UAV Power Research Articles

Joint UAV Trajectory and Power Allocation With Hybrid FSO/RF for Secure Space–Air–Ground Communications

Joint UAV Trajectory and Power Allocation With Hybrid FSO/RF for Secure Space–Air–Ground Communications

UPKD: Unsupervised pylon keypoint detection from 3D LiDAR data for autonomous UAV power inspection

The automatic extraction of inspection points for pylons is essential for intelligent Unmanned Aerial Vehicle (UAV) power inspections, especially in generating inspection route. However, current researches primarily focus on power scene perception and power component fault detection, with little attention paid to the inspection point detection. The primary reason are the lack of public pylon datasets for keypoint detection and the neglect of distinguish the inspection points from the keypoints used in feature extraction. Regarding that, this paper proposes a two-stage unsupervised pylon keypoint detection (UPKD) method to improve the efficiency of power inspections. In the first stage, the UPKD Network processes the point cloud to generate candidate keypoints, which comprises two main components: a data normalization module and an unsupervised keypoint detection network (UKD-Net). The data normalization module compresses information based on the symmetric structure of pylons, thereby reducing instability in inspection point detection. The UKD-Net incorporates a Point Transformer layer that uses self-attention mechanisms to extract features from the point cloud. In the second stage, a convex optimization strategy is applied to filter and acquire inspection points. These inspection points are then interconnected using a shortest-path strategy to generate the UAV inspection route. Our dataset is obtained using the Riegl VUX-1 laser measurement system and comprises 3,296 pylons of 10 types. Each pylon’s point cloud contains up to 25,000 points, with a high point density of 100 pts/m2. Extensive experiments show that the UPKD Network achieved state-of-the-art performance on our dataset, with repeatability achieving 90.39%, effectiveness (the ratio of effective keypoints to annotation points) achieving 69.95%, and completeness (the ratio of detected annotation points to keypoints) achieving 79.55%.

UAV Trajectory, User Association, and Power Control for Multi-UAV-Enabled Energy-Harvesting Communications: Offline Design and Online Reinforcement Learning

UAV Trajectory, User Association, and Power Control for Multi-UAV-Enabled Energy-Harvesting Communications: Offline Design and Online Reinforcement Learning

Layerwise Quantum Deep Reinforcement Learning for Joint Optimization of UAV Trajectory and Resource Allocation

This study proposes a layerwise quantum-based deep reinforcement learning (LQ-DRL) method for optimizing continuous large space and time series problems using deep layer training. The actions in LQ-DRL are optimized using a layerwise quantum embedding that leverages the advantages of quantum computing to maximize reward and reduce training loss. Moreover, this study employs a local loss to minimize the occurrence of barren plateaus phenomena and further enhance performance. As a particular case, the proposed scheme is employed to jointly optimize: (1) UAV trajectory planning, (2) user grouping, and (3) power allocation for higher energy efficiency of a UAV as the reward. The combination of these optimized factors is referred to as action space in the presented LQ-DRL. The LQ-DRL is employed to solve the optimization problem due to its non-convexity, continuous and large action space, and time-series domain. In a practical view, LQ-DRL aims to solve the issue of energy consumption related to limited-battery energy of a UAV base station while maintaining quality-of-service (QoS) for users, by gaining maximum energy efficiency as the reward. One of real applications, as an example, LQ-DRL can be employed to maximize the energy efficiency of a UAV base station in UAV empowered disaster recovery networks scenario. The quantum circuits of layerwise quantum embedding are presented to show the practical implementation in noisy intermediate-scale quantum computers. Based on the results, LQ-DRL outperformed the classical DRL by achieving higher effective dimension, rewards, and lower learning losses. In addition, better performances were achieved using more layers.

Performance Analysis Based on Desert Rescue UAV Power Battery in High Temperature Environment

The main purpose of this paper is to analyze and discuss the performance of the lithium iron phosphate battery and the new lithium titanate battery commonly used in UAV that affect the endurance in high temperature environment. This paper first introduces the functions of desert rescue UAV and the development status of UAV power battery technology. This paper first points out the main factors that affect their endurance at high temperature, expounds the reasons for the failure of lithium-ion batteries and high-temperature polymer batteries at high temperature, and introduces the materials and general performance characteristics of the two types of batteries, and then reviews the methods and results of various studies on the performance of lithium-ion batteries and lithium polymer batteries at high temperature. Finally, this paper points out the shortcomings of the existing research and points out the direction for the future development. By comparing the working performance of the two batteries at high temperature and combining with the practical problems that need to be considered in production and use, this paper puts forward selection suggestions according to the existing conditions, so that later workers can choose the type of UAV power battery according to different working environments.

Analysis of the Current Status of UAV Power Sources

UAVs have been applied in various industries and fields, and have an indispensable role. UAV power system is the key to the normal work of the UAV, this paper focuses on the summary introduction of lithium-ion battery, internal combustion engine, solar energy, fuel cell and hybrid power of the working principle, domestic and international research status, key technologies, advantages and disadvantages, part of the classification and application of the power system, and UAV power of the future development and outlook and so on.

Current status of power system for unmanned aerial vehicle fuel cell under different scenarios

With the increasingly wide application of the unmanned aerial vehicle, a new power resource is in urgent demand in the market. Domestic and foreign researchers have also done much research on how to improve the power system. Considering the energy density, gravimetric specific power, economic benefits and environmental protection, fuel cell becomes the best choice to solve the problem of unmanned aerial vehicle endurance. The gravimetric specific power of fuel cells is low, and it is difficult to cope with the large range fluctuation of load power when the UAV performs complex flight tasks. After comparing the performance and products of various types of fuel cells, the cathode open air-cooled proton exchange membrane fuel cell is chosen as the most suitable one for UAVs. This paper will illustrate how fuel cell and battery hybrid power system works and introduce their structure. At the end of the paper, some conclusions and prospects for the development of UAV power systems are also made.

Deep Reinforcement Learning Based Resource Allocation and Trajectory Planning in Integrated Sensing and Communications UAV Network

In this paper, multi-UAVs serve as mobile aerial ISAC platforms to sense and communicate with on-ground target users. To optimize the communication and sensing performance, we formulate a joint user association, UAV trajectory planning and power allocation problem to maximize the minimum weighted spectral efficiency among UAVs. This paper exploits the centralized and the decentralized deep reinforcement learning (DRL) solutions to solve the sequential decision-making problem. On one hand, we first introduce the centralized soft actor-critic (SAC) algorithm. Then, we explore the equivalent transformation of the optimization objective based on symmetric group, propose the random and the adaptive data augmentation schemes to design the replay memory buffer of SAC, and accordingly propose SAC algorithms assisted by data augmentation to tackle the transformed problem. On the other hand, the multi-agent soft actor-critic (MASAC), a decentralized solution, is also introduced to solve this sequential decision-making problem. The experiment results reveal the effectiveness of the centralized and the decentralized solutions in considered scenarios. Specifically, the SAC assisted by the adaptive scheme significantly outperforms other centralized solutions in the training speed and the weighted spectral efficiency. Meanwhile, the decentralized MASAC algorithm behaves best in the early training speed.

Data-similarity-based IoT node selection for UAV trajectory optimization

Data collected from densely deployed IoT sensor nodes via UAVs may incur remarkable redundancy, unnecessarily wasting the UAV power. This paper introduces a framework of UAV trajectory planning for optimizing energy efficiency via data-similarity-based node selection while maintaining sufficient information integrity out of the refined data. The proposed framework consists of three phases, namely data similarity determination, redundant nodes removal, and UAV trajectory planning. In particular, we propose a sliding-window dynamic time warping (SDTW) algorithm to quantify the data similarity between nodes. Then a hybrid genetic ant colony algorithm (HGACA) is introduced for the redundant node removal, where data similarity and UAV energy consumption are jointly considered. Finally, we formulate the trajectory planning problem as a three-stage integer linear programming (ILP) model, which clusters the nodes with minimal overlap and finds the shortest UAV cruising path that traverses each cluster head once and only once. The simulation result demonstrates that the proposed framework outperforms all the considered counterparts under various threshold values of data similarity in terms of execution time and power consumption while maintaining information integrity.

Research on the Endurance Optimisation of Multirotor UAVs for High-Altitude Environments

Multirotor UAVs are becoming increasingly important in both civilian and military fields. In the plateau environment, the endurance time of multirotor UAVs is still too short for mission requirements. Thus, the optimisation of the efficiency of the power system is considered to be an effective way to overcome this problem. This paper presents a practical method of power system design to maximise endurance time in a plateau environment. First, the optimal proportion of battery quality is analysed from the perspective of energy consumption of multirotor UAVs. Second, the modelling method of each component of the power system is studied separately. Then, the endurance solution method is given, the multirotor UAV power system is optimised and analysed according to the given design requirements, and the effectiveness and practicability of the method are verified through experiments.

MAESTRO-X: Distributed Orchestration of Rotary-Wing UAV-Relay Swarms

This work details a scalable framework to orchestrate a swarm of rotary-wing UAVs serving as cellular relays to facilitate beyond line-of-sight connectivity and traffic offloading for ground users. First, a Multiscale Adaptive Energyconscious Scheduling and TRajectory Optimization (MAESTRO) framework is developed for a single UAV. Aiming to minimize the time-averaged latency to serve user requests, subject to an average UAV power constraint, it is shown that the optimization problem can be cast as a semi-Markov decision process, and exhibits a multiscale structure: outer actions on radial wait velocities and terminal service positions minimize the longterm delay-power trade-off, optimized via value iteration; given these outer actions, inner actions on angular wait velocities and service trajectories minimize a short-term delay-energy cost; finally, rate adaptation is embedded along the trajectory to leverage air-to-ground channel propagation conditions. A novel hierarchical competitive swarm optimization scheme is developed in the inner optimization, to devise high-resolution trajectories via iterative pair-wise updates. Next, MAESTRO is eXtended to UAV swarms (MAESTRO-X) via scalable policy replication, enabled by a decentralized command-and-control network augmented with: (1) spread maximization to proactively position UAVs to serve future requests; (2) consensus-driven conflict resolution to orchestrate scheduling decisions based on delay-energy costs including queuing dynamics; (3) adaptive frequency reuse to improve spectrum utilization across the network; and (4) a piggybacking mechanism allowing UAVs to serve multiple ground users simultaneously. Numerical evaluations show that, for user requests of 10 Mbits, generated according to a Poisson arrival process with rate 0.2 req/min/UAV, single-agent MAESTRO offers 3.8× faster service than a high-altitude platform and 29% faster than a static UAV deployment; moreover, for a swarm of 3 UAV-relays, MAESTRO-X delivers data payloads 4.7× faster than a successive convex approximation scheme; and remarkably, a single UAV optimized via MAESTRO outclasses 3 UAVs optimized via a deep-Q network by 38%.

Joint Trajectories and Resource Allocation Design for Multi-UAV-Assisted Wireless Power Transfer with Nonlinear Energy Harvesting

In this work, we explore a multi-UAV-assisted wireless power transfer (WPT) network, where multiple UAVs are deployed to provide WPT services to multiple ground devices (GDs) in order to extend their lifespan. To enhance the WPT efficiency while considering fairness, we investigate the joint trajectories and transmit power design. For fairness-aware consideration, our objective is to maximize the harvested energy of the GD with the worst condition, taking into account UAV mobility, anti-collision, and power budget constraints. Unlike previous works that focus on the simplified linear energy harvesting (EH) model, a more accurate multi-source nonlinear EH model is, for the first time, adopted to formulate the problem. Given the highly non-convex nature of the original problem due to the presence of coupled variables, we leverage the convexity of the multi-source nonlinear EH model and introduce a convex approximation method, which enables us to construct a tightly convex problem in each iteration for the original joint design problem, thereby obtaining a high-quality solution. Finally, we present numerical results to showcase the convergence of our algorithm and validate the performance advantages of the proposed multi-UAV WPT scheme with a nonlinear EH model versus benchmarks.

Unmanned aerial vehicle power system performance test platform design

To achieve high-frequency and high-precision measurement of the performance of the UAV power system, a set of devices that can realize tensile and torque decoupling tests were designed and built, and a design scheme for the UAV power test was given. On this basis, the design of the data acquisition system and signal conditioning system with the data acquisition board as the core and the development of LabVIEW test software were completed. Finally, the debugging test of each system verified the functions of tensile force, torque separation measurement, data acquisition, host computer display, preservation, etc. It realized the high-frequency and high-precision measurement of the performance of the UAV power system. . The test results showed that the output signal of the test bench was highly linear, and the repeatability was good.

Analysis of Laser Cell Response Characteristics under Different Irradiation Conditions

Although the application of laser wireless energy transmission technology in many fields such as UAV power supply is increasing, the laser incidence angle and beam shift remain the key factors limiting the efficiency of long-range laser wireless energy transmission. In this study, a laser cell response test platform was built to measure and analyze the response characteristics of a laser cell under different laser incidence angles and beam shifts. The results show that the increase in the incident angle intensifies the reflection on the irradiated surface, resulting in a linear decrease in the power density received by the laser cell, which eventually leads to a significant decrease in the output power, and the output power tends to be close to 0 when the incident angle exceeds 75°. The increase in the beam offset distance increases the reverse bias of the cell, which is the main reason for the significant decrease in the output power. The local irradiation also leads to an increase in the heat generation power; when the beam coverage is below 50%, the overall output power tends to be close to 0. This study provides a reference for improving the laser wireless energy transmission efficiency and laser cell optimization.

Trajectory Optimization and Power Allocation for Cell-Free Satellite-UAV Internet of Things

Massive access outside the coverage of terrestrial cellular networks will be the main feature of the sixth generation (6G) networks. In order to cope with it, the cognitive satellite-UAV network (CSUN) has drawn a lot of attentions. In this paper, we investigate the UAV trajectory optimization and power allocation for the cell-free CSUN consisting of one satellite and a swarm of UAVs. Indeed, due to the on-board energy constraints of UAVs, both the trajectory optimization and the power allocation can significantly save the energy to improve the energy efficiency. The joint trajectory optimization and power allocation problem is formulated as a mixed-integer non-convex optimization problem which is extremely difficult to solve. In order to reduce the computational complexity, we decompose the original optimization problem into two subproblems in terms of the trajectory optimization and power allocation. For the trajectory optimization subproblem, we model it as a Traveling Salesman Problem (TSP), and the PSO is adopted to solve it. When the trajectory variables are fixed, the power allocation subproblem is still difficult to tackle due to its non-convexity and large scale. Firstly, we present a kind of centralized algorithm in which the DC (difference of two convex functions) algorithm is applied to optimize it, and then a distributed algorithm based on auxiliary variables is proposed to reduce the signaling overhead and computational complexity. The simulation results demonstrate the effectiveness of the proposed joint trajectory optimization and power allocation algorithm for the cell-free CSUN.

Visual-based Assistive Method for UAV Power Line Inspection and Landing

Visual-based Assistive Method for UAV Power Line Inspection and Landing

Array design for mm-wave UAV-assisted cooperative communications with simultaneous wireless information and power transmission

High performance UAV-assisted communications system using simultaneous wireless information and power transmission (SWIPT) in mm-wave band is presented. UAV is a moving relay powered from a ground source through a power-splitting mechanism. In mm-wave band we utilize antenna array to increase the antenna gain while keeping array size small and practical. The radiation pattern of the UAV antenna is continuously adjusted to peak towards the source and destination. Two array geometries, a line and a cross, are designed for UAV antenna. We achieve near optimal pattern, utilizing innovative low power switches instead of phase shifters which are high power consuming components and using them here defies the purpose. We maximize the end-to-end cooperative throughput by optimizing the UAV power profile, power-splitting ratio profile, antenna weights (0, 1), and UAV trajectory for amplify-and-forward (AF) protocol. We consider two cases. Case1: UAV transmits and receives data simultaneously along two predefined trajectories. The antenna weights for line and cross arrays are optimized utilizing genetic algorithm. The power profile and, power-splitting ratio profile are also optimized using the penalty method. Case2: UAV accumulates the data and power along an optimal trajectory until it reaches the vicinity of target, when it transmits data at high bit rate. Here we define the optimization of parameters mentioned in Case1, while at an optimal point along the trajectory, as sub-problem1, and finding the next optimal point as sub-problem2. Sub-problem1 is solved using the genetic algorithm and dual decomposition method. Sub-problem2 is then solved using successive concave optimization. The overall problem, i.e. cooperative throughput, is solved by reciprocal iteration over the two sub problems. The simulation results show the proposed mm-wave band cross array antenna and switches can overcome the high frequency propagation losses, hence, achieving higher power harvest and data rates. The achieved higher data throughput outperforms the conventional single antenna low frequency systems.

Capacity Optimization of Next-Generation UAV Communication Involving Non-Orthogonal Multiple Access

Unmanned air vehicle communication (UAV) systems have recently emerged as a quick, low-cost, and adaptable solution to numerous challenges in the next-generation wireless network. In particular, UAV systems have shown to be very useful in wireless communication applications with sudden traffic demands, network recovery, aerial relays, and edge computing. Meanwhile, non-orthogonal multiple access (NOMA) has been able to maximize the number of served users with the highest traffic capacity for future aerial systems in the literature. However, the study of joint optimization of UAV altitude, user pairing, and power allocation for the problem of capacity maximization requires further investigation. Thus, a capacity optimization problem for the NOMA aerial system is evaluated in this paper, considering the combination of convex and heuristic optimization techniques. The proposed algorithm is evaluated by using multiple heuristic techniques and deployment scenarios. The results prove the efficiency of the proposed NOMA scheme in comparison to the benchmark technique of orthogonal multiple access (OMA). Moreover, a comparative analysis of heuristic techniques for capacity optimization is also presented.

Dual-View Stereovision-Guided Automatic Inspection System for Overhead Transmission Line Corridor

Overhead transmission line corridor detection is important to ensure the safety of power facilities. Frequent and uncertain changes in the transmission line corridor environment requires an efficient and autonomous UAV inspection system, whereas the existing UAV-based inspection systems has some shortcomings in control model and ground clearance measurement. For one thing, the existing manual control model has the risk of striking power lines because it is difficult for manipulators to judge the distance between the UAV fuselage and power lines accurately. For another, the ground clearance methods based on UAV usually depend on LiDAR (Light Detection and Ranging) or single-view visual repeat scanning, with which it is difficult to balance efficiency and accuracy. Aiming at addressing the challenging issues above, a novel UAV inspection system is developed, which can sense 3D information of transmission line corridor by the cooperation of the dual-view stereovision module and an advanced embedded NVIDIA platform. In addition, a series of advanced algorithms are embedded in the system to realize autonomous control of UAVs and ground clearance measurement. Firstly, an edge-assisted power line detection method is proposed to locate the power line accurately. Then, 3D reconstruction of the power line is achieved based on binocular vision, and the target flight points are generated in the world coordinate system one-by-one to guide the UAVs movement along power lines autonomously. In order to correctly detect whether the ground clearances are in the range of safety, we propose an aerial image classification based on a light-weight semantic segmentation network to provide auxiliary information categories of ground objects. Then, the 3D points of ground objects are reconstructed according to the matching points set obtained by an efficient feature matching method, and concatenated with 3D points of power lines. Finally, the ground clearance can be measured and detected according to the generated 3D points of the transmission line corridor. Tests on both corresponding datasets and practical 220-kV transmission line corridors are conducted. The experimental results of different modules reveal that our proposed system can be applied in practical inspection environments and has good performance.

DCPLD-Net: A diffusion coupled convolution neural network for real-time power transmission lines detection from UAV-Borne LiDAR data

The stable and reliable supply of electric power is strongly related to the normal social production. In recent years, power transmission lines inspection based on remote sensing methods has made great progress, especially using the UAV-borne LiDAR system. The extraction and identification of power transmission lines (i.e., conductors) from 3D point clouds are the basis of LiDAR data-based power grid risk management. However, existing rule-based/traditional machine learning extraction approaches have exposed some limitations, such as the lack of timeliness and generalization. Moreover, the potential of deep learning is seriously overlooked in RoW (Right of Way) LiDAR inspection tasks. Thus, we proposed DCPLD-Net: a diffusion coupled convolution neural network for real-time power transmission lines detection from UAV-borne LiDAR data. To implement efficient 2D convolution on 3D point clouds, we proposed a novel point cloud representation, named Cross Section View (CSV), which transforms the discrete point clouds into 3D tensors constructed by voxels with a deformed geometric shape along the flight trajectory. After the CSV feature generation, the encoded features of each voxel are treated as energy (i.e., heat) signals and diffused in space to generate diffusion feature maps. The feature of the power line points are thus enhanced through this simulated physical process (diffusion). Finally, a single-stage detector named PLDNet is proposed for the multiscale detection of conductors on the diffused CSV representations. The experimental results show that the DCPLD-Net achieves an average F1 score of 97.14 % at 8 Hz detection frequency on RoWs inspection LiDAR datasets collected by both mini-UAV LiDAR and large-scale fully autonomous UAV power lines inspection robots, and surpasses compared methods (i.e. PointNet ++, RandLA-Net) in terms of F1 scores and IoU.