The path planning problem for unmanned aerial vehicles (UAVs) is important for scheduling the UAV missions. This paper presents an optimal path planning model for UAV to control its direction during target touring, where UAV and target are at the same altitude. Geometric interpretation of the given model is provided when the vehicles consider connecting an initial position to the destination position with specific target touring. We develop a nonlinear constrained model based on an arc parameterization approach to determine the UAV's optimal path touring a target. The model is then extended to touring finite numbers of targets and optimizing the routes. The model is found reliable through several simulations. Numerical experiments are conducted and we have shown that the UAV's generated path satisfies vehicle dynamics constraints, tours the targets, and arrives at its destination.

The effectiveness of uncertainty communication with varied automation reliability: Specificity is necessary but (potentially) not enough

Automation reliance and functionality are ever increasing, especially in supervisory control environments like unmanned aerial vehicle (UAV) missions. Of particular relevance is understanding how automation transparency, i.e., explaining the capabilities and limitations of automation to the human in real-time, can improve human-automation performance across automated systems that vary in reliability. Two hundred seventy one Naval Aviation trainees completed a simulated multi-UAV supervisory control mission for 42 minutes with three automated systems that varied in reliability. Participants were never explicitly told the reliability varied, but halfway through the mission, they were alerted that the least reliable system may falter. Results indicated human-automation performance improved after the alert for this specific system, but not as a whole, as one system’s human-automation performance deteriorated. This work suggests uncertainty communication should not only include the specific, real-time capabilities of the automation, but also communicate unintentional consequences it may have on the whole environment.

Model-Based Development of Multirotor UAV Power Profiles for Performance Investigation of Different Flight Missions

In this study, a model is developed for a comparative assessment of three flight missions of multirotor unmanned aerial vehicles (UAVs) based on their power profiles in order to identify future technical research priorities and possible improvements in operational management. The model describes the missions (transport, facade inspection, drone show) based on the maneuver-specific parameters for hovering flight, vertical flight, and horizontal flight and calculates the respective power profiles of the missions based on the input parameters of a reference UAV with a battery-powered electric motor. The power profiles of the investigated missions show that the maximum powers occur during accelerated climb in each case. The greatest difference in maximum power occurs between the transport mission and the facade inspection. Considering the small difference in the respective payload, the significantly higher maximum power during the transport mission can mainly be attributed to the higher acceleration assumed for this mission. Consequently, the necessary power can be influenced by the choice of maximum acceleration. This also applies to the drone show, but the difference in power cannot only be linked to the acceleration since for the considered mission the mass differs significantly because no payload is needed. Concerning the different maneuvers, horizontal flight with constant flight speed has the largest time share and the largest energy requirement in all missions. Accordingly, this is where the greatest potential for energy savings is seen. It is shown that an ideal speed for minimizing the energy demand in horizontal flight results for each total mass.

An Edge-Based Architecture for Offloading Model Predictive Control for UAVs

Thanks to the development of 5G networks, edge computing has gained popularity in several areas of technology in which the needs for high computational power and low time delays are essential. These requirements are indispensable in the field of robotics, especially when we are thinking in terms of real-time autonomous missions in mobile robots. Edge computing will provide the necessary resources in terms of computation and storage, while 5G technologies will provide minimal latency. High computational capacity is crucial in autonomous missions, especially for cases in which we are using computationally demanding high-level algorithms. In the case of Unmanned Aerial Vehicles (UAVs), the onboard processors usually have limited computational capabilities; therefore, it is necessary to offload some of these tasks to the cloud or edge, depending on the time criticality of the application. Especially in the case of UAVs, the requirement to have large payloads to cover the computational needs conflicts with other payload requirements, reducing the overall flying time and hindering autonomous operations from a regulatory perspective. In this article, we propose an edge-based architecture for autonomous UAV missions in which we offload the high-level control task of the UAV’s trajectory to the edge in order to take advantage of the available resources and push the Model Predictive Controller (MPC) to its limits. Additionally, we use Kubernetes to orchestrate our application, which runs on the edge and presents multiple experimental results that prove the efficacy of the proposed novel scheme.

The Development of Air Escort Tactics for High-Value Airborne Assets Using Manned-Unmanned Teaming and the Study on Effective Force Disposition Using M&amp;amp;S

As the role of high-value air assets(e.g., AWACS, JSTARS, Rivet Joint, E-2) becomes more critical in modern warfare, the air escort for these assets blocking attacks from any potential enemy fighter also becomes vital. Without the escort, the operations of the assets become restricted. However, such an escort is not always possible due to the limited flight time of the escort fighters. In this paper, we introduce an escort tactics for high-value air assets performed by the manned-unmanned teaming composed of a transport aircraft and UAVs(unmanned aerial vehicles). In this tactics, the transport aircraft plays the role of an aircraft carrier, which carries, launches, and retrieves the UAVs. The missions of UAVs in this tactics are to detect and engage enemy fighters. We also introduce the simulation result of this tactics to identify the UAVs' required capabilities and optimal maneuvering.

The Missing Link of UTM

Abstract As the use cases for rural unmanned aerial vehicle (UAV) missions are evolving, the next step is to make on-site UAV missions controlled over long distances. The control is called Beyond visual line of sight (BVLOS) and there is a great need for use cases to become autonomous or self-sustaining over long distances. Designing airspace to include autonomous and BVLOS UAV involves proposals for traffic that do not exist to date, different actors, and a multitude of risks; system design must answer a wide range of questions before its release. Luckily, most of the technical questions are already solved, most of the safety regulations are there in place in general aviation, and the industry is waiting for a new type of transportation. In this paper, the authors propose action for policymakers on how to approach the challenge of developing UAV Traffic Management (UTM) systems that can be applied to any geographic location and identify missing links in decision-making to enable development.

Automated extraction and validation of Stone Pine (Pinus pinea L.) trees from UAV-based digital surface models

ABSTRACT Stone Pine (Pinus pinea L.) is currently the pine species with the highest commercial value with edible seeds. In this respect, this study introduces a new methodology for extracting Stone Pine trees from Digital Surface Models (DSMs) generated through an Unmanned Aerial Vehicle (UAV) mission. We developed a novel enhanced probability map of local maxima that facilitates the computation of the orientation symmetry by means of new probabilistic local minima information. Four test sites are used to evaluate our automated framework within one of the most important Stone Pine forest areas in Antalya, Turkey. A Hand-held Mobile Laser Scanner (HMLS) was utilized to collect the reference point cloud dataset. Our findings confirm that the proposed methodology, which uses a single DSM as an input, secures overall pixel-based and object-based F 1-scores of 88.3% and 97.7%, respectively. The overall median Euclidean distance revealed between the automatically extracted stem locations and the manually extracted ones is computed to be 36 cm (less than 4 pixels), demonstrating the effectiveness and robustness of the proposed methodology. Finally, the comparison with the state-of-the-art reveals that the outcomes of the proposed methodology outperform the results of six previous studies in this context.