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Unmanned Aerial Vehicles and Systems: Research Feed

Latest Research from TRID Database

  • Unmanned Aerial Vehicle Aided Batteryless Covert Communications: A Robust Design Against Cooperative Hostile DetectionThis link opens in a new windowNov 22, 2024
    An unmanned-aerial-vehicle (UAV) is exploited for securely collecting sensing data from massively deployed batteryless devices on the ground. Specifically, signals emitted by the UAV have the following functions: jamming hostile wardens (HWs) and powering legitimate devices (LDs). The transmit power of both the LDs and the UAV as well as the UAV's flying trajectory are jointly optimised for maximising the uplink throughput of data collection, while ensuring that the uplink transmissions of LDs to the UAV cannot be detected by HWs. The formulated optimisation problem is difficult to solve due to its non-convexity. Therefore, a successive convex approximation (SCA) based alternating optimisation (AO) is proposed for obtaining a near-optimal solution. In the authors' design, the UAV does not need the exact locations of the HWs, while it is robust to the optimal cooperative detection among the HWs with a greedy scheduling (GS) based algorithm. The numerical results demonstrate that the uplink-throughput can be substantially improved by the authors' joint design.
  • Detecting Fouled Ballast Using Forward-Looking Infrared Radiometer (FlIR) TechnologyThis link opens in a new windowNov 20, 2024
    The feasibility and limitations of Forward Looking Infrared Radiometer (FLIR) Aerial Technology for detecting fouled ballasts is studied in this project. The method is intended to provide an efficient and ready-to-use approach that can help the railroads detect fouled ballasts in their early stages. Ballast fouling commonly occurs as a result of fine particles clogging off water passage through them. Subsequently, this results in trapped water that often results in poor foundation strength, rotting of the ties, and other ill effects. This study includes a novel approach to evaluate the railway ballast fouling by using thermal imaging techniques. A simple setup for implementing ballast fouling of different amounts have been implemented in the lab. For the purpose of laboratory testing, the camera is set up in stationary and moving configurations. The thermal characteristics of clean and fouled ballasts are studied using FLIR cameras that can be used onboard rolling stock, Hyrail trucks, or drones. Laboratory tests are primarily performed to measure the surface temperature changing rate of clean and fouled ballasts in response to ambient temperature changes. The test results indicate that clean and fouled ballasts have different thermal characteristics. In particular, different thermal patterns are obtained during naturally-occurring daily temperature change. The test results also indicate that the FLIR cameras can be used on a moving platform for quick scanning of thermal images of the ballasts that could be used for assessing the early stage of fouling.
  • Optimization of the Special Cargo Delivery by UAVThis link opens in a new windowNov 20, 2024
    During the pandemic, drones helped many FFCs access goods and services to the government and the general population. In response, many regulatory bodies worldwide have shown interest in helping the industry develop. Regulators are now looking for ways to support the development of drone technology by exploring ways to transport heavier goods and people. They are issuing more permits within existing frameworks and adopting more comprehensive frameworks to allow for more drone operations. Overall, drones are expected to play an essential role in all applications. This makes them an excellent solution for transportation applications. This study analyzes the role of unmanned aerial vehicles (UAVs) in delivering medical and emergency supplies to remote areas. It outlines potential considerations for operators wishing to use UAVs to provide medical and emergency supplies to remote locations. The article also discusses some practical considerations regarding the organization wishing to conduct such operations, the operations themselves, and the technology used. These considerations are primarily driven by the nature of the international regulatory framework for UAV operations and the specifics of using UAVs to deliver medical and emergency supplies.
  • Influence of the Ground Effect on the Precise Landing of an Unmanned AircraftThis link opens in a new windowNov 20, 2024
    This paper examines a multi-rotor unmanned aerial vehicle and the influence of the ground effect on the aircraft during the landing stage. Theoretical and practical research of other authors and results are described. It is briefly discussed how the position of the high-pressure area changes with changing environmental conditions. The study examined the ground effect on the drone at 1/3R - 2R. Calculations show that the propeller thrust increases up to 7%. Also, discussing the results, it has been hypothesized that an increase in thrust near the ground will reduce engine speed, which may directly impact the drone's stability and landing accuracy. Further research is needed to explore this issue further.
  • Development of Multi-Rotor-UAV-Based Rail Track Irregularity Monitoring and Measuring Platform With Image and Lidar SensorsThis link opens in a new windowNov 20, 2024
    The field of track geometry measurement has evolved from manual and visual methods to sophisticated Track Geometry Measurement Systems (TGMS), which use advanced digital instrumentation to record various parameters. Despite their ability to measure long distances with minimal human resources, TGMS still face a critical limitation: the need to close tracks during inspection. This project aimed to develop a multi-rotor UAV-based track geometry measurement system using image and LiDAR sensors that does not require the closure of track during inspection. Key challenges, including UAV path planning, data collection, and data processing, were addressed. The research was divided into three stages: exploration of optimal path planning, development of a LiDAR-only track geometry measurement system, and development of a camera-LiDAR track geometry measurement system. The authors explored integrating camera data with LiDAR to enhance track geometry measurement. Due to time constraints, a semi-assisted supervised image segmentation approach was used, yielding high accuracy when the rails were vertically aligned in the images. Calibration results were highly accurate with checkerboard data but showed significant errors when applied to rail data. As a result, the current data fusion approach is not yet suitable for the track geometry measurement platform. Future research will aim to achieve more comprehensive image segmentation and improve the accuracy of LiDAR-camera calibration.
  • Sustainable last mile logistics employing drones and e-bikesThis link opens in a new windowNov 20, 2024
    Last mile delivery is an important and growing part of the supply chain that has a sizeable negative environmental impact. This paper considers more sustainable approaches to home-delivery that are pragmatic for many non-rural environments. The authors address the two-echelon, multi-trip, capacitated vehicle routing problem with home-delivery and optional self-pickup services using different combinations of drones, trucks, and electric-assisted bikes (i.e. e-bikes). In the proposed approach, parcels are transported from a depot to parcel lockers by either drones or trucks and are then delivered to customer locations by either e-bikes or trucks. The four approaches range from the most sustainable (drones then e-bikes) to partly green (drone then truck or truck then e-bike) to finally the traditional (truck then truck). The authors formulate a mathematical model that determines the vehicle routes to minimize the total cost, which consists of vehicle operational cost and operator wages. The four types of delivery networks are assessed and compared for both costs and emissions. Experimental results suggest that with a modest increase in total cost (as little as 13%), emission reductions of up to 92%, on average, can be achieved when using the greenest delivery strategy of drones and e-bikes. The other delivery options have varying tradeoffs between costs and environmental impact. The percentage of self-pickup customers is an influencing factor to consider when choosing the delivery strategy that best meets the organization’s budget and environmental goals, especially when using e-bikes in the second echelon.
  • A Centralized Multi-Agent DRL-Based Trajectory Control Strategy for Unmanned Aerial Vehicle-Enabled Wireless CommunicationsThis link opens in a new windowNov 19, 2024
    Unmanned aerial vehicles (UAVs) are becoming increasingly popular as mobile base stations due to their flexible deployment and low-cost features, particularly for emergency communications, traffic offloading, and terrestrial communications infrastructure failures. This paper presents an autonomous trajectory control method for multiple UAVs equipped with base stations for UAV-enabled wireless communications. The objective of this work is to address the optimization challenge of maximizing both communication coverage and network throughput for ground users. The proposed multi-aerial base station trajectory control (MATC) scheme employs a two-stage learning approach. Initially, the authors developed a long short-term memory-based link quality estimation model to assess each user's link quality over time. The trajectory of the aerial base stations is then continuously adjusted through a centralized multi-agent deep reinforcement learning algorithm to optimize communication performance. The authors evaluated their proposed system using real channel measurement data, i.e., amplitude and phase signal information. Notably, the proposed approach operates solely on received signals from users, without requiring knowledge of their specific locations. The proposed MATC strategy achieves 97.41% communication coverage while maintaining satisfactory system throughput performance. Numerical results demonstrate that the proposed method significantly enhances both communication coverage and network throughput in comparison to the base line algorithms.
  • Large Language Models for UAVs: Current State and Pathways to the FutureThis link opens in a new windowNov 19, 2024
    Unmanned Aerial Vehicles (UAVs) have emerged as a transformative technology across diverse sectors, offering adaptable solutions to complex challenges in both military and civilian domains. Their expanding capabilities present a platform for further advancement by integrating cutting-edge computational tools like Artificial Intelligence (AI) and Machine Learning (ML) algorithms. These advancements have significantly impacted various facets of human life, fostering an era of unparalleled efficiency and convenience. Large Language Models (LLMs), a key component of AI, exhibit remarkable learning and adaptation capabilities within deployed environments, demonstrating an evolving form of intelligence with the potential to approach human-level proficiency. This work explores the significant potential of integrating UAVs and LLMs to propel the development of autonomous systems. The authors comprehensively review LLM architectures, evaluating their suitability for UAV integration. Additionally, they summarize the state-of-the-art LLM-based UAV architectures and identify novel opportunities for LLM embedding within UAV frameworks. Notably, the authors focus on leveraging LLMs to refine data analysis and decision-making processes, specifically for enhanced spectral sensing and sharing in UAV applications. Furthermore, they investigate how LLM integration expands the scope of existing UAV applications, enabling autonomous data processing, improved decision-making, and faster response times in emergency scenarios like disaster response and network restoration. Finally, the authors highlight crucial areas for future research that are critical for facilitating the effective integration of LLMs and UAVs.
  • Utilizing Partial Non-Orthogonal Multiple Access (P-NOMA) in Drone-Enabled Internet-of-Things Wireless NetworksThis link opens in a new windowNov 19, 2024
    Future drone-enabled Internet-of-Things (IoT) wireless networks have attracted considerable attention from industry and academia. Future drone-enabled IoT wireless networks are expected to enable the Internet of Everything and provide services with massive connectivity, heterogeneous quality of service, ultra-reliability, and higher throughput. Therefore, future drone-enabled IoT wireless networks necessitate more effective use of wireless resources and efficient interference management approaches. As a result, the multiple access techniques and the physical layer for wireless communication systems have been rethought and redesigned. This paper proposes utilizing the partial non-orthogonal multiple access (P-NOMA) in drone-enabled IoT wireless networks, where a single drone provides wireless coverage for a set of IoT devices. In P-NOMA, a portion of the channel is orthogonal, while the other is non-orthogonal for each IoT device. When using a non-orthogonal channel portion, an IoT device that receives high transmit power from the drone treats a signal of another IoT device as noise and quickly recovers its signal without using a successive interference cancellation (SIC) process. However, an IoT device that receives low transmit power from that drone must perform the SIC process on a non-orthogonal channel portion to recover its signal. The optimization problem in this research aims to find the maximum sum data rate of all IoT devices, considering the 3D placement of the drone, device pairing, and the parameters of P-NOMA. Finding the optimal solution to the optimization problem is challenging because of the NP-completeness of the formulated problem. Therefore, a decomposition framework is proposed to aid in solving it. Particularly, the optimization problem is decomposed into three subproblems: the 3D placement for the drone, device pairing, and P-NOMA parameters. Then efficient techniques are proposed to solve these subproblems. Simulation results verify the efficacy of utilizing P-NOMA in drone-enabled IoT wireless networks. Specifically, the results demonstrate that P-NOMA can boost the sum rate by 22%–28% compared with NOMA and by 83%–104% compared with OMA.
  • A Systematic Review of the UAV Technology Usage in ASEANThis link opens in a new windowNov 19, 2024
    Unmanned aerial vehicles (UAVs) are emerging and have been globally incorporated in wide range of technologies for various purposes due to its advantages over conventional techniques. Nonetheless, the strength of its application areas varies globally. The aim of this paper is to systematically review the literature to provide pertinent information on UAVs’ applications among the association of southeast Asian nations (ASEAN) countries by reviewing 179 documents published from 2012 to the end of 2023. Besides, the authors also investigated the current state of the relevant policies and regulations among member states. The results of the research demonstrate the state of UAV adoption, application areas, popularity among member states, key aspects that are main drivers for the adoption of UAV technology in the region, and a comparison of UAV policy usage among member states. In particular, the reviewed documents highlighted 12 distinct application areas and 4 major aspects making UAV technology attractive to the region, including geographical, climatic and environmental, ecosystem conservation, and economic factors.
  • UAV-Assisted Space-Air-Ground Integrated Networks: A Technical Review of Recent Learning AlgorithmsThis link opens in a new windowNov 18, 2024
    Recent technological advancements in space, air, and ground components have made possible a new network paradigm called “space-air-ground integrated network” (SAGIN). Unmanned aerial vehicles (UAVs) play a key role in SAGINs. However, due to UAVs' high dynamics and complexity, real-world deployment of a SAGIN becomes a significant barrier to realizing such SAGINs. UAVs are expected to meet key performance requirements with limited maneuverability and resources with space and terrestrial components. Therefore, employing UAVs in various usage scenarios requires well-designed planning in algorithmic approaches. This paper provides an essential review and analysis of recent learning algorithms in a UAV-assisted SAGIN. The authors consider possible reward functions and discuss the state-of-the-art algorithms for optimizing the reward functions, including Q-learning, deep Q-learning, multi-armed bandit, particle swarm optimization, and satisfaction-based learning algorithms. Unlike other survey papers, they focus on the methodological perspective of the optimization problem, applicable to various missions on a SAGIN. The authors consider real-world configurations and the 2-dimensional (2D) and 3-dimensional (3D) UAV trajectories to reflect deployment cases. Their simulations suggest the 3D satisfaction-based learning algorithm outperforms other approaches in most cases. With open challenges discussed at the end, the authors aim to provide design and deployment guidelines for UAV-assisted SAGINs.
  • Advancing UAV Communications: A Comprehensive Survey of Cutting-Edge Machine Learning TechniquesThis link opens in a new windowNov 18, 2024
    This paper provides a comprehensive overview of the evolution of Machine Learning (ML), from traditional to advanced, in its application and integration into unmanned aerial vehicle (UAV) communication frameworks and practical applications. The manuscript starts with an overview of the existing research on UAV communication and introduces the most traditional ML techniques. It then discusses UAVs as versatile actors in mobile networks, assuming different roles from airborne user equipment (UE) to base stations (BS). UAV have demonstrated considerable potential in addressing the evolving challenges of next-generation mobile networks, such as enhancing coverage and facilitating temporary hotspots but pose new hurdles including optimal positioning, trajectory optimization, and energy efficiency. The authors therefore conduct a comprehensive review of advanced ML strategies, ranging from federated learning, transfer and meta-learning to explainable AI, to address those challenges. Finally, the use of state-of-the-art ML algorithms in these capabilities is explored and their potential extension to cloud and/or edge computing based network architectures is highlighted.
  • Deep Reinforcement Learning Assisted UAV Path Planning Relying on Cumulative Reward Mode and Region SegmentationThis link opens in a new windowNov 18, 2024
    In recent years, unmanned aerial vehicles (UAVs) have been considered for many applications, such as disaster prevention and control, logistics and transportation, and wireless communication. Most UAVs need to be manually controlled using remote control, which can be challenging in many environments. Therefore, autonomous UAVs have attracted significant research interest, where most of the existing autonomous navigation algorithms suffer from long computation time and unsatisfactory performance. Hence, the authors propose a Deep Reinforcement Learning (DRL) UAV path planning algorithm based on cumulative reward and region segmentation. The proposed region segmentation aims to reduce the probability of DRL agents falling into local optimal trap, while the proposed cumulative reward model takes into account the distance from the node to the destination and the density of obstacles near the node, which solves the problem of sparse training data faced by the DRL algorithms in the path planning task. The proposed region segmentation algorithm and cumulative reward model have been tested in different DRL techniques, where the authors show that the cumulative reward model can improve the training efficiency of deep neural networks by 30.8% and the region segmentation algorithm enables deep Q-network agent to avoid 99% of local optimal traps and assists deep deterministic policy gradient agent to avoid 92% of local optimal traps.
  • Unmanned Underwater Vehicles in Crime Detection and PreventionThis link opens in a new windowNov 18, 2024
    The relevance of the research is determined by the need to develop effective methods and strategies for the use of modern technologies to ensure the security of marine and underwater facilities in the face of the growing threat of underwater activity, terrorism, and violations of laws at sea. The research aims to analyse and document specific tactical and organisational approaches to the use of unmanned underwater vehicles aimed at increasing the effectiveness of actions related to the detection, investigation, and prevention of underwater crime. Classification, analytical, functional, statistical, and synthesis methods are among the methods used. The research results highlight the global experience of using drones in forensic practice and may serve as a basis for further improvement of legislation in this area. The application of uninhabited underwater vehicles by law enforcement and state bodies in the investigation of criminal offences is considered in the article, and the norms of criminal procedural legislation of the Republic of Kazakhstan regulating the use of technical means in criminal proceedings are analysed. The study reveals the lack of comprehensive research on the use of unmanned underwater vehicles as scientific and technical means for the investigation of certain types of criminal cases, such as drug-trafficking and poaching, and offers recommendations on their use to identify and search for evidence in the aquatic environment. This research has a practical value by providing new methodologies and strategies for organisations and law-enforcement agencies in the field of underwater security, contributing to more effective detection, investigation, and prevention of crime in the maritime and underwater domains.
  • Supporting inclusive debate on Advanced Air Mobility: An evaluationThis link opens in a new windowNov 18, 2024
    Advanced Air Mobility (AAM) is being progressed, yet evidence suggests low levels of public salience and minimal debate. Efforts to engage the public have been framed around achieving acceptance made with little clarity of the potential impacts and benefits. This paper analyses an approach which sought to overcome low interest and to make technical information accessible to a general audience. The research used virtual reality (VR) to represent AAM technologies in public spaces proximal to where participants lived. During a second phase of research, additional supporting materials (an animation, a short game, and a recorded presentation) were developed to respond to gaps in understanding. The research was undertaken at five sites in England (N = 603). The representativeness of the sample is analysed, and the value of the VR, additional materials, and siting of the research are reviewed. Drawing upon detailed responses to open questions, the extent of meaningful involvement is explored showing how the additional supporting materials increased the depth of understanding amongst participants.