Operation Of Unmanned Aerial Vehicles Research Articles

Quadcopters Control Using Online Dynamic Mode Decomposition*

Over the past few years, unmanned aerial vehicles (UAVs) have gained popularity in a wide range of applications such as delivery services, inspection, and monitoring. However, for these applications to be safe and efficient, an accurate model of the UAVs dynamic is essential. In this article, the authors propose a novel control system for the DJI Matrice 100 quadcopter based on the online dynamic mode decomposition with control (DMDc) algorithm and iterative optimal estimation techniques. The online DMDc algorithm enables the identification of the system's dynamic, a critical requirement for an adaptive control. By decomposing the quadcopter's system into dynamic modes, the authors create a precise model of the system's behavior using a Hardware in the Loop (HiL) framework, which is used to design the control system based on inverse dynamics. The adaptive control system can adjust its parameters based on changes in the system's behavior, making it suitable for UAV operations in unpredictable environments. These results demonstrate that the proposed control system significantly improves performance in the presence of disturbances. The research contributes to the field of UAV control and highlights the potential of DMD and adaptive control techniques for enhancing the safety and efficiency of UAV operations.

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.

Electromagnetic Radiation from High-Voltage Transmission Lines: Impact on UAV Flight Safety and Performance

Unmanned aerial vehicles (UAVs) have emerged as versatile tools across various sectors, including power distribution infrastructure. While UAVs offer significant advancements in the time and cost-effectiveness of energy distribution infrastructure inspections, such operations present several challenges. The main challenges include the risk of collision with transmission cables themselves and the high potential for electromagnetic interference caused by high-voltage power lines. This research investigates the strength of the electromagnetic field (EMF) emitted by high-voltage power lines while identifying potential concerns for UAVs operating nearby. The research team conducted a series of experiments to quantify the strength of magnetic, electric, and radiofrequency fields induced by power transmission lines. A thorough understanding of the magnitude and potential effects of EMF from power lines is of great importance when conducting UAV flights in their direct vicinity. This understanding is crucial not only for ensuring the safety of infrastructure inspection flights but also for the safety of all UAV flights carried out near high-voltage lines. Our study contributes to the body of knowledge concerning the influence of electromagnetic radiation from high-voltage transmission lines on UAVs, providing valuable insights into mitigating risks associated with UAV flights in critical infrastructure inspections, as well as other operations.

Effects of flight parameters for plant protection UAV on droplets deposition rate based on a 3D simulation approach

With the development of aviation agricultural technology, the number of farmers adopting the use of drones in daily agricultural activities is growing rapidly in recent decades. Among these, a large portion constitutes agricultural drones being used in pest control and crop protection practices, e.g. agriculture spraying of pesticides. Spraying pesticides with drones have proven to be faster than other traditional methods. On the downside, flight time and range of Unmanned Aerial Vehicles (UAV) are often limited. Thus, a proper arrangement of flight height and velocity will greatly improve spraying efficiency. A new strategy to optimize the flight parameters, i.e. flight height and flight velocity, for fixed-wing UAV with a 3D simulation-based approach together with an automatic optimization algorithm was proposed in this study. To find the optimal parameters for a UAV to fly and spray under certain environmental conditions, a three-dimensional model of the target crop was established first, followed by a detailed simulation of droplet spraying. As a demonstration case, a grass model was developed and used as the target plant, and a physics-based method was used to simulate realistically the movement of the droplets in the air as well as the interaction between the droplets and the plant to obtain the droplet deposition rate under the specified operating parameters. Furthermore, the standard Particle Swarm Optimization (PSO) algorithm was used to optimize the UAV operating parameters to obtain the best operating parameters. The results indicate that using the standard PSO algorithm to optimize the operating parameters of the drone could significantly improve the deposition rate and find the best operating parameters. Keywords: unmanned aerial vehicle, droplet deposition, 3D simulation, particle swarm optimization algorithm DOI: 10.25165/j.ijabe.20231601.6581 Citation: Xu L F, Yang Z Z, Huang Z S, Ding W L, Buck-Sorlin G. Effects of flight parameters for plant protection UAV on droplets deposition rate based on a 3D simulation approach. Int J Agric & Biol Eng, 2023; 16(1): 66–72.

Adaptive Height Optimization for Cellular-Connected UAVs: A Deep Reinforcement Learning Approach

Providing reliable connectivity to cellular-connected Unmanned Aerial Vehicles (UAVs) can be very challenging; their performance highly depends on the nature of the surrounding environment, such as density and heights of the ground Base Stations (BSs). On the other hand, tall buildings might block undesired interference signals from ground BSs, thereby improving the connectivity between the UAVs and their serving BSs. To address the connectivity of UAVs in such environments, this paper proposes a Reinforcement Learning (RL) algorithm to dynamically optimise the height of a UAV as it moves through the environment, with the goal of increasing the throughput or spectrum efficiency that it experiences. The proposed solution is evaluated in two settings: using a series of generated environments where we vary the number of BS and building densities, and in a scenario using real-world data obtained from an experiment in Dublin, Ireland. Results show that our proposed RL-based solution improves UAV Quality of Service (QoS) by 6% to 41%, depending on the scenario. We also conclude that, when flying at heights higher than the buildings, building density variation has no impact on UAV QoS. On the other hand, BS density can negatively impact UAV QoS, with higher numbers of BSs generating more interference and deteriorating UAV performance.

Use of unmanned aerial vehicle (UAV) for mapping, and accuracy assessment of the orthophoto with and without using GCPs: A case study in Nepal

The conventional methods of aerial photogrammetry using helicopters or airplanes are costly and challenging for small areas. For a developing country like Nepal, where Geospatial data is in high demand, a new competitive approach is essential for rapid spatial data acquisition at a low cost and time. This article demonstrates how this can be achieved using one of the evolving remote sensing technology, Unmanned Aerial Vehicles (UAVs). The application of UAVs is rapidly increasing in Nepal due to its capability of acquiring images remotely and the potential to provide data with a very high spatial and temporal resolution even in inaccessible terrain at a relatively low cost. Here, the performance of UAVs for topographical surveying and mapping has been investigated, along with the comparison between orthophoto obtained using GCPs, and without using GCPs. For this study, a DJI Phantom 3 Advanced quadcopter collected about 700 images at a flying height of 50 m above the settlement area. An orthophoto of 3.78 cm GSD covering 40.83 hectares of area was produced. With appropriate ground control points, an absolute positional accuracy of 0.035 m RMSE was achieved, whereas the output obtained without using GCPs was satisfactory. This study also highlights the use of a High-Performance Computing (HPC) system and open-source platform for rapid image processing.

UAV path planning method for data collection of fixed-point equipment in complex forest environment.

In a complicated forest environment, it is usual to install many ground-fixed devices, and patrol personnel periodically collects data from the device to detect forest pests and valuable wild animals. Unlike human patrols, UAV (Unmanned Aerial Vehicles) may collect data from ground-based devices. The existing UAV path planning method for fixed-point devices is usually acceptable for simple UAV flight scenes. However, it is unsuitable for forest patrol. Meanwhile, when collecting data, the UAV should consider the timeliness of the collected data. The paper proposes two-point path planning and multi-point path planning methods to maximize the amount of fresh information collected from ground-fixed devices in a complicated forest environment. Firstly, we adopt chaotic initialization and co-evolutionary algorithmto solve the two-point path planning issue considering all significant UAV performance and environmental factors. Then, a UAV path planning method based on simulated annealing is proposed for the multi-point path planning issue. In the experiment, the paper uses benchmark functions to choose an appropriate parameter configuration for the proposed approach. On simulated simple and complicated maps, we evaluate the effectiveness of the proposed method compared to the existing pathplanning strategies. The results reveal that the proposed ways can effectively produce a UAV patrol path with higher information freshness in fewer iterations and at a lower computing cost, suggesting the practical value of the proposed approach.

Incorporating Artificial Intelligence Technology in Smart Greenhouses: Current State of the Art

This article presents the current state-of-the-art research on applying artificial intelligence (AI) technology in smart greenhouses to optimize crop yields, water, and fertilizer use efficiency, to reduce pest and disease, and to enhance agricultural sustainability. The key technologies of interest were robotic systems for pesticide application, irrigation, harvesting, bio-inspired algorithms for the automation of greenhouse processes, energy management, machine path planning and operation of UAVs (unmanned aerial vehicles), resolution of scheduling problems, and image signal processing for pest and disease diagnosis. Additionally, the review investigated the cost benefits of various energy-management and AI-based energy-saving technologies, the integration of photovoltaics and dynamic pricing based on real-time and time-of-use metrics, and the cost benefits of LoRa, Wi-Fi, Bluetooth, ZigBee, mobile, and RFID (radiofrequency identification) technologies. The review established that commercially viable AI technologies for agriculture had increased exponentially. For example, AI-based irrigation and soil fertilizer application enabled farmers to realize higher returns on investment on fertilizer application and gross returns above the fertilizer cost, higher yields, and resource use efficiency. Similarly, AI image detection techniques led to the early diagnosis of powdery mildew. The precise operation of agricultural robots was supported by the integration of light imaging, detection, and ranging (LIDAR) optical and electro-optical cameras in place of the traditional GPS (geographic positioning systems) technologies, which are prone to errors. However, critical challenges remained unresolved, including cost, disparities between research and development (R&D) innovations and technology commercialization, energy use, the tradeoff between accuracy and computational speeds, and technology gaps between the Global North and South. In general, the value of this review is that it surveys the literature on the maturity level of various AI technologies in smart greenhouses and offers a state-of-the-art picture of how far the technologies have successfully been applied in agriculture and what can be done to optimize their usability.

КОМПЛЕКСНЫЙ ПОКАЗАТЕЛЬ ТАКТИКО-ТЕХНИЧЕСКИХ ХАРАКТЕРИСТИК БЕСПИЛОТНЫХ ЛЕТАТЕЛЬНЫХ АППАРАТОВ

The problem of forming an integrated indicator of tactical and technical characteristics of an unmanned aerial vehicle (UAV) is considered. The theory of weighting means is used to calculate an integrated indicator. Weighting factors are determined by statistical processing of projects. The method of integrated estimation is presented and examples of calculating the integrated indicator of UAV performance characteristics are given. The grouping of models and the clustering of weighting means on base of the ordered rows are illustrated.

Factors Affecting Unmanned Aerial Vehicles’ Unsafe Behaviors and Influence Mechanism Based on Social Network Theory

With the widespread application of unmanned aerial vehicles (UAVs), flight safety issues have gradually become prominent. To improve the safety level of UAV flight, a conceptual model was constructed through groups of unsafe behaviors of UAV flight based on the Swiss cheese model (reason model). The relationship network model of unsafe behaviors of UAV flight was built after using the two-mode and one-mode social network analysis, and the unsafe behaviors of UAV flight influence mechanism were studied by basic characteristics of network analysis, centrality analysis, core-periphery structure analysis, in/out-degree analysis, and structural hole analysis. The results showed that the two-mode network is closely related: unreasonable safety management structure of the organization and weak supervision of UAV flight operation were those unsafe behaviors of UAV supervision that had great influence. The unsafe behaviors of UAV supervision, such as the organization’s illegal deployment of unqualified personnel for tasks and lack of ground commander for the mission plan, were in the core position of the network. The proposed model can effectively reduce the unsafe behaviors of UAV operations by eliminating critical unsafe behaviors of UAV supervision in the network and reducing UAV flight accidents.

Hybrid Optimization Based Multi-Objective Path Planning Framework for Unmanned Aerial Vehicles

Unmanned aerial vehicles (UAVs) have been touted as a cost-effective way to follow sensitive resources across wide areas. The extensive wireless sensor networks that UAVs travel through have allowed them to acquire exceptional amounts of data. The path planning of UAV operations in low-altitude urban environments is the main emphasis of this research. Regarding two objectives, namely journey distance and safety level, a Multi-Objective Path Planning (MOPP) framework is introduced. A unique hybrid optimization approach is used to optimally find the best flight path between adjacent acquisition stations based on these MOPPs. The conceptual fusion of traditional DHOA with WOA results in the suggested hybrid model known as Deer Hunter Updated Whale Optimization (DHUWO). Ultimately, the projected collision-free optimal path framework referred to Multi-Objective Path Planning with DHUWO (MOPP-DHUWO) model is compared over the existing models in terms of safety level and distance as well.

Detecting structural anomalies of quadcopter UAVs based on LSTM autoencoder

Detecting a structural anomaly, such as a damaged propeller or motor, is crucial for mission-critical operation of unmanned aerial vehicles (UAVs). The existing solutions often fail to detect structural anomalies because the pre-defined parameters required for the solution are limited in reflecting the flight pattern or the external environment, such as wind conditions. In this paper, we propose a method for detecting structural anomalies in quadcopter UAVs, using only regular data and specifically considering flight patterns and runtime flight conditions. To this end, we employ a long short-term memory (LSTM) autoencoder model to learn complex features of regular flight data. While flying the UAV, the trained model estimates the degree of outlierness of the incoming data and assesses abnormal behavior of UAV by adaptively considering its movement. This way, the proposed method accurately detects structural anomalies in UAVs regardless of the runtime environment or flight mission. Our experiment results with an off-the-shelf UAV show that the proposed approach detects diverse structural anomalies by an average of 98.6% specificity and 90.3% sensitivity.

Security Threats and Cellular Network Procedures for Unmanned Aircraft Systems: Challenges and Opportunities

Researchers and standardization bodies have raised concerns about using legacy cellular networks for supporting unmanned aerial vehicle (UAV) operations. Different from traditional user equipment (UE), an unmanned aircraft system (UAS)-capable UE - UAV-UE or controller-UE - needs additional network security measures to ensure safe airspace operation. This article introduces the security requirements and threats with respect to three major themes: authentication and authorization, location information veracity and tracking, and command and control signaling. We present the 3GPP reference architecture for network connected UASs, the new application functions of the 5G core network, and the 5G security mechanisms and procedures for meeting the established requirements. Three 5G core application functions supporting UASs facilitate the interworking between the 3GPP network and the UAS traffic management, delivering location reports, validating UAS subscriptions, and matching UAS IDs with their respective UE IDs, among others. We identify opportunities for UAS network security research and recommend critical security features and processes to be considered for standardization. We conclude that while the 5G standard introduces important security mechanisms, more security research and benchmarking are needed for cellular networks to support secure and scalable real-time control of UAVs and the emerging applications enabled by them.

Gemini II: Design, Modeling, and Control of a Compact Yet Efficient Servoless Bi-copter

Unmanned aerial vehicles (UAV) are widely used in the field for tasks that require 3-D movements in space. The simultaneous demand for heavy payload capacity, long operating time, and size restriction poses a challenge to UAV design. To solve this conundrum, this article proposes a novel bi-copter with only two actuators. Unlike tandem rotor bi-copters that utilize two servomotors to achieve yaw and roll control, our novel design, the Gemini II bi-copter, controls attitude by using cyclic flapping response in hinges that connect the blades. This passive cyclic pitch-varying mechanism makes the UAV no longer depend on two heavy and expensive servo motors or swashplate to vector the thrust. This change of propulsion system not only makes the UAV mechanically simpler, more reliable, and cost-effective, but also enhances the UAV performance by mitigating the issues of backlash, nonlinearity, and nonminimum phase caused by servo motors. To the best of the authors’ knowledge, the Gemini II, built entirely on off-the-shelf electronics, is the first servoless bi-copter that can precisely control its 3-D position and orientation with two actuators only. To demonstrate the flight performance and applications of the proposed novel UAV, we conduct path-following experiments along with manual poking and wind disturbances tests.

Independent Control Spraying System for UAV-Based Precise Variable Sprayer: A Review

Pesticides are essential for removing plant pests and sustaining good yields on agricultural land. Excessive use has detrimental repercussions, such as the depletion of soil fertility and the proliferation of immune insect species, such as Nilaparvata lunges and Nezara viridula. Unmanned aerial vehicle (UAV) variable-rate spraying offers a precise and adaptable alternative strategy for overcoming these challenges. This study explores research trends in the application of semi-automatic approaches and land-specific platforms for precision spraying. The employment of an autonomous control system, together with a selection of hardware such as microcontrollers, sensors, pumps, and nozzles, yields the performance necessary to accomplish spraying precision, UAV performance efficacy, and flexibility in meeting plant pesticide requirements. This paper discusses the implications of ongoing and developing research. The comparison of hardware, control system approaches, and data acquisition from the parameters of each study is presented to facilitate future research. Future research is incentivized to continue the precision performance of the variable rate development by combining it with cropland mapping to determine the need for pesticides, although strict limits on the amount of spraying make it difficult to achieve the same, even though the quality is very beneficial.

A Novel Technique Using Planar Area and Ground Shadows Calculated from UAV RGB Imagery to Estimate Pistachio Tree (Pistacia vera L.) Canopy Volume

Interest in pistachios has increased in recent years due to their healthy nutritional profile and high profitability. In pistachio trees, as in other woody crops, the volume of the canopy is a key factor that affects the pistachio crop load, water requirements, and quality. However, canopy/crown monitoring is time-consuming and labor-intensive, as it is traditionally carried out by measuring tree dimensions in the field. Therefore, methods for rapid tree canopy characterization are needed for providing accurate information that can be used for management decisions. The present study focuses on developing a new, fast, and low-cost technique, based on two main steps, for estimating the canopy volume in pistachio trees. The first step is based on adequately planning the UAV (unmanned aerial vehicle) flight according to light conditions and segmenting the RGB (Red, Green, Blue) imagery using machine learning methods. The second step is based on measuring vegetation planar area and ground shadows using two methodological approaches: a pixel-based classification approach and an OBIA (object-based image analysis) approach. The results show statistically significant linear relationships (p < 0.05) between the ground-truth data and the estimated volume of pistachio tree crowns, with R2 > 0.8 (pixel-based classification) and R2 > 0.9 (OBIA). The proposed methodologies show potential benefits for accurately monitoring the vegetation of the trees. Moreover, the method is compatible with other remote sensing techniques, usually performed at solar noon, so UAV operators can plan a flexible working day. Further research is needed to verify whether these results can be extrapolated to other woody crops.

Real-time unmanned aerial vehicle flight path prediction using a bi-directional long short-term memory network with error compensation

Abstract In recent years, unmanned aerial vehicle (UAV) autonomous flight technology has been applied in many fields. However, in the process of autonomous operation, the UAV may deviate from the set flight path due to various disturbance factors, which results in mission failure. In order to find the abnormal situation in time and take corresponding measures, it is necessary to monitor the operation state of the UAV. Predicting the UAV flight path is the main monitoring method at present; however, the accuracy and real-time of the existing prediction methods are limited. Therefore, this paper proposes an error compensation Bessel bidirectional long short-term memory real-time path prediction model deployed in ground stations. First, because of inconsistency of the units in all directions of the original positioning information provided by global positioning system, the Bessel geodetic coordinate transformation is introduced to unify the units of three-dimensional coordinate data, so as to improve the prediction accuracy. Second, considering the problems of poor data quality and data missing in the operation process, the least square fitting method is used to supplement and correct the positioning coordinate data to obtain more reliable and accurate path observation values as the model input. Finally, a deep learning path prediction model based on bi-directional long short-term memory (BiLSTM) network is constructed, and the appropriate network parameters are determined with the prediction accuracy and time as evaluation indicators. In order to further improve the prediction accuracy, a compensator based on proportional integral differential error control theory is designed according to the output characteristics of the BiLSTM network, which is used for providing compensation values for the prediction results of the model. The training and testing results using the actual flight data of UAV operation show that, under the experimental environment built, the model proposed in this paper can complete the UAV flight path prediction with root mean square error < 1 meter within 0.1 second, and has better performance and higher prediction accuracy than other neural network models.

Robust Backstepping Control Combined with Fractional-Order Tracking Differentiator and Fractional-Order Nonlinear Disturbance Observer for Unknown Quadrotor UAV Systems

In this paper, we studied a fractional-order robust backstepping control (BSC) combined with a fractional-order tracking differentiator and a fractional-order nonlinear disturbance observer for a quadrotor unmanned aerial vehicle (UAV) system. A fractional-order filtered error and a fractional-order tracking differentiator were utilized in a conventional BSC system to improve the positioning control performance of a highly coupled nonlinear quadrotor UAV system and bypass the differentiation issue of the virtual control and compensation of the transformation error in the conventional BSC design. A new fractional-order disturbance observer with the sine hyperbolic function was then proposed to enhance the estimation performance of the uncertain quadrotor UAV. Sequential comparative simulations were conducted, demonstrating that the proposed positioning controller and observer utilizing fractional-order calculus outperformed those of the conventional controller and observer systems.

Three-Dimensional Path Planning of UAVs in Complex Urban Terrains: A Case Study of Emergency Medicine Delivery in Shanghai (China)

Unmanned aerial vehicles (UAVs), widely known as drones, are used in various domains for tasks including geological prospecting, e-commerce business, and emergencies. Because of the necessity for fast and efficient delivery for emergency medicine distribution, drones can play crucially important roles by their ability to pass through complex urban environments. Drones might therefore aid people living under strict lockdown conditions during surges cases of COVID-19 or other communicable diseases. Nevertheless, distribution routes are usually planned in two-dimensional space. Moreover, restricted areas in urban aerial domains might be overlooked because of complex environmental considerations. To boost the feasibility of drone use, three-dimensional (3D) path routing can be applied when planning aerial distribution routes for drones, such as those used for delivering emergency medicines. This study specifically examines a more reliable method of using heuristic algorithms and software ArcGIS. After collecting location data of chronic patients in lockdown areas from the Shanghai official information system database, 3D visualization of the terrain and complex airspace was done using ArcGIS. Secondly, UAV routing constraints are summarized according to current laws and regulations for UAV operation at low altitudes. Furthermore, feasible solutions are incorporated into this model. Finally, after improved ant colony optimization (ACO) application to 3D route planning problems, programming was done using MATLAB (ver. 2017b). Assuming guaranteed safety and compliance with regulations, the solutions demonstrate the algorithmic efficiency and provide a satisfactory route plan for emergency medicine delivery that might guide emergency delivery system routing design in similarly complex urban environments.

Autonomous Maneuver Decision of Air Combat Based on Simulated Operation Command and FRV-DDPG Algorithm

With the improvement of UAV performance and intelligence in recent years, it is particularly important for unmanned aerial vehicles (UAVs) to improve the ability of autonomous air combat. Aiming to solve the problem of how to improve the autonomous air combat maneuver decision ability of UAVs so that it can be close to manual manipulation, this paper proposes an autonomous air combat maneuvering decision method based on the combination of simulated operation command and the final reward value deep deterministic policy gradient (FRV-DDPG) algorithm. Firstly, the six-degree-of-freedom (6-DOF) model is established based on the air combat process, UAV motion, and missile motion. Secondly, a prediction method based on the Particle swarm optimization radial basis function (PSO-RBF) is designed to simulate the operation command of the enemy aircraft, which makes the training process more realistic, and then an improved DDPG strategy is proposed, which returns the final reward value to the previous reward value in a certain proportion of time for offline training, which can improve the convergence speed of the algorithm. Finally, the effectiveness of the algorithm is verified by building a simulation environment. The simulation results show that the algorithm can improve the autonomous air combat maneuver decision-making ability of UAVs.