Unmanned Aerial Vehicle Technology Research Articles

Review of Crop Phenotyping in Field Plot Experiments Using UAV-Mounted Sensors and Algorithms

The phenotyping of field crops quantifies a plant’s structural and physiological characteristics to facilitate crop breeding. High-throughput unmanned aerial vehicle (UAV)-based remote sensing platforms have been extensively researched as replacements for more laborious and time-consuming manual field phenotyping. This review aims to elucidate the advantages and challenges of UAV-based phenotyping techniques. This is a comprehensive overview summarizing the UAV platforms, sensors, and data processing while also introducing recent technological developments. Recently developed software and sensors greatly enhance the accessibility of UAV-based phenotyping, and a summary of recent research (publications 2019–2024) provides implications for future research. Researchers have focused on integrating multiple sensing data or utilizing machine learning algorithms, such as ensemble learning and deep learning, to enhance the prediction accuracies of crop physiological traits. However, this approach will require big data alongside laborious destructive measurements in the fields. Future research directions will involve standardizing the process of merging data from multiple field experiments and data repositories. Previous studies have focused mainly on UAV technology in major crops, but there is a high potential in minor crops or cropping systems for future sustainable crop production. This review can guide new practitioners who aim to implement and utilize UAV-based phenotyping.

Vulnerability Analysis of UAV Swarm Network with Emergency Tasks

With the rapid development of Unmanned Aerial Vehicle (UAV) technology, UAV swarms are used for emergency tasks in various scenarios such as area detection, fire rescue, logistics, and transportation. However, for complex scenarios, UAV swarms are prone to environmental interference that damages their equipment or disrupts their communication links, affecting the normal execution of tasks. In this paper, an information–communication interdependent network model is designed for the vulnerability analysis of UAV swarm networks with emergency tasks. Firstly, from the perspective of network functions of a UAV swarm, we introduce the theory of interdependent networks to abstract the relationship between the UAV swarm’s communication network and its information network, where the communication network represents its communication topology and the information network is related to the function of each UAV individual in the UAV swarm. Then, the vulnerability of the UAV swarm is analyzed according to the relationship between network construction costs and network connectivity under environmental interference. Finally, the effectiveness of the vulnerability analysis method is verified through simulation.

Multi unmanned aerial vehicle odor source location based on improved artificial fish swarm algorithm

Odor source location technology has important application value in environmental monitoring, safety emergency and search and rescue operations. For example, it can be used in post-disaster search and rescue, detection of hazardous gas leakage, and fire source detection. Existing odor source location methods have problems such as low search efficiency, inability to adapt to complex environments, and inaccurate odor source location. In this study, based on unmanned aerial vehicle technology and using swarm intelligence optimization algorithm, an improved artificial fish swarm algorithm (IAFSA) is proposed by combining curiosity in psychology on the basis of retaining the good optimization performance of the artificial fish swarm algorithm. The algorithm quantifies the curiosity of artificial fish searching high-concentration areas through a model, dynamically adjusts the artificial fish's field of vision and step length with the calculated curiosity factor, and avoids the oscillation phenomenon in the later stage of the algorithm. Simulation results show that the IAFSA has a higher success rate and smaller location error. Finally, odor source location experiments were carried out in an indoor physical environment, the feasibility of the odor source location method proposed in this study is verified in actual scenarios.

Optimization of Energy Efficiency for Federated Learning over Unmanned Aerial Vehicle Communication Networks

Federated learning (FL) is considered a promising machine learning technique that has attracted increasing attention in recent years. Instead of centralizing data in one location for training a global model, FL allows the model training to occur on user devices, such as smartphones, IoT devices, or local servers, thereby respecting data privacy and security. However, implementing FL in wireless communication faces a significant challenge due to the inherent unpredictability and constant fluctuations in channel characteristics. A key challenge in implementing FL over wireless communication lies in optimizing energy efficiency. This holds significant importance, especially considering user devices with restricted power resources. On the other hand, unmanned aerial vehicle (UAV) technologies present a cost-effective solution owing to flexibility and mobility compared to terrestrial base stations. Consequently, the deployment of UAV communication in FL is viewed as a potential approach to deal with the energy efficiency challenge. In this paper, we address the problem of minimizing the total energy consumption of all user equipment (UE) during the training phase of FL over a UAV communication network. Our proposed system facilitates UE to operate concurrently at the same time and frequency, thereby improving bandwidth utilization efficiently. In this paper, we address the problem of minimizing the total energy consumption during the training phase of FL over a UAV communication network. To deal with the proposed nonconvex problem, we propose a novel alternating optimization approach by dividing the problem into two suboptimal problems. We then develop iterative algorithms based on the inner approximation method, yielding at least one locally optimal solution. The numerical results demonstrate the superiority of the proposed algorithm in solving the proposed problem compared to other benchmark algorithms, particularly in determining the optimal trajectory of the UAVs. In addition, we conduct extensive experiments to evaluate how different parameter settings affect performance after implementing the proposed optimization approaches for deploying FL within the UAV communication system. These analyses yield valuable insights into the comparative effectiveness of the proposed optimization algorithms concerning overall energy consumption reduction.

Utilization of Unmanned Aerial Vehicle (UAV) Technology to Identify Land Cover in Kedai Damar Village, Serdang Bedagai, North Sumatera

Remote sensing has been widely used in various fields, one of which is to provide information related to land cover classification Kedai Damar Village is one of 13 villages in the Tebing Tinggi sub-district, Serdang Bedagai Regency. The settlement of Kedai Damar is the one that is most remote from the headquarters of the high cliffs sub-district. This settlement is situated in the center of the PT.Perkebunan Nusantara-IV plantation region in Pabatu North Sumatera. As a result, several rural towns have opened shops providing food and other essentials to farmers and farmworkers in order to rely on PT. Perkebunan Nusantara-IV plantation. This research was conducted in the Kedai Damar Village administrative center area using unmanned aircraft technology and then visually interpretation of aerial photos and mapping administrative area land cover in the Kedai Damar Village administrative center area using processed data in the form of orthophoto from agisoft metashape software. Based on the results of the study, the Kedai Damar Village area has 5 types of land cover classifications that can be identified visually based on 9 elements of interpretation (hue, color, shape, size, texture, pattern, shadow, site and association) as well as ground checks or data collection and observations. In the field. there are 5 types of land cover that can be visually identified, including non-oil Palm, open land, factory Area, settlement, oil palm plantations.

Effect of Incidence Angle on Temperature Measurement of Solar Panel with Unmanned Aerial Vehicle-Based Thermal Infrared Camera

This study utilizes Thermal Infrared (TIR) imaging technology to detect hotspots in photovoltaic (PV) modules of solar power plants. Unmanned aerial vehicle (UAV)-based TIR imagery is crucial for efficiently analyzing fault detection in solar power plants. This research explores optimal operational parameters for generating high-quality TIR images using UAV technology. In addition to existing variables such as humidity, emissivity, height, wind speed, irradiance, and ambient temperature, newly considered variables including the angle of incidence between the target object and the thermal infrared camera are analyzed for their impact on TIR images. Based on the solar power plant’s tilt (20°) and the location coordinate data of the hotspot modules, the inner and outer products of the vectors were used to obtain the normal vector and angle of incidence of the solar power plant. It was discovered that the difference between measured TIR temperature data and Land Surface Temperature (LST) data varies with changes in the angle of incidence. The analysis presented in this study was conducted using multiple regression analysis to explore the relationships between dependent and independent variables. The Ordinary Least Squares (OLS) regression model employed was able to explain 63.6% of the variability in the dependent variable. Further, the use of the Condition Number (Cond. No.) and the Variance Inflation Factor (VIF) revealed that the multicollinearity among all variables was below 10, ensuring that the independence among variables was well-preserved while maintaining statistically significant correlations. Furthermore, a positive correlation was observed with the actual measured temperature values, while a negative correlation was observed between the TIR image data values and the angle of incidence. Moreover, it was found that an angle of incidence between 15° and 20° yields the closest similarity to LST temperature data. In conclusion, our research emphasizes the importance of adjusting the angle of incidence to 15–20° to enhance the accuracy of TIR imaging by mitigating overestimated TIR temperature values.

Use of Unmanned Aerial Vehicle Technology in the Protection of Goods of Cultural Interest (GCIs): The Case of the Castle of Cala (Huelva, Spain)

Currently, the use of modern technologies, such as UAVs, allows for a detailed analysis of the protection of Sites of Cultural Interest (BICs) in Spain. Cala Castle in Huelva is selected for this study, which is one of the most important medieval fortifications in the mountainous region and was rehabilitated between 2003 and 2011. After a decade of its architectural rehabilitation, the use of this new UAV technology will allow the creation of a database of the property and its surroundings, made up of a series of 3D models and photogrammetric studies of the territory. This analysis allows us two complementary readings to the original study: on the one hand, to characterize the natural and landscape context of Cala Castle, and on the other, to identify the pre-existence of a series of historic buildings, which have historically articulated this region. The 3D models obtained provide relevant graphic information. The current state of the BIC allows this space to be considered for reactivation as a social space at the regional level. The visuals of the regional environment allow us to identify that the most recent growth has been articulated longitudinally along the N-630 highway, this infrastructure being the backbone of the nucleus. As a pilot test, the beginning of this graphic and visual database (3D) at a regional level will provide a valuable tool for the conservation and registration of built heritage, given that it seeks to incorporate other fortifications that make up Banda Gallega with the aim of defining a sustainable development strategy at county level.

Manipulating Camera Gimbal Positioning by Deep Deterministic Policy Gradient Reinforcement Learning for Drone Object Detection

The object recognition technology of unmanned aerial vehicles (UAVs) equipped with “You Only Look Once” (YOLO) has been validated in actual flights. However, here, the challenge lies in efficiently utilizing camera gimbal control technology to swiftly capture images of YOLO-identified target objects in aerial search missions. Enhancing the UAV’s energy efficiency and search effectiveness is imperative. This study aims to establish a simulation environment by employing the Unity simulation software for target tracking by controlling the gimbal. This approach involves the development of deep deterministic policy-gradient (DDPG) reinforcement-learning techniques to train the gimbal in executing effective tracking actions. The outcomes of the simulations indicate that when actions are appropriately rewarded or penalized in the form of scores, the reward value can be consistently converged within the range of 19–35. This convergence implies that a successful strategy leads to consistently high rewards. Consequently, a refined set of training procedures is devised, enabling the gimbal to accurately track the target. Moreover, this strategy minimizes unnecessary tracking actions, thus enhancing tracking efficiency. Numerous benefits arise from training in a simulated environment. For instance, the training in this simulated environment is facilitated through a dataset composed of actual flight photographs. Furthermore, offline operations can be conducted at any given time without any constraint of time and space. Thus, this approach effectively enables the training and enhancement of the gimbal’s action strategies. The findings of this study demonstrate that a coherent set of action strategies can be proficiently cultivated by employing DDPG reinforcement learning. Furthermore, these strategies empower the UAV’s gimbal to rapidly and precisely track designated targets. Therefore, this approach provides both convenience and opportunities to gather more flight-scenario training data in the future. This gathering of data will lead to immediate training opportunities and help improve the system’s energy consumption.

A UAV-Based Single-Lens Stereoscopic Photography Method for Phenotyping the Architecture Traits of Orchard Trees

This article addresses the challenges of measuring the 3D architecture traits, such as height and volume, of fruit tree canopies, constituting information that is essential for assessing tree growth and informing orchard management. The traditional methods are time-consuming, prompting the need for efficient alternatives. Recent advancements in unmanned aerial vehicle (UAV) technology, particularly using Light Detection and Ranging (LiDAR) and RGB cameras, have emerged as promising solutions. LiDAR offers precise 3D data but is costly and computationally intensive. RGB and photogrammetry techniques like Structure from Motion and Multi-View Stereo (SfM-MVS) can be a cost-effective alternative to LiDAR, but the computational demands still exist. This paper introduces an innovative approach using UAV-based single-lens stereoscopic photography to overcome these limitations. This method utilizes color variations in canopies and a dual-image-input network to generate a detailed canopy height map (CHM). Additionally, a block structure similarity method is presented to enhance height estimation accuracy in single-lens UAV photography. As a result, the average rates of growth in canopy height (CH), canopy volume (CV), canopy width (CW), and canopy project area (CPA) were 3.296%, 9.067%, 2.772%, and 5.541%, respectively. The r2 values of CH, CV, CW, and CPA were 0.9039, 0.9081, 0.9228, and 0.9303, respectively. In addition, compared to the commonly used SFM-MVS approach, the proposed method reduces the time cost of canopy reconstruction by 95.2% and of the cost of images needed for canopy reconstruction by 88.2%. This approach allows growers and researchers to utilize UAV-based approaches in actual orchard environments without incurring high computation costs.

Research on UAV flight attitude control system based on simulink and genetic algorithm

With the evolution of networking and information technology, unmanned aerial vehicles (UAVs) have emerged as integral components in numerous fields, executing diverse and vital tasks. The accuracy and efficiency of UAV operations are greatly influenced by their attitude control systems, which dictate flight stability, speed, and functionality. However, traditional control methods for UAV flight stability and accuracy have become outdated, unable to meet the demands of modern applications. To address these challenges, this study introduces a novel attitude control approach based on genetic algorithms. This method utilizes the principles of natural selection and genetic traits to optimize the parameters of the attitude control system. The objective is to enhance the current control effectiveness, improve the UAV's maneuverability, and enable its versatile applications across multiple fields. Simulink tool simulations are conducted to evaluate the stability of the optimized results. This simulation tool allows for the replication of real-world flight conditions and the assessment of the attitude control system's performance under various scenarios. The results of these simulations provide valuable insights into the effectiveness of the genetic algorithm-based attitude control method. This study contributes to the advancement of UAV technology by improving the accuracy and stability of attitude control systems. It paves the way for more efficient and reliable UAV operations in various fields, from environmental monitoring to search-and-rescue missions, thus enhancing their operational capabilities and expanding their potential applications.

An advanced cooperative multi-hive drone swarm system for global dynamic multi-source information awareness

With the advancement of unmanned aerial vehicle technology, dynamic monitoring with drones has been widely adopted to enhance multi-source information awareness capabilities. The cooperative strategy among drones still poses a significant challenge. Redundant actions within the drone swarm system can lead to a noticeable decrease in awareness performance. In this work, an advanced cooperative multi-hive drone swarm system is developed, which integrates multiple drones for information awareness and multiple hives for battery replacement. The system response is modeled by a series of discrete system state-action sequence, which follows a parallel system state transition mode. A well-designed simulated annealing-based hybrid algorithm (SA-HA) is developed for system response optimization, of which the simulated annealing process is adopted to coordinate two heuristic operators. To avoid redundant actions, an asynchronous cooperation mechanism (ACM) is proposed to strengthen the collaboration among agents in staggered system time intervals. Computational results indicate that the involvement of ACM can extract more problem-specific knowledge, which makes SA-HA easier to get high-quality system state-action sequences. Through the system redundancy analysis, we found that properly configured drones and hives can achieve high-efficiency global dynamic multi-source information awareness. The proposed system can provide pivotal support for regional situation awareness and analysis.

Design and Fabrication of Unmanned Aerial Vehicle for Object Recognition

The innovative realm of unmanned aerial vehicle assisted human face recognition. By amalgamating unmanned aerial vehicle technology and advanced computer vision algorithms, the research presents a robust system capable of detecting and identifying human faces from aerial perspectives. Real-time image processing and artificial intelligence methodologies are harnessed to ensure accurate performance across diverse environmental conditions. The proposed framework showcases its potential in surveillance, search and rescue missions, and crowd monitoring. Ethical considerations, limitations, and effectiveness are scrutinized through empirical evaluations, contributing valuable insights to the discourse surrounding privacy, security, and responsible unmanned aerial vehicle utilization.

Promoting UAV autonomous aerial video to improve cross-cultural understanding, historical knowledge, and students’ perception

ABSTRACT We designed a cross-cultural learning activity leveraging unmanned aerial vehicle (UAV) technology to enhance historical and cultural understanding among twenty university students from Indonesia and China. By recording and exchanging aerial videos of local cultural and historical sites using UAVs equipped with the latest AI features, students gained unique perspectives on these landmarks, unavailable through traditional ground-level photography. This study examined how such activities foster cross-cultural understanding and deepen historical knowledge, alongside participants' perceptions of UAV video production. Employing a mixed method approach, including video analysis, questionnaires, and interviews, we discovered that UAV technology not only facilitated a unique learning experience but also significantly impacted participants' cross-cultural and historical insights. The ease of UAV use and its effectiveness in cross-cultural education were particularly noted, with participants expressing intentions to use UAV technology in future learning contexts. Based on these findings, we offer recommendations for educators and researchers to integrate UAV technology into educational settings.

Unmanned aerial vehicle-assisted wideband cognitive radio network based on DDQN-SAC

Cognitive radio (CR) systems have emerged as effective tools for improving spectrum efficiency and meeting the growing demands of communication. This study focuses on a flexible CR system based on opportunistic spectrum access technology, which enables secondary networks to efficiently utilize unoccupied spectrum resources for information transmission by actively sensing the spectrum utilization of primary networks. Specifically, we introduce unmanned aerial vehicles (UAV) technology into the CR system to further enhance its flexibility and adaptability, which enables the transmission efficiency of low-altitude UAV networks. In this CR system, UAVs are employed for more flexible spectrum management. The objective of this research is to maximize the average achievable rate of SUs by jointly optimizing the trajectories of secondary UAV, the trajectories of primary UAV, the beamforming of secondary UAV, subchannel allocation and sensing time. To achieve this goal, we employ deep reinforcement learning (DRL) algorithms to optimize these variables. Compared to traditional optimization algorithms, DRL algorithms not only have lower computational complexity but also achieve faster convergence. To address the mixed-action space problem, we propose a Dueling DQN-Soft Actor Critic algorithm. Simulation results demonstrate that the proposed approach in this paper significantly enhances the performance of the CR system compared to traditional baseline schemes. This is manifested in higher spectrum efficiency and data transmission rates, while minimizing interference with the primary network. This innovative research combines drone technology and DRL algorithms, bringing new opportunities and challenges to the future development of cognitive communication systems.

Utilizing unmanned aerial vehicle technology for environmental monitoring: Future trends, methods, and applications

AbstractThis paper provides an overview of the application of Unmanned Aerial Vehicles (UAVs) in environmental monitoring, with a focus on air pollution surveillance. Initially, the paper highlights the significance of UAV technology for real‐time air quality monitoring in developing countries and explores the characteristics of fixed‐wing and multirotor UAVs in monitoring air pollutants. It then delves into methods of measuring air pollutants using UAV platforms, including the three‐dimensional distribution of pollutants around roadsides, green belts, and street‐facing communities. The advantages of UAV measurements, such as lower costs, greater flexibility, and the ability to monitor in three dimensions, are emphasized. Finally, the paper discusses future trends in the field of environmental monitoring using UAVs, including technological advancements, evolving regulatory policies, and integration with other technologies like Artificial Intelligence, big data analytics, and 5G communication. These developments suggest an increasingly significant role for UAVs in environmental monitoring, enhancing efficiency, reducing costs, and contributing to public participation and environmental awareness. However, due to the delays in publication and review processes, the most recent studies may not have been included in the literature review, leading to a scenario where the review might not reflect the latest research trends.

Interactive technology of high voltage tower acceptance UAV Based on AR and YOLOv7

To address many shortcomings of the current unmanned aerial vehicle (UAV) inspection mode of the transmission line high-voltage towers and improve the inspection efficiency, the UAV tower foreign matter detection technology based on Augmented Reality (AR) and You Only Look Once: Unified, Real-Time Object Detection (YOLOv7) has been developed. Firstly, the system architecture, including UAV, ground station, and AR glasses is established. Then, the AR scene is built by using the Unity engine, and the interactive control mode between AR glasses and UAV is designed. Subsequently, the mapping mode of head action and UAV command is devised to synchronize the UAV action and head action. Finally, the tower is detected for foreign objects by using YOLOv7 vision detection technology. The experimental results validate the efficacy of the built AR scene, demonstrating seamless functionality in completing the video streaming and attitude data transmission functions. The use of AR glasses can achieve interactive control of the UAV attitude. In addition, the YOLOv7 visual detection algorithm can accurately detect the attachment of foreign objects to the high-voltage tower.

High Speed Fighter UAV with Electric Coil Gun

The paper focus on "High-Speed Fighter Drone Using Electromagnetic Coil Gun and Dropping Mechanism" project represents a significant milestone in the realm of unmanned aerial vehicles (UAVs). This project aimed to design, develop, and demonstrate a cutting-edge high-speed fighter drone equipped with an electromagnetic coil gun and an advanced payload deployment mechanism. The primary objectives were to enhance drone maneuverability, achieve precision targeting capabilities, and enable efficient payload deployment, thus expanding the potential applications of UAV technology. This project involved an intricate blend of engineering, innovation, and technology integration. The drone's design and construction incorporated a lightweight yet durable frame, high-speed motors, and advanced flight control systems, ensuring exceptional agility and stability during flight. The electromagnetic coil gun, a pivotal component, was meticulously designed for precision targeting, delivering impressive accuracy and firing control. Moreover, the payload deployment mechanism, a vital feature of the system, was developed to ensure the precise and reliable delivery of payloads, expanding the drone's versatility across various domains, including defense, surveillance, and emergency response Throughout the project's lifecycle, rigorous testing, calibration, and data analysis were conducted to validate the drone's performance, accuracy of the electromagnetic coil gun, and efficiency of the payload deployment mechanism. The project yielded promising results, demonstrating the drone's exceptional flight performance, electromagnetic coil gun's precision targeting, and payload deployment's reliability. This project report provides a comprehensive overview of the project's objectives, methodologies, results, and significance. It explores the intricate system design, integration processes, testing procedures, and detailed analyses of outcomes. Furthermore, it discusses the challenges faced during development and the potential impact of this high- speed fighter drone in various industries. In conclusion, the "High-Speed Fighter Drone Using Electromagnetic Coil Gun and Dropping Mechanism" project represents a significant leap forward in UAV technology. Its successful realization opens up new possibilities for drone applications, promising advancements in precision targeting, payload deployment, and the overall versatility of UAVs in both civilian and military contexts

Performance Analysis of Forest Canopy Height Model Generated from UAV and InSAR

Forest canopy height is a crucial parameter in ecosystem process modelling, yet research on generating canopy height models (CHMs) using photogrammetry method from UAV data remains limited compared to methods such as light detection and ranging (LiDAR). This study investigates the performance of accuracy and effectiveness of CHM generated from low-cost Unmanned Aerial Vehicle (UAV) via the photogrammetry method together with the well-known long-established Interferometric Synthetic Aperture Radar (InSAR). Leveraging advancements in UAV technology for three-dimensional land surface modelling, this research focuses on the Pasoh Research Station (nearby the Arboretum area) of Pasoh Forest Reserve in Negeri Sembilan, Malaysia. A series of images were captured at ten different UAV flying altitudes ranging from 120m to 500m above the mean sea level. Subsequently, CHMs were derived from both UAV and InSAR data, and compared against field measurements of tree height. The results indicate that UAV with a flying height below 200m can generate much more accurate results (with average height difference less than 10m) than InSAR (average height difference of 10.532m). It is noticeable that when comparing the InSAR-generated CHM to field measurements at lower altitudes (below 150m), the canopy height obtained from InSAR data is less accurate (more than 10m difference with field measurement) than the CHM obtained via UAV photogrammetry processing (only 5.353 m difference with field measurement). However, when UAV flying height is above 200m, InSAR data is performed more reliably. A 120m UAV flying height has a good potential to generate canopy height that is closer to the field measurement.

Integration of UAVs with public transit for delivery: Quantifying system benefits and policy implications

The maturation and scalability of unmanned aerial vehicle (UAV) technology offer transformative opportunities to revolutionize prompt delivery. This study explores integrating UAVs with public transportation vehicles (PTVs) to establish a novel delivery paradigm that enhances revenue for public transit operators and improves transport system efficiency without compromising passenger convenience or operational efficiency. Employing hexagonal planning technology, this study identifies and quantifies the available spatio-temporal resources of PTVs for UAV integration. This involves aligning the spatio-temporal dynamics of prompt delivery orders with PTV ridership, based on field data from Beijing’s Haidian District. Utilizing these outputs, we quantitatively analyze the benefits of integrating UAVs with PTVs on increasing public transit revenue, and potentials of reducing carbon emissions and mitigating congestion. Furthermore, we quantify the long-term benefits of UAV-PTV integration by predicting future increases in delivery demand. Based on obtained quantitative results, this study discusses practical and policy implications to support the sustainable integration of UAVs with PTVs.

OPTIMIZING UNMANNED AERIAL VEHICLE BASED FOOD DELIVERY THROUGH VEHICLE ROUTING PROBLEM: A COMPARATIVE ANALYSIS OF THREE DELIVERY SYSTEMS.

In recent times, there has been a notable increase in interest surrounding the integration of Un-manned Aerial Vehicle (UAV) technology and vehicle routing problems (VRP) for package delivery purposes. While existing studies have explored various types of package deliveries utilizing VRP, limited attention has been given to on-demand food delivery. This study aims to develop a VRP model that incorporates practical constraints such as payload capacity and maximum flying range, with the primary objective of minimizing travel distance in food delivery operations. A comparative analysis is conducted among three delivery methods, including UAV delivery, to determine the most effective approach and assess the feasibility of each method. Through a case study analysis focused on a pizza delivery service in Sri Lanka, it was observed that implementing VRP in a motorbike delivery system resulted in reduced travel distance, time, cost, and CO2 emissions compared to the existing delivery system. Furthermore, the utilization of UAVs in conjunction with VRP yielded even greater improvements across all parameters. Based on a comprehensive cost analysis considering long-term operations, the UAV-based delivery system was identified as the most cost-effective method, followed by the VRP-incorporated motorbike delivery method. Although the VRP-incorporated motorbike delivery system exhibited a slightly higher average time per route compared to the existing method, the total travel time required to complete all routes remained lower. Consequently, the study concludes that the VRP-incorporated motorbike delivery system outperforms the existing delivery method for food delivery, with the use of UAVs incorporating VRP identified as the optimal delivery method among the three alternatives. The findings contribute valuable insights to the optimization of food delivery logistics, emphasizing the potential of VRP and exploring the feasibility of UAVs for sustainable and efficient long-term delivery solutions.