Capabilities Of Unmanned Aerial Vehicles Research Articles

Local-bearing-information-based unmanned aerial vehicle collision avoidance trajectory planning

Due to its low cost, low power, and non-cooperative advantage, monocular vision has become a crucial solution for enhancing the Sense and Avoid capability of unmanned aerial vehicles (UAVs). However, due to the restricted optical measurement properties of monocular vision, there are drawbacks to monocular-vision-based obstacle perception; namely, the capability for obstacle range information perception and global obstacle perception is insufficient. In this paper, a local-bearing-angle-based trajectory planning algorithm is proposed for UAV collision avoidance. First, the observability of monocular-vision-based obstacle range perception is theoretically analyzed. The local coordinate system is selected for collision avoidance, and the bearing angle is selected as the observation. Second, the problem of UAV monocular vision collision avoidance trajectory planning is mathematically formulated. The quadratic Bezier curve is selected to model the trajectory, and the trajectory planning problem is transformed into optimization based on a single objective function. The collision avoidance constraint based on the relative bearing angle rate is designed, and the collision avoidance trajectory segment is optimized on the basis of the bearing angle observed within a limited time period. Finally, the receding horizon is adopted to optimize the trajectory sequence, and the sequences are connected to formulate the near-globally optimal trajectory. Simulation experiments reveal that the collision avoidance trajectory planning method is capable of avoiding both static and dynamic obstacles. Compared with global-information-based collision avoidance, the convergence time of the method proposed in this paper is reduced, and the near-globally optimal trajectory can be acquired in real time, which is in accordance with the perception capability of monocular vision.

Maneuvering target tracking of UAV based on MN-DDPG and transfer learning

Tracking maneuvering target in real time autonomously and accurately in an uncertain environment is one of the challenging missions for unmanned aerial vehicles (UAVs). In this paper, aiming to address the control problem of maneuvering target tracking and obstacle avoidance, an online path planning approach for UAV is developed based on deep reinforcement learning. Through end-to-end learning powered by neural networks, the proposed approach can achieve the perception of the environment and continuous motion output control. This proposed approach includes: (1) A deep deterministic policy gradient (DDPG)-based control framework to provide learning and autonomous decision-making capability for UAVs; (2) An improved method named MN-DDPG for introducing a type of mixed noises to assist UAV with exploring stochastic strategies for online optimal planning; and (3) An algorithm of task-decomposition and pre-training for efficient transfer learning to improve the generalization capability of UAV’s control model built based on MN-DDPG. The experimental simulation results have verified that the proposed approach can achieve good self-adaptive adjustment of UAV’s flight attitude in the tasks of maneuvering target tracking with a significant improvement in generalization capability and training efficiency of UAV tracking controller in uncertain environments.

Meta-analysis of Unmanned Aerial Vehicle (UAV) Imagery for Agro-environmental Monitoring Using Machine Learning and Statistical Models

Unmanned Aerial Vehicle (UAV) imaging systems have recently gained significant attention from researchers and practitioners as a cost-effective means for agro-environmental applications. In particular, machine learning algorithms have been applied to UAV-based remote sensing data for enhancing the UAV capabilities of various applications. This systematic review was performed on studies through a statistical meta-analysis of UAV applications along with machine learning algorithms in agro-environmental monitoring. For this purpose, a total number of 163 peer-reviewed articles published in 13 high-impact remote sensing journals over the past 20 years were reviewed focusing on several features, including study area, application, sensor type, platform type, and spatial resolution. The meta-analysis revealed that 62% and 38% of the studies applied regression and classification models, respectively. Visible sensor technology was the most frequently used sensor with the highest overall accuracy among classification articles. Regarding regression models, linear regression and random forest were the most frequently applied models in UAV remote sensing imagery processing. Finally, the results of this study confirm that applying machine learning approaches on UAV imagery produces fast and reliable results. Agriculture, forestry, and grassland mapping were found as the top three UAV applications in this review, in 42%, 22%, and 8% of the studies, respectively.

Rate Splitting on Mobile Edge Computing for UAV-Aided IoT Systems

In the Internet of Things (IoT), numerous low complexity and energy constrained devices are employed to collect and transmit data simultaneously, where the unmanned aerial vehicle (UAV) is an efficient means to relay the signals. Considering the limited power and computational capability of UAV, mobile edge computing (MEC) is carried out to enhance the usage of UAV-aided IoT networks. In order to develop robust UAV-aided MEC systems, energy efficient transmission schemes and low-latency computational resource allocation become crucial to cope with the energy limitation of UAV and computing delay of MEC. In this paper, we develop UAV-aided MEC systems, where UAVs collect data from IoT devices and then transmit to MEC-based access points for computation. In order to minimize the energy consumption of a UAV in the centralized and distributed MEC computation modes under the constraints of transmission rate and computational time, respectively, a joint rate splitting problem is formulated to optimally allocate rates for transmission links between two antenna arrays on the UAV. In addition, the altitude of UAV is analyzed and designed. From simulation results, it shows the proposed architecture is able to provide robust and high quality transmission rate for the UAV-aided MEC systems.

Use of UAV-Photogrammetry for Quasi-Vertical Wall Surveying

In this study, an analysis of the capabilities of unmanned aerial vehicle (UAV) photogrammetry to obtain point clouds from areas with a near-vertical inclination was carried out. For this purpose, 18 different combinations were proposed, varying the number of ground control points (GCPs), the adequacy (or not) of the distribution of GCPs, and the orientation of the photographs (nadir and oblique). The results have shown that under certain conditions, the accuracy achieved was similar to those obtained by a terrestrial laser scanner (TLS). For this reason, it is necessary to increase the number of GCPs as much as possible in order to cover a whole study area. In the event that this is not possible, the inclusion of oblique photography ostensibly improves results; therefore, it is always advisable since they also improve the geometric descriptions of break lines or sudden changes in slope. In this sense, UAVs seem to be a more economic substitute compared to TLS for vertical wall surveying.

Static Workspace Optimization of Aerial Cable Towed Robots With Land-Fixed Winches

This article focuses on the static workspace (SW) of aerial cable towed robots (ACTRs) with land-fixed winches and provides an optimization approach to maximize the size of such a workspace. In the structure of the studied robots, land-fixed winches beside the ACTRs, actuated by unmanned aerial vehicles (UAVs), are used to manipulate a platform to reach high-altitude poses and balance platform's interaction force/moment in such poses. Capability of UAVs in choosing and holding different positions and orientations enables the studied robots to adapt their available net wrench set (AW) to various required net wrench sets. In order to find the SW of ACTRs with land-fixed winches, at first, AW of a generic robot for a given arrangement of UAVs is developed analytically. Then, a geometrical approach is provided to find all collision-free arrangements of the UAVs. Based on that, a performance index is derived and optimized to find an optimal collision-free arrangement of the UAVs, which maximizes the magnitude of the force that can be balanced by the platform in any arbitrary direction. Finally, the application of the proposed optimization approach in size maximization of the SW is shown in an example.

Analysis of the Capabilities of UAV’s and the Leading Trends in their Application in the European Cooperation Projects

Abstract The European defense agency (EDA) is the key institution in the European Union in implementing resource security measures for the Common Security and Defense Policy (CSDP). The Agency has set objectives in the field of military resource development, defense research and development, pan-European armaments cooperation projects, strengthening the industrial and technological resources and as a key focus the creation of a common competitive market for military and dual-use products. Significant progress has also been made in the area of unmanned aerial vehicles (Remotely Piloted Aircraft Systems, RPAS). The goal of the project is to introduce unmanned aerial vehicles (UAVs) into the internal airspace. The Agency coordinates the interaction between UAV national operators the European Commission, represented by the Directorate-General for Mobility and Transport and the European UAV Group.

Pemanfaatan Teknologi Unmanned Aerial Vehicle (UAV) untuk Pemetaan Kadaster

Abstract: Geospatial data (on horizontal and vertical positions) play an important role in decision making.Regarding that, the issue of funds, diversity of areas, human resources, and non-updated data become great obstacles. This study aims to examine the technological capabilities of Unmanned Aerial Vehicles (UAVs) or Unmanned Aircraft in acquiring and updating geospatial data, evaluating and inspecting changes in the conversion of agricultural land and the environment, as well as describing orthophoto extraction opportunities in digital services based on multipurpose cadastre. This research used a model combination method (concurrent triangulation). Several alternative requirements were derived to design methods of mapping, processing and controlling the quality of application to the concept of Fit For Purpose Land Administration (FFP-LA), and layering with Geographic Information Systems (GIS). The final product of this system is effective to produce and update land base map, evaluate and inspect changes in the conversion of land functions as well as facilitate digital services. The availability of open source software efficiently extracts and combines terrestrial and photogrammetric mapping products in real time and has the opportunity to realize multipurpose cadastre. The involvement of local human resources is applied to ensure legal certainty in the land registration system as well as to improve and strengthen the management of agrarian resourcesAbstract: Geospatial data (on horizontal and vertical positions) play an important role in decision making.Regarding that, the issue of funds, diversity of areas, human resources, and non-updated data become great obstacles. This study aims to examine the technological capabilities of Unmanned Aerial Vehicles (UAVs) or Unmanned Aircraft in acquiring and updating geospatial data, evaluating and inspecting changes in the conversion of agricultural land and the environment, as well as describing orthophoto extraction opportunities in digital services based on multipurpose cadastre. This research used a model combination method (concurrent triangulation). Several alternative requirements were derived to design methods of mapping, processing and controlling the quality of application to the concept of Fit For Purpose Land Administration (FFP-LA), and layering with Geographic Information Systems (GIS). The final product of this system is effective to produce and update land base map, evaluate and inspect changes in the conversion of land functions as well as facilitate digital services. The availability of open source software efficiently extracts and combines terrestrial and photogrammetric mapping products in real time and has the opportunity to realize multipurpose cadastre. The involvement of local human resources is applied to ensure legal certainty in the land registration system as well as to improve and strengthen the management of agrarian resources

Control algorithms and capabilities of universal UAV

The area of unmanned aerial vehicles (UAV) has been studied for a long time. They are actively used for military purposes. Nowadays, their application in everyday life to solve the problems of ordinary people has become relevant. There are many UAV designs, but the issue of an effective universal UAV design has not been fully addressed. In this paper, it is proposed to consider a variant of the universal UAV design, which differs from existing solutions and requires the development of its own rules for generating a PWM signal, control and stabilization algorithms, and the assessment of technical characteristics and application possibilities. The paper provides a brief description of the main differences of the proposed universal design from other solutions. It is proposed to consider the necessary control algorithms for UAVs of universal design, which were tested on the developed mathematical model of the UAV flight process of universal design. The basic equations of motion of this UAV are presented. The optimal technical characteristics and examples of using the electric version of the UAV of universal design to solve the problem of processing the field when powered by rechargeable batteries or an electric cable are considered.

Cooperative Multiple Task Assignment of Heterogeneous UAVs Using a Modified Genetic Algorithm with Multi-type-gene Chromosome Encoding Strategy

The cooperative multiple task assignment problem (CMTAP) of heterogeneous fixed-wing unmanned aerial vehicles (UAVs) performing the Suppression of Enemy Air Defense (SEAD) mission against multiple ground stationary targets is studied in this paper. The CMTAP is a NP-hard combinatorial optimization problem, which faces many challenges like problem scale, heterogeneity of UAVs (different capability and maneuverability), task coupling and task precedence constraints. To address this issue, we proposed a modified genetic algorithm (GA) with multi-type-gene chromosome encoding strategy. Firstly, the multi-type-gene encoding scheme is raised to generate feasible chromosomes that satisfy the UAV capability, task coupling and task precedence constraints. Then, Dubins car model is adopted to calculate the mission execution time (objective function of CMTAP model) of each chromosome, and make each chromosome conform to the UAV maneuverability constraint. To balance the searching ability of algorithm and the diversity of population, we raise the modified crossover operator and multiple mutation operators according to the multi-type-gene chromosome encoding. The simulation results demonstrate that the modified GA has better optimization performance compared with random search method, ant colony optimization method and particle search optimization method.

A cascade adaboost and CNN algorithm for drogue detection in UAV autonomous aerial refueling

To promote the combat capability of unmanned aerial vehicles (UAVs) in the future battlefield, the autonomous aerial refueling (AAR) technology becomes a challenging research issue. An accurate position relationship between the tanker and the receiver is significant for AAR. A novel drogue detection method is presented in this paper. The Adaptive boosting (Adaboost) and the convolutional neural networks (CNN) classifier with the improved focal loss (IFL) function are utilized to detect the drogue in complex environments. The sample imbalance during the training stage of the CNN classifier is solved by the IFL function. The PyTorch deep learning framework is employed to implement the software system with the graphics processing units (GPUs). Real scenario images with a mimetic drogue on the tanker are captured for training and testing dataset by the airborne camera on the receiver. The experimental results indicate that the presented algorithm can accelerate the detection speed and improve the detection accuracy.

Discrete vector fields for 2-D navigation under minimum turning radius constraints

To greatly extend the capabilities of unmanned aerial vehicles, automation systems for the various piloting functions are being developed. However, the development of these auto-systems is not without challenges. One of these challenges is the ability to prove the robustness of an autonomous system against unpredicted events. For the navigation task, this means to prove that the system can recover safely after a deviation from the original plan. In a decoupled scheme where the planner generates a plan online and the controller executes the plan, it is difficult to prove the safety of a system exhaustively. Conversely, using a feedback motion plan generated off-line, it is possible to pre-verify and pre-approve a large number of cases simultaneously. Therefore, it is easier to prove system safety. In this work, this approach is utilized to integrate the guidance toward a target location or a target path with geofence avoidance and recovery for a fully autonomous fixed-wing aircraft. Our formulation extends the wavefront expansion to the case of vehicles having minimum turning radius constraints. The solution is suitable for both single goal missions and path following problems in presence of geofences that can have both convex and non-convex shape. The main novelties introduced in the proposed method are the followings: (1) path following in presence of obstacles having an arbitrary shape, (2) definition of a transition function for the rotation of the flow around the predefined path, (3) use of a Gaussian filter to smooth the vector field. Simulation and experimental results demonstrate the effectiveness of the proposed method.

UAV-Aided Edge/Fog Computing in Smart IoT Community for Social Augmented Reality

In 5G and beyond, the adequate interaction between densely deployed Internet-of-Things (IoT) devices and cellular users will generate a massive cyber-physical information stream in a real-time manner. How to capture insights underneath these data in a smart city context is gaining great attention nowadays. In this article, we introduce a highly function-differentiated metropolitan scenario, which is covered by multiple unmanned aerial vehicles (UAVs) serving as cache-enabled edge computing nodes. With the help of wireless backhaul technology, coverage capability of UAV can be dynamically configured through smart 3-D placement, where trajectories of two types of UAVs are optimized. A social augmented reality (AR)-based use case is proposed and discussed in the proposed scenario, from which we derive the fundamental mechanisms and strategies to maintain a green and sustainable edge/fog computing framework. We sequentially establish two nonconvex programming problems and optimize delay and energy performance during AR data acquisition and AR content downloading, respectively. Two convex approximation skills are applied to transform the original problems into tractable form. The experimental results show that our proposed edge computing framework can help provide energy-efficient AR service to cellular users, catering to pretty tight delay constraints.

Performance Analysis of Medium Altitude Low-Cost Autonomous Quadcopter

In modern engineering, the rapid growth in the development of unmanned aerial vehicles which deserves the major space in the area of intelligence and surveillance operations. Unlike the conventional aircraft, the number of rotors mounted in an autonomous body that determines the propulsive efficiency as well as the maneuvering capabilities of unmanned aerial vehicles. The main objective of this research reveals that the low cost and an autonomous aerial vehicle with four rotors configuration design to achieve the long endurance flight limit. The developed Quadcopter flying model integrated with Arduino autopilot module which effectively controls the various maneuvering actions during the flight. This paper also reveals the entire hardware structure of the designed Quadcopter and its flight dynamic response characteristics. The remotely operated Quadcopter model was subjected to the flight test and the performance parameters have recorded. The flight test was conducted in an open environment in which stability parameters has been observed during onboard. Pitch angle, the Rotational speed of four rotors and the various attitudes of Quadcopter were changed accordingly to maintain the steady level flight. Findings are plotted with respect to the flight endurance.

Using Ship-Deployed High-Endurance Unmanned Aerial Vehicles for the Study of Ocean Surface and Atmospheric Boundary Layer Processes

Unmanned aerial vehicles (UAVs) are proving to be an important modern sensing platform that supplement the sensing capabilities from platforms such as satellites, aircraft, research vessels, moorings, and gliders. UAVs, like satellites and aircraft can provide a synoptic view of a relatively large area. However, the coarse resolution provided by satellites and the operational limitations of manned aircraft has motivated the development of unmanned systems. UAVs offer unparalleled flexibility of tasking; for example, low altitude flight and slow airspeed allow for the characterization of a wide variety of geophysical phenomena at the ocean surface and in the marine atmospheric boundary layer. Here, we present the development of cutting-edge payload instrumentation for UAVs that provides a new capability for ship-deployed operations to capture a unique, high resolution spatial and temporal variability of the changing air-sea interaction processes than was previously possible. The modular design of the base payload means that new instruments can be incorporated into new research proposals that may include new instruments for expanded use of the payloads as a long-term research facility. Additionally, we implement a novel capability for vertical take-off and landing (VTOL) from research vessels. This VTOL capability is safer and requires less logistical support than previous ship-deployed systems. The payloads developed include thermal infrared, visible broadband and hyperspectral, and near-infrared hyperspectral high-resolution imaging. Additional capabilities include quantification of the longwave and shortwave hemispheric radiation budget (up- and down-welling) as well as direct air-sea turbulent fluxes. Finally, a UAV-deployed dropsonde-microbuoy was developed in order to profile the temperature, pressure and humidity of the atmosphere and the temperature and salinity of the near-surface ocean. These technological advancements provide the next generation of instrumentation capability for UAVs. When deployed from research vessels, UAVs will provide a transformational science prism unequaled using 1-D data snapshots from ships or moorings alone.

SIDR: A Swarm Intelligence-Based Damage-Resilient Mechanism for UAV Swarm Networks

Unmanned Aerial Vehicle (UAV) swarm networks have been presented as a promising paradigm for conducting monitoring, and inter-connecting tasks in unattended or even hostile environments. However, harsh deployment scenario may make the UAVs susceptible to large-scale damage, and thus degrades the connectivity and performance of the network. None of existing technologies can effectively re-organize the surviving UAVs in a severely damaged UAV swarm into a unified UAV Swarm Network (USNET), this paper presents and analyzes the damage-resilience problem of USNETs for the first time, and put forwards a Swarm Intelligence-based Damage-Resilient (SIDR) mechanism. First, a damage model of USNETs and several metrics are defined before the problem is formally formulated. Second, the SIDR mechanism is detailed based on comprehensively utilizing the storage, communication, positioning, and maneuvering capabilities of UAVs. Third, a potential-field-based solution to the proposed SIDR mechanism is presented, aiming to recover a USNET rapidly and elastically. At last, an evaluation environment is built on the OMNeT++ platform, and the proposed SIDR mechanism is implemented. Extensive simulations are conducted in both dynamic and static scenarios. Simulation results demonstrate that SIDR outperforms the existing algorithms in terms of resilience capability, convergence time and communication overhead. Even if a USNET is divided into multiple disjoint subnets with arbitrary shape, SIDR can aggregate the surviving nodes into a connected network while the network is still flying along the flight path during the recovery process.

Maneuver Decision of UAV in Short-Range Air Combat Based on Deep Reinforcement Learning

With the development of artificial intelligence and integrated sensor technologies, unmanned aerial vehicles (UAVs) are more and more applied in the air combats. A bottleneck that constrains the capability of UAVs against manned vehicles is the autonomous maneuver decision, which is a very challenging problem in the short-range air combat undergoing highly dynamic and uncertain maneuvers of enemies. In this paper, an autonomous maneuver decision model is proposed for the UAV short-range air combat based on reinforcement learning, which mainly includes the aircraft motion model, one-to-one short-range air combat evaluation model and the maneuver decision model based on deep Q network (DQN). However, such model includes a high dimensional state and action space which requires huge computation load for DQN training using traditional methods. Then, a phased training method, called “basic-confrontation”, which is based on the idea that human beings gradually learn from simple to complex is proposed to help reduce the training time while getting suboptimal but efficient results. Finally, one-to-one short-range air combats are simulated under different target maneuver policies. Simulation results show that the proposed maneuver decision model and training method can help the UAV achieve autonomous decision in the air combats and obtain an effective decision policy to defeat the opponent.

Infrared and Visible Airborne Targets Image Fusion with Applications to Sense and Avoid

Machine vision has revealed great potential in recent years for Sense and Avoid (SAA) ability of Unmanned Aerial Vehicle (UAV). However, the target perception capability of machine vision largely depends on illumination, which restricts UAV to move safely in dark environment. Since images acquired by infrared and visible sensors are complementary in most cases, enhancing image qualities in dark environments by fusion of infrared and visible images is a promising solution. By considering the difficulties of image fusion for airborne targets, a Convolutional Sparse Representation (CSR) based infrared and visible airborne targets image fusion algorithm is proposed in this paper for enhancing SAA capability of UAV in dark environments, which contains three parts: image decomposition, image transformation and image reconstruction. A series of registered infrared and visible images containing airborne targets are selected to evaluate the algorithm proposed in this paper. Simulation results demonstrate the algorithm proposed in this paper effectively increases image qualities in dark environments. In the aspects of fusion metrics, the algorithm proposed in this paper can achieve favorable performance against other image fusion algorithms.

Conceptual aspects of improving legislation and law enforcement practice in use of robot systems to ensure traffic safety in large cities

The article provides a general description of the legislation for the use of unmanned aerial vehicles (UAVs) in the Russian Federation and some foreign countries. Special attention is paid to the promising directions of UAV use in the process of ensuring traffic safety and regulatory restrictions that prevent the use of UAVs over highways and in the airspace of large cities (megacities). We analyze UAV capabilities in the implementation of the main administrative procedures (actions) to study the circumstances of road traffic accidents (RTAs), as well as gaps in legislation related to the implementation of these procedures. Based on the research, including comparative legal studies, we propose a “road map” for improving legislation aimed at protecting the interests of a person, society, and the state against the negative consequences of RTAs through the use of UAVs.

CORRELATION OF UAV-BASED MULTISPECTRAL VEGETATION INDICES AND LEAF COLOR CHART OBSERVATIONS FOR NITROGEN CONCENTRATION ASSESSMENT ON RICE CROPS

Abstract. Fertilizer application is a crucial farming operation for regulating crop health thus crop yield. Optimal fertilizing doubles agricultural production subsequently raising farmers’ income, food security and economic agriproducts. To optimize the application of fertilizers, initial monitoring of the current nutrient status of the crops is required. This research will focus on Nitrogen (N), the most extensive fertilizer nutrient in crop cultivation. Conventional N monitoring involves the use of Leaf Color Charts (LCC) wherein leaf color intensity is associated with the N content of the crops. Despite its ability to quantify the optimal amount of needed fertilizers, the LCC method requires extensive on-site labor and lacks accuracy. This study developed a method that incorporates capabilities of Unmanned Aerial Vehicles (UAVs) equipped with a multispectral sensor in N monitoring specifically in rice crops, a major agricultural product in the Philippines. In situ N level information collected through LCC was correlated with remote sensing data, particularly vegetation indices (VIs) extracted from UAV multispectral imagery of a rice plantation in San Rafael, Bulacan. Several VIs sensitive to crop N content were tested to determine which has the highest correlation with the LCC data. Through Pearson correlation and regression analysis, NDVIRed Edge was found to be the most strongly correlated with LCC data suggesting its potential in mapping variability in fertilizer requirements. An equation modelling LCC observations and NDVIRed Edge values that estimates the N levels of an entire rice plantation was generated along with the N concentration map of the study area.