Unmanned Aerial Vehicle Trajectory Research Articles

Two-Tier Efficient QoE Optimization for Partitioning and Resource Allocation in UAV-Assisted MEC.

Unmanned aerial vehicles (UAVs) have increasingly become integral to multi-access edge computing (MEC) due to their flexibility and cost-effectiveness, especially in the B5G and 6G eras. This paper aims to enhance the quality of experience (QoE) in large-scale UAV-MEC networks by minimizing the shrinkage ratio through optimal decision-making in computation mode selection for each user device (UD), UAV flight trajectory, bandwidth allocation, and computing resource allocation at edge servers. However, the interdependencies among UAV trajectory, binary task offloading mode, and computing/network resource allocation across numerous IoT nodes pose significant challenges. To address these challenges, we formulate the shrinkage ratio minimization problem as a mixed-integer nonlinear programming (MINLP) problem and propose a two-tier optimization strategy. To reduce the scale of the optimization problem, we first design a low-complexity UAV partition coverage algorithm based on the Welzl method and determine the UAV flight trajectory by solving a traveling salesman problem (TSP). Subsequently, we develop a coordinate descent (CD)-based method and an alternating direction method of multipliers (ADMM)-based method for network bandwidth and computing resource allocation in the MEC system. Extensive simulations demonstrate that the CD-based method is simple to implement and highly efficient in large-scale UAV-MEC networks, reducing the time complexity by three orders of magnitude compared to convex optimization methods. Meanwhile, the ADMM-based joint optimization method achieves approximately an 8% reduction in shrinkage ratio optimization compared to baseline methods.

User Scheduling and Path Planning for Reconfigurable Intelligent Surface Assisted MISO UAV Communication

The high mobility of unmanned aerial vehicles (UAVs) enables them to improve system throughput by establishing line-of-sight (LoS) links. Nevertheless, in urban environments, these LoS links can be disrupted by complex urban structures, leading to potential interference issues. Reconfigurable intelligent surfaces (RIS) provide an innovative approach to enhance communication performance by intelligently reflecting incident signals. Recent studies suggest that utilizing multi-antenna transmission can increase system efficiency, while single-antenna transmission may be more prone to interference. To address these challenges, this article introduces a RIS-assisted multiple-input single-output (MISO) UAV communication system. Our objective is to optimize the minimum user rate, thereby guaranteeing equitable communication for all users. Nevertheless, the non-convexity inherent in this optimization problem complicates the pursuit of a direct solution. Hence, we decompose the problem into four subproblems: user scheduling optimization, RIS phase-shift optimization, UAV trajectory optimization, and UAV transmit beamforming optimization. To obtain suboptimal solutions, we have developed an alternating iterative optimization algorithm for addressing the four subproblems. Numerical results demonstrate that our algorithm effectively boosts the minimum user rate of the entire system.

Optimization of multi-user fairness in RIS-enhanced UAV secure transmission systems

This paper proposes a reconfigurable intelligent surface (RIS)-enhanced unmanned aerial vehicle (UAV) secure communicating scheme with multiple mutually-untrusted ground users (GUs). To resist eavesdropping, a pre-defined artificial noise (AN) is added to the intended signal at UAV for scheduled GUs in each time slot. The GU scheduling schemes, UAV trajectories and transmit power allocations in different time slots are jointly designed to ensure communication security for all GUs. To boost secrecy rate of the system while ensuring secure fairness among GUs, we conduct a joint optimization process, involving optimizing UAV trajectories, RIS phase shifts, GU scheduling schemes, and UAV transmit power splitting factors, adhering to the constraints of UAV mobility, RIS phase shifts and other related constraints. To solve this non-convex optimization, we utilize the block coordinate descent (BCD) method for problem decomposition, coupled with an iterative algorithm to optimize the resulting sub-problems. To further solve the non-convex sub-problems, we employ the variable substitution to convexify the transmit power allocation, the semi-deterministic relaxation (SDR) to convexify the RIS passive beamforming and successive convex approximation (SCA) to convexify UAV mobility constraints. Simulation results show the convergence and effectiveness of the proposed scheme, compared to the benchmark schemes, the average worst-case secrecy rate increases by 32.33%, 60.38% and 80.09‘% respectively.

Метод виділення контурів для вирішення завдань моніторингу транспортної інфраструктури

Introduction. Technologies for monitoring transport infrastructure have been rapidly evolving in recent years, absorbing innovations and the latest developments. The main direction of development and use for this technology has been the implementation of continuous monitoring and control of different aspects of transport infrastructure to ensure its safety and allow efficient and timely management. Computer vision has been playing the main role in the evolution of these technologies and has made unmanned aerial vehicles (UAVs) the most cost-efficient remote monitoring tool. The purpose of the paper. Among the main tasks in the field are monitoring traffic and the conditions of road surfaces and markings. Fast and accurate monitoring systems enable quick responses and minimize negative consequences for citizens. Despite the active development of computer vision algorithms, there is no universal algorithm that suits all scenarios. Algorithms depend on the task, conditions, and even UAV trajectory; even a slight change in the visual scene can cause suboptimal results. Lately, significant progress has been made in the development of edge detection algorithms. However, they do not consider the specifics of the task of monitoring road markings. The algorithm should consider the characteristics of the objects of interest – their geometric and color features, and the presence of many other objects in the images. The goal of this paper is to present method crafted specifically for the task of monitoring transport infrastructure. Methods. Computer vision, threshold filtering, Sobel operator, noise removal, probabilistic Hough transform, histograms. Results. The main features of the task of monitoring transport infrastructure using visual data obtained from surveillance cameras or unmanned aerial vehicles have been analyzed. Developed an algorithm for boundary extraction of point clusters using histograms. A method for edge detection in images has been developed, which addresses the shortcomings of known methods and is specifically enhanced for transport infrastructure monitoring. The method leverages the narrow specialization of the task to improve the obtained results. The foundation of the method is based on the features of the HSL color model, filtering in the saturation and lightness channels using gradients obtained from the Sobel operator, segment detection based on the probabilistic Hough transform, and a developed algorithm for boundary extraction of point clusters using histograms. Conclusion. The proposed method can be used in automated and semi-automated decision-making systems, UAV design bureaus, UAV manufacturing enterprises, and information-analytical centers to develop unmanned aviation systems and aerial monitoring technologies to enhance human safety and the economic development of the state. The use of automatic remote monitoring data processing methods allows for faster acquisition of necessary results and improves the efficiency of using geospatial data. Keywords: Computer vision, object detection, edge detection, image filtering, transportation infrastructure, information technology, monitoring.

Multi-UAV aided energy-aware transmissions in mmWave communication network: Action-branching QMIX network

Advancements in drone technology and high-frequency millimeter-wave communications are transforming unmanned-aerial-vehicles (UAV)-aided networks, expanding their potential across diverse applications. Despite the advantages of broad frequency bandwidth and enhanced line of sight connectivity in the UAV-aided millimeter-wave networks, it is challenging to provide high network performance because of the inherent limitations of limited UAV energy and millimeter-wave’s large path loss. This challenge becomes more important in dynamically changing multi-UAV environments. To address this challenge in multi-UAV networks, we propose a novel approach based on multi-agent deep reinforcement learning called action-branching QMIX. Our method determines nearly optimal codebook-based discrete beamforming vectors and UAV trajectories while maintaining a balance between communication efficiency and energy consumption. The proposed approach employs a new Long Short-Term Memory module to control long sequences effectively and enables it to adapt to changing environmental variables in real-time. We thoroughly evaluate the proposed control with a real-world measurement-based channel model. The evaluation confirms that the proposed control converges stably and consistently, and provides enhanced performance in terms of downlink data rate, success rate of reaching the destination, and service duration when compared to traditional benchmark multi-agent reinforcement learning schemes. These results emphasize the enhanced energy sustainability, robustness, and stability of the proposed approach in dynamically changing multi-UAV environments when compared to the existing benchmark algorithms.

Multi-IRSs enhanced UAV secure semantic communication system

This paper delves into a unmanned aerial vehicle (UAV) semantic communication system featuring multiple intelligent reflecting surfaces (IRSs). The UAV transmits semantic symbols to users in the presence of eavesdropping, and thus has inevitable security problems. In order to measure secure communication performance in semantic communication, the security semantic transmission rate (SSR) based on the semantic similarity is employed. An optimization problem is formulated to enhance the average SSR by optimizing UAV trajectory, IRS reflecting coefficients, number of semantic symbols, and transmission power, while satisfying position, speed, anti-collision, and power constraints. Due to the highly dynamic nature of the environment and the difficulties in solving complex modeling problems, a novel approach based on hybrid soft actor–critic (H-SAC) deep reinforcement learning (DRL) is proposed to address this non-convex optimization problem. The algorithm addresses the challenge of dynamic environments in UAV scenarios by seamlessly accommodating both discrete and continuous action spaces such as the trajectory and the IRS reflecting coefficient within the network. This enables effective processing of actions taken by intelligent agents, ensuring adaptability to the diverse requirements of the environment without the need for separate handling mechanisms. Simulation results show that the proposed intelligent algorithm exhibits excellent convergence and achieves a well-balanced pairing of appropriate IRSs and selection of flight paths, significantly improving the average SSR of the system. Specifically, our proposed scheme enhances the secure semantic transfer rate by more than fifty percent compared to when the number of semantic symbols is fixed at two.

UAV-Assisted Mobile Edge Computing: Dynamic Trajectory Design and Resource Allocation.

The recent advancements of mobile edge computing (MEC) technologies and unmanned aerial vehicles (UAVs) have provided resilient and flexible computation services for ground users beyond the coverage of terrestrial service. In this paper, we focus on a UAV-assisted MEC system in which the UAV equipped with MEC servers is used to assist user devices in computing their tasks. To minimize the weighted average energy consumption and delay in the UAV-assisted MEC system, a LQR-Lagrange-based DDPG (LLDDPG) algorithm, which jointly optimizes the user task offloading and the UAV trajectory design, is proposed. To be specific, the LLDDPG algorithm consists of three subproblems. The DDPG algorithm is used to address the issue of UAV desired trajectory planning, and subsequently, the LQR-based algorithm is employed to achieve the real-time tracking control of UAV desired trajectory. Finally, the Lagrange duality method is proposed to solve the optimization problem of computational resource allocation. Simulation results indicate that the proposed LLDDPG algorithm can effectively improve the system resource management and realize the real-time UAV trajectory design.

Design of a UAV Trajectory Prediction System Based on Multi-Flight Modes

With the burgeoning impact of artificial intelligence on the traditional UAV industry, the pursuit of autonomous UAV flight has emerged as a focal point of contemporary research. Addressing the imperative for advancing critical technologies in autonomous flight, this paper delves into the realm of UAV flight state recognition and trajectory prediction. Presenting an innovative approach focused on improving the precision of unmanned aerial vehicle (UAV) path forecasting via the identification of flight states, this study demonstrates its efficacy through the implementation of two prediction models. Firstly, UAV flight data acquisition was realized in this paper by the use of multi-sensors. Finally, two models for UAV trajectory prediction were designed based on machine learning methods and classical mathematical prediction methods, respectively, and the results before and after flight pattern recognition are compared. The experimental results show that the prediction error of the UAV trajectory prediction method based on multiple flight modes is smaller than the traditional trajectory prediction method in different flight stages.

Joint resource and trajectory optimization for secure UAV-based two-way relay system

In this paper, we analyze the average secrecy rate (ASR) of a two-way relay system based on an unmanned aerial vehicle (UAV). Specifically, a UAV serves as an aerial relay station, assisting bidirectional transmission between terrestrial users in the presence of terrestrial eavesdroppers. By optimally combining transmit power and UAV trajectory, the total ASR in the proposed system is maximized. The ASR problem, which involves a tight combination of optimal variables and is therefore difficult to solve directly, is effectively addressed through successive convex approximations and block coordinate descent techniques. Simulation results reveal that eavesdroppers with scattered positions are more dangerous than those with concentrated positions. Furthermore, despite the assistance of the UAV, the downlink ASR is still significantly lower than the uplink ASR. Moreover, the ASR in the proposed system is better than that in the benchmark systems.

Robust digital-twin airspace discretization and trajectory optimization for autonomous unmanned aerial vehicles

The infiltration of heterogenous fleets of autonomous Unmanned Aerial Vehicles (UAVs) in smart cities is leading to the consumerization of city air space which includes infrastructure creation of roads, traffic design, capacity estimation, and trajectory optimization. This study proposes a novel autonomous Advanced Aerial Mobility (AAM) logistical system for high density city centers. First, we propose a real-time 3D geospatial mining framework for LiDAR data to create a dynamically updated digital twin model. This enables the identification of viable airspace volumes in densely populated 3D environments based on the airspace policy/regulations. Second, we propose a robust city airspace dynamic 4D discretization method (Skyroutes) for autonomous UAVs to incorporate the underlying real-time constraints coupled with externalities, legal, and optimal UAV operation based on kinematics. An hourly trip generation model was applied to create 1138 trips in two scenarios comparing the cartesian discretization to our proposed algorithm. The results show that the AAM enables a precise airspace capacity/cost estimation, due to its detailed 3D generation capabilities. The AAM increased the airspace capacity by up to 10%, the generated UAV trajectories are 50% more energy efficient, and significantly safer.

Enhanced Multi-UAV Path Planning in Complex Environments With Voronoi-Based Obstacle Modelling and Q-Learning

To tackle the challenge of obstacle avoidance path planning for multiple unmanned aerial vehicles (UAVs) in intricate environments, this study introduces a Voronoi graph–based model to represent the obstacle-laden environment and employs a Markov decision process (MDP) for single UAV path planning. The traditional Q-learning algorithm is enhanced by adjusting the initial state of the Q-table and fine-tuning the reward and penalty values, enabling the acquisition of efficient obstacle avoidance paths for individual UAVs in complex settings. Leveraging the improved Q-learning algorithm for single UAVs, the Q-table is iteratively refined for a fleet of UAVs, with dynamic modifications based on the waypoints chosen by each UAV. This approach ensures the generation of collision-free paths for multiple UAVs, as validated by simulation results that showcase the algorithm’s effectiveness in learning from past training data. The proposed method offers a robust framework for practical UAV trajectory generation in complex environments.

IRS Integrated UAV Communication to Enhance Physical Layer Security: A Deep Reinforcement Learning approach

The paper investigates the security of an intelligent reflecting surface (IRS) assisted unmanned aerial vehicle (UAV) network, where a base station (BS) transmits confidential information to the ground user (GU) via IRS-assisted UAV. The study explores using UAVs with IRS to enhance the security and reliability of wireless communication systems, particularly in the presence of eavesdroppers and friendly jammers. The beamforming at IRS-assisted UAV and UAV trajectory are jointly formulated as a non-convex optimization problem, which is solved by the deep reinforcement learning (DRL) algorithm to maximize the sum secrecy rate of GU with the aid of jammer. We proposed a dual-DDPG (D3PG) algorithm that utilizes the deep deterministic policy gradient (DDPG) structure to effectively address these dual non-convex problems of the UAV-trajectory optimization and the UAV-IRS beamforming optimization. The proposed algorithm's effectiveness and robustness are demonstrated through simulation results, with the IRS significantly enhancing the sum secrecy rate. Extensive simulations show that the proposed DRL-based D3PG scheme outperforms the traditional optimization schemes and ordinary DQN schemes.

Reinforcement learning-based unmanned aerial vehicle trajectory planning for ground users’ mobility management in heterogeneous networks

The surge of data traffic in wireless networks necessitates the provision of high-quality data services to meet users’ satisfaction levels. However, the limited spectral resources of the current network infrastructures and inherent challenges of achieving reliable line-of-sight (LoS) probability for ground users (GUs) in urban environments often lead to disruption to communication services delivery. This paper aims to address the challenges of frequent handover (HO) failures and disrupted communication services for mobile GUs by deploying an unmanned aerial vehicle as a flying base station (UAV-BS) in heterogeneous networks (HetNets). A channel model is investigated that considers both LoS and non-line-of-sight (NLoS) paths in three-dimensional (3D) air-to-ground (A2G) links using a detailed mathematical model with urban infrastructure parameters like building density and heights. In addition, a reinforcement learning (RL) algorithm is presented in this work to optimize UAV trajectories in response to the dynamic mobility of GUs for enhancing LoS connections. The proposed algorithm dynamically adjusts the UAV positions and enhances transmission channels by identifying both LoS and NLoS paths. Simulation results demonstrate that the proposed algorithm outperforms existing benchmarks through learning-based adaptive control of UAVs’ mobility, ensuring ubiquitous network connectivity for GUs and reducing HO failures in HetNets.

Energy-efficient mobile edge computing assisted by layered UAVs based on convex optimization

The utilization of unmanned aerial vehicles (UAVs) as aerial mobile edge computing (MEC) servers presents an effective approach for mitigating the computational limitations of intelligent mobile devices. However, the limited battery capacity of UAVs poses a significant challenge in ensuring sustained service provision. To counter this obstacle and enhance energy efficiency, we introduce a partial offloading MEC scheme assisted by layered UAVs (POAL). Our objective is to minimize the total energy consumption of UAVs by jointly optimizing several factors, including wireless channels, transmit power, task partition, computing power, and UAV trajectories. Due to the non-convexity of the problem, we decompose it into three sub-problems and address them iteratively. Firstly, we employ the CVX optimization tool to allocate wireless channels and utilize Lagrangian duality to determine the optimal transmit power. Secondly, leveraging the monotonicity of the optimization objective, we derive the optimal computing power. Lastly, we employ the successive convex approximation (SCA) technique to tackle the trajectory planning sub-problem. Numerical results demonstrate that the proposed algorithm can effectively position UAVs within 15 iterations. Furthermore, our layered scheme significantly reduces overall energy consumption.

Low-Resolution Optimization for an Unmanned Aerial Vehicle Communication Network under a Passive Reconfigurable Intelligent Surface and Active Reconfigurable Intelligent Surface

This paper investigates the optimization of an unmanned aerial vehicle (UAV) network serving multiple downlink users equipped with single antennas. The network is enhanced by the deployment of either a passive reconfigurable intelligent surface (RIS) or an active RIS. The objective is to jointly design the UAV’s trajectory and the low-bit, quantized, RIS-programmable coefficients to maximize the minimum user rate in a multi-user scenario. To address this optimization challenge, an alternating optimization framework is employed, leveraging the successive convex approximation (SCA) method. Specifically, for the UAV trajectory design, the original non-convex optimization problem is reformulated into an equivalent convex problem through the introduction of slack variables and appropriate approximations. On the other hand, for the RIS-programmable coefficient design, an efficient algorithm is developed using a penalty-based approximation approach. To solve the problems with the proposed optimization, high-performance optimization tools such as CVX are utilized, despite their associated high time complexity. To mitigate this complexity, a low-complexity algorithm is specifically tailored for the optimization of passive RIS-programmable reflecting elements. This algorithm relies solely on closed-form expressions to generate improved feasible points, thereby reducing the computational burden while maintaining reasonable performance. Extensive simulations are created to validate the performance of the proposed algorithms. The results demonstrate that the active RIS-based approach outperforms the passive RIS-based approach. Additionally, for the passive RIS-based algorithms, the low-complexity variant achieves a reduced time complexity with a moderate loss in performance.

Multi-agent deep reinforcement learning for trajectory planning in UAVs-assisted mobile edge computing with heterogeneous requirements

In heterogeneous wireless networks, massive user equipments (UEs) generate computing tasks with time-varying heterogeneous requirements. To improve the service quality, this paper formulates a unmanned aerial vehicles (UAVs)-assisted mobile edge computing (MEC) framework for time-varying heterogeneous task requirements. In the framework, the task delay and the number of successfully executed tasks are optimized by jointly controlling the trajectories of multiple UAVs. To address the considered trajectory planning optimization problem, a collaborative multi-agent deep reinforcement learning (MADRL) algorithm is proposed, where each UAV is regarded as a learning agent. First, a counterfactual inference based personalized policy update mechanism is proposed to evaluate the independent policy of agents by comparing the policy with a designed counterfactual policy. Based on this idea, each agent updates a personalized policy from both group and individual interests to improve its cooperation ability in dynamic and complex environments. Then, a diversified experience sampling mechanism is proposed to enhance the efficiency of policy evaluation and update with rich experiences provided by the environment interaction and the modified whale optimization algorithm. Finally, evaluation results demonstrate the superiority and effectiveness of the proposed MADRL algorithm.

UAVs Path Planning based on Combination of Rapidly Exploring Random Tree and Rauch-Tung-Striebel Filter

Aiming at the problem of Unmanned Aerial Vehicle(UAV) formation path planning under complex constraints, a UAV formation path planning method based on the combination of Rapidly exploring Random Tree (RRT) and Rauch-Tung-Striebel (RTS) filter is proposed. Firstly, a path planning algorithm based on the improved RRT algorithm with adaptive step size is de-signed to solve the problem that the RRT algorithm is easy to fall into local optimum. Then, an RTS filter is introduced to smooth the trajectory planned by the improved RRT algorithm to achieve curvature continuity. Finally, taking the smooth trajectory as the reference, a UAV formation path planning algorithm over the Artificial Potential Field (APF) method is designed. The simulation results show that the designed UAV formation path planning algorithm can solve the planning problems of single trajectory and formation trajectories in complex constrained space, and can plan the formation trajectory with continuous curvature, to facilitate the UAV trajectory tracking control.

Unlocking the Potential of Mobile-Edge Cloud: A Comprehensive Review and Future Directions

With the rapid expansion of the Internet of Things (IoT), the demand for computational resources continues to soar, necessitating innovative solutions to address the needs of resource-constrained IoT users. Mobile edge computing (MEC) emerges as a promising remedy, mitigating the strain imposed by resource-intensive mobile applications. Concurrently, leveraging unmanned aerial vehicles (UAVs) as aerial platforms presents an enticing opportunity to enhance connectivity in wireless networks, owing to their on-demand deployment capabilities, high cruising altitudes, and maneuverability in three-dimensional space. This paper presents a comprehensive examination of UAV-enabled aerial MEC, elucidating its advantages, challenges, and recent advancements across various domains. Topics explored include joint optimization of UAV trajectory, computation offloading, and resource allocation, UAV deployment strategies, task scheduling, load balancing, interplay with other technologies, and machine learning-driven optimizations. Additionally, the paper outlines key avenues for future research endeavors.

Unlocking the Potential of Mobile-Edge Cloud: A Comprehensive Review and Future Directions

With the rapid expansion of the Internet of Things (IoT), the demand for computational resources continues to soar, necessitating innovative solutions to address the needs of resource-constrained IoT users. Mobile edge computing (MEC) emerges as a promising remedy, mitigating the strain imposed by resource-intensive mobile applications. Concurrently, leveraging unmanned aerial vehicles (UAVs) as aerial platforms presents an enticing opportunity to enhance connectivity in wireless networks, owing to their on-demand deployment capabilities, high cruising altitudes, and maneuverability in three-dimensional space. This paper presents a comprehensive examination of UAV-enabled aerial MEC, elucidating its advantages, challenges, and recent advancements across various domains. Topics explored include joint optimization of UAV trajectory, computation offloading, and resource allocation, UAV deployment strategies, task scheduling, load balancing, interplay with other technologies, and machine learning-driven optimizations. Additionally, the paper outlines key avenues for future research endeavors.

Quadrotor UAV Trajectory Tracking Control Based on Adaptive Non-singular Terminal Sliding Mode

To address the issue of the quadrotor unmanned aerial vehicle (UAV) being susceptible to external disturbances and model uncertainties during flight, an adaptive non-singular terminal sliding mode controller with disturbance compensation is proposed. Firstly, the quadrotor UAV dynamic model considering system disturbances is constructed using the Newton-Euler method. Next, the trajectory tracking problem is transformed into a command tracking control problem for each channel. To enhance the adaptability and robustness of trajectory tracking for the quadrotor UAV, an adaptive law is designed to dynamically adjust the sliding mode surface gain. In addition, a disturbance observer is introduced to estimate and compensate for external disturbances and model uncertainties. Finally, the stability of the designed controller is proven through the Lyapunov function. Simulation results demonstrate that the proposed controller effectively enhances the disturbance resistance and trajectory tracking response speed of the quadrotor UAV.