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Drones, Volume 6, Issue 12 (December 2022) – 61 articles

Cover Story (view full-size image): Drones for planetary exploration missions require increased onboard mission-planning abilities to access their full operational potential in remote environments (e.g., Antarctica, Mars, or Titan). Limitations in available sensors and environmental uncertainty present challenges for planning such missions. We used partially observable Markov decision processes (POMDPs) to formulate mission planning with uncertainty. Our UAV4PE framework for autonomous mission planning uses Python, C++, ROS, PX4, and JuliaPOMDP to plan and execute planetary missions in simulation, emulation, and real-world experiments. The source code and the experiment data are included in the UAV4PE framework. View this paper
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16 pages, 2915 KiB  
Article
Formation Control Algorithm of Multi-UAVs Based on Alliance
by Yan Jiang, Tingting Bai and Yin Wang
Drones 2022, 6(12), 431; https://doi.org/10.3390/drones6120431 - 19 Dec 2022
Cited by 3 | Viewed by 2458
Abstract
Among the key technologies of Multi-Unmanned Aerial Vehicle (UAV) leader–follower formation control, formation reconfiguration technology is an important element to ensure that multiple UAVs can successfully complete their missions in a complex operating environment. This paper investigates the problem of formation reconfiguration due [...] Read more.
Among the key technologies of Multi-Unmanned Aerial Vehicle (UAV) leader–follower formation control, formation reconfiguration technology is an important element to ensure that multiple UAVs can successfully complete their missions in a complex operating environment. This paper investigates the problem of formation reconfiguration due to battlefield mission requirements. Firstly, in response to the mission requirements, the article proposes the Ant Colony Pheromone Partitioning Algorithm to subgroup the formation. Secondly, the paper establishes the alliance for the obtained subgroups. For the problem of no leader within the alliance formed after grouping or reconfiguring, the Information Concentration Competition Mechanism is introduced to flexibly select information leaders. For the problem of the stability of alliance structure problem, the control law of the Improved Artificial Potential Field method is designed, which can effectively form a stable formation to avoid collision of UAVs in the alliance. Thirdly, the Lyapunov approach is employed for convergence analysis. Finally, the simulation results of multi-UAV formation control show that the partitioning algorithm and the competition mechanism proposed can form a stable alliance as well as deal with the no-leader in it, and the improved artificial potential field designed can effectively avoid collision of the alliance and also prove the highly efficient performance of the algorithm in this paper. Full article
(This article belongs to the Special Issue Swarm Intelligence in Multi-UAVs)
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29 pages, 7545 KiB  
Article
Multidomain Joint Learning of Pedestrian Detection for Application to Quadrotors
by Yuan-Kai Wang, Jonathan Guo and Tung-Ming Pan
Drones 2022, 6(12), 430; https://doi.org/10.3390/drones6120430 - 19 Dec 2022
Cited by 1 | Viewed by 2279
Abstract
Pedestrian detection and tracking are critical functions in the application of computer vision for autonomous driving in terms of accident avoidance and safety. Extending the application to drones expands the monitoring space from 2D to 3D but complicates the task. Images captured from [...] Read more.
Pedestrian detection and tracking are critical functions in the application of computer vision for autonomous driving in terms of accident avoidance and safety. Extending the application to drones expands the monitoring space from 2D to 3D but complicates the task. Images captured from various angles pose a great challenge for pedestrian detection, because image features from different angles tremendously vary and the detection performance of deep neural networks deteriorates. In this paper, this multiple-angle issue is treated as a multiple-domain problem, and a novel multidomain joint learning (MDJL) method is proposed to train a deep neural network using drone data from multiple domains. Domain-guided dropout, a critical mechanism in MDJL, is developed to self-organize domain-specific features according to neuron impact scores. After training and fine-tuning the network, the accuracy of the obtained model improved in all the domains. In addition, we also combined the MDJL with Markov decision-process trackers to create a multiobject tracking system for flying drones. Experiments are conducted on many benchmarks, and the proposed method is compared with several state-of-the-art methods. Experimental results show that the MDJL effectively tackles many scenarios and significantly improves tracking performance. Full article
(This article belongs to the Special Issue Intelligent Recognition and Detection for Unmanned Systems)
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21 pages, 9048 KiB  
Article
High-Resolution Terrain Reconstruction of Slot Canyon Using Backpack Mobile Laser Scanning and UAV Photogrammetry
by Yonghui Xin, Ran Wang, Xi Wang, Xingwei Wang, Zhouxuan Xiao and Jingyu Lin
Drones 2022, 6(12), 429; https://doi.org/10.3390/drones6120429 - 19 Dec 2022
Cited by 1 | Viewed by 1947
Abstract
Accurate terrain models are critical for studying the formation and development of slot canyons. However, for slot canyon landforms, it is challenging to generate comprehensive and high-resolution morphological data by individual observation due to the inaccessibility of steep walls on either side and [...] Read more.
Accurate terrain models are critical for studying the formation and development of slot canyons. However, for slot canyon landforms, it is challenging to generate comprehensive and high-resolution morphological data by individual observation due to the inaccessibility of steep walls on either side and the complexity of the field observation environment, such as variable-slope terrain, partial vegetation cover, and lack of satellite signal. Off-the-shelf surveying techniques, including Unmanned Aerial Vehicles (UAV) photogrammetry and Backpack Mobile Laser Scanning (BMLS), facilitate slot canyon surveys and provide better observations. This paper proposes an integrated scheme to generate comprehensive and centimeter-resolution slot canyon terrain datasets (e.g., color point clouds, Digital Elevation Models (DEM), and 3D mesh) using BMLS and fine UAV photogrammetry. The results show that the fine flight of UAVs based on a rough model can avoid collision with obstacles or flying into restricted areas, allowing users to perform tasks faster and safer. Data integration of BMLS and UAV photogrammetry can obtain accurate terrain datasets with a Root Mean Squared Error (RMSE) of point cloud registration of 0.028 m. Such high-resolution integration terrain datasets reduce local data shadows produced solely by individual datasets, providing a starting point to revealing morphological evolution and genesis of slot canyons. Full article
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22 pages, 2982 KiB  
Article
Practically Robust Fixed-Time Convergent Sliding Mode Control for Underactuated Aerial Flexible JointRobots Manipulators
by Kamal Rsetam, Zhenwei Cao, Lulu Wang, Mohammad Al-Rawi and Zhihong Man
Drones 2022, 6(12), 428; https://doi.org/10.3390/drones6120428 - 19 Dec 2022
Cited by 11 | Viewed by 1880
Abstract
The control of an aerial flexible joint robot (FJR) manipulator system with underactuation is a difficult task due to unavoidable factors, including, coupling, underactuation, nonlinearities, unmodeled uncertainties, and unpredictable external disturbances. To mitigate those issues, a new robust fixed-time sliding mode control (FxTSMC) [...] Read more.
The control of an aerial flexible joint robot (FJR) manipulator system with underactuation is a difficult task due to unavoidable factors, including, coupling, underactuation, nonlinearities, unmodeled uncertainties, and unpredictable external disturbances. To mitigate those issues, a new robust fixed-time sliding mode control (FxTSMC) is proposed by using a fixed-time sliding mode observer (FxTSMO) for the trajectory tracking problem of the FJR attached to the drones system. First, the underactuated FJR is comprehensively modeled and converted to a canonical model by employing two state transformations for ease of the control design. Then, based on the availability of the measured states, a cascaded FxTSMO (CFxTSMO) is constructed to estimate the unmeasurable variables and lumped disturbances simultaneously in fixed-time, and to effectively reduce the estimation noise. Finally, the FxTSMC scheme for a high-order underactuated FJR system is designed to guarantee that the system tracking error approaches to zero within a fixed-time that is independent of the initial conditions. The fixed-time stability of the closed-loop system of the FJR dynamics is mathematically proven by the Lyapunov theorem. Simulation investigations and hardware tests are performed to demonstrate the efficiency of the proposed controller scheme. Furthermore, the control technique developed in this research could be implemented to the various underactuated mechanical systems (UMSs), like drones, in a promising way. Full article
(This article belongs to the Special Issue Bioinspiration, Biomimicry, and Soft Robotics of Drones)
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6 pages, 3276 KiB  
Communication
A New Method for Surveying the World’s Smallest Class of Dragonfly in Wetlands Using Unoccupied Aerial Vehicles
by Hideyuki Niwa and Takumi Hirata
Drones 2022, 6(12), 427; https://doi.org/10.3390/drones6120427 - 17 Dec 2022
Cited by 2 | Viewed by 1435
Abstract
Field surveys in wetlands are limited by the difficulty in accessing the site, hazards during surveys, and the risk of disturbing the ecosystem. Thus, the use of unoccupied aerial vehicles (UAVs) can overcome these limiting factors and can assist in monitoring small organisms, [...] Read more.
Field surveys in wetlands are limited by the difficulty in accessing the site, hazards during surveys, and the risk of disturbing the ecosystem. Thus, the use of unoccupied aerial vehicles (UAVs) can overcome these limiting factors and can assist in monitoring small organisms, such as plants and insects, that are unique to wetlands, aiding in wetland management and conservation. This study aimed to demonstrate the effectiveness of a survey method that uses a small drone equipped with a telephoto lens to monitor dragonflies, which are unique to wetlands and have been difficult to survey quantitatively, especially in large wetlands. In this study, the main target species of dragonflies was Nannophya pygmaea, which is the world’s smallest dragonfly (about 20 mm long). The study area was Mizorogaike wetland (Kita Ward, Kyoto City, Japan). The UAV was flown at a low speed at an altitude of 4 m to 5 m, and images were taken using 7× telephoto lens on Mavic 3 (7× optical and 4× digital). A total of 107 dragonflies of seven species were identified from the photographs taken by the drone. N. pygmaea, about 20 mm long, was clearly identified. Eighty-five dragonflies belonging to N. pygmaea were identified from the images. Thus, by using a small drone equipped with a telephoto lens, the images of N. pygmaea were captured, and the effects of downwash and noise were reduced. The proposed research method can be applied to large wetlands that are difficult to survey in the field, and can thus provide new and important information pertaining to wetland management and conservation. This research method is highly useful for monitoring wetlands as it is non-invasive, does not require the surveyor to enter the wetland, requires little research effort, and can be repeated. Full article
(This article belongs to the Special Issue Ecological Applications of Drone-Based Remote Sensing-II)
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21 pages, 3209 KiB  
Article
Experimentally Determining Optimal Conditions for Mapping Forage Fish with RPAS
by Nicola R. Houtman, Jennifer Yakimishyn, Mike Collyer, Jennifer Sutherst, Cliff L. K. Robinson and Maycira Costa
Drones 2022, 6(12), 426; https://doi.org/10.3390/drones6120426 - 17 Dec 2022
Viewed by 1453
Abstract
RPAS (Remotely piloted aircraft systems, i.e., drones) present an efficient method for mapping schooling coastal forage fish species that have limited distribution and abundance data. However, RPAS imagery acquisition in marine environments is highly dependent on suitable environmental conditions. Additionally, the size, color [...] Read more.
RPAS (Remotely piloted aircraft systems, i.e., drones) present an efficient method for mapping schooling coastal forage fish species that have limited distribution and abundance data. However, RPAS imagery acquisition in marine environments is highly dependent on suitable environmental conditions. Additionally, the size, color and depth of forage fish schools will impact their detectability in RPAS imagery. In this study, we identified optimal and suboptimal coastal environmental conditions through a controlled experiment using a model fish school containing four forage fish-like fishing lures. The school was placed at 0.5 m, 1.0 m, 1.5 m, and 2.0 m depths in a wide range of coastal conditions and then we captured RPAS video imagery. The results from a cluster analysis, principal components, and correlation analysis of RPAS data found that the optimal conditions consisted of moderate sun altitudes (20–40°), glassy seas, low winds (<5 km/h), clear skies (<10% cloud cover), and low turbidity. The environmental conditions identified in this study will provide researchers using RPAS with the best criteria for detecting coastal forage fish schools. Full article
(This article belongs to the Special Issue Drones for Biodiversity Conservation)
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21 pages, 4190 KiB  
Article
A Group Handover Scheme for Supporting Drone Services in IoT-Based 5G Network Architectures
by Emmanouil Skondras, Ioannis Kosmopoulos, Emmanouel T. Michailidis, Angelos Michalas and Dimitrios D. Vergados
Drones 2022, 6(12), 425; https://doi.org/10.3390/drones6120425 - 17 Dec 2022
Cited by 3 | Viewed by 2253
Abstract
Next generation mobile networks are expected to integrate multiple drones organized in Flying Ad Hoc Networks (FANETs) to support demanding and diverse services. The highly mobile drones should always be connected to the network in order to satisfy the strict requirements of upcoming [...] Read more.
Next generation mobile networks are expected to integrate multiple drones organized in Flying Ad Hoc Networks (FANETs) to support demanding and diverse services. The highly mobile drones should always be connected to the network in order to satisfy the strict requirements of upcoming applications. As the number of drones increases, they burden the network with the management of signaling and continuous monitoring of the drones during data transmission. Therefore, designing transmission mechanisms for fifth-generation (5G) drone-aided networks and using clustering algorithms for their grouping is of paramount importance. In this paper, a clustering and selection algorithm of the cluster head is proposed together with an efficient Group Handover (GHO) scheme that details how the respective Point of Access (PoA) groups will be clustered. Subsequently, for each cluster, the PoA elects a Cluster Head (CH), which is responsible for manipulating the mobility of the cluster by orchestrating the handover initiation (HO initiation), the network selection, and the handover execution (HO execution) processes. Moreover, the members of the cluster are informed about the impending HO from the CH. As a result, they establish new uplink and downlink communication channels to exchange data packets. In order to evaluate the proposed HO scheme, extensive simulations are carried out for a next-generation drone network architecture that supports Internet of Things (IoT) and multimedia services. This architecture relies on IEEE 802.11p Wireless Access for Vehicular Environment (WAVE) Road Side Units (RSUs) as well as Long-Term Evolution Advanced (LTE-A) and IEEE 802.16 Worldwide Interoperability for Microwave Access (WiMAX). Furthermore, the proposed scheme is also evaluated in a real-world scenario using a testbed deployed in a controlled laboratory environment. Both simulation and real-world experimental results verify that the proposed scheme outperforms existing HO algorithms. Full article
(This article belongs to the Special Issue UAVs in 5G and beyond Networks)
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26 pages, 8701 KiB  
Review
Application and Development of Autonomous Robots in Concrete Construction: Challenges and Opportunities
by Shun Zhao, Qiang Wang, Xinjun Fang, Wei Liang, Yu Cao, Changyi Zhao, Lu Li, Chunbao Liu and Kunyang Wang
Drones 2022, 6(12), 424; https://doi.org/10.3390/drones6120424 - 16 Dec 2022
Cited by 11 | Viewed by 5573
Abstract
Updated concrete construction robots are designed to optimize equipment operation, improve safety, enhance workspace awareness, and further ensure a proper working environment for construction workers. The importance of concrete construction robots has been constantly highlighted, as they have a profound impact on construction [...] Read more.
Updated concrete construction robots are designed to optimize equipment operation, improve safety, enhance workspace awareness, and further ensure a proper working environment for construction workers. The importance of concrete construction robots has been constantly highlighted, as they have a profound impact on construction quality and efficiency. Autonomous vehicle driving monitoring has been widely employed in concrete construction robots; however, they lack clear relevance to the key functions in the building process. This paper aims to bridge this knowledge gap by systematically classifying and summarizing the existing concrete construction robots, analyzing their existing problems, and providing direction for their future development. The prescription criteria and selection of robots depend on the concrete construction process, which includes six common functional levels: distribution, leveling and compaction, floor finishing, surface painting, 3D printing, and surveillance. Misunderstood functions and the improper adjustment of construction robots may lead to increased cost, reduced effectiveness, and restricted application scenarios. Our review identifies current commercial and recently studied concrete construction robots to facilitate the standardization and optimization of robotic construction design. Moreover, this study may be able to guide future research and technology development efforts for autonomous robots in concrete construction. Full article
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19 pages, 6164 KiB  
Article
Transferability of Models for Predicting Rice Grain Yield from Unmanned Aerial Vehicle (UAV) Multispectral Imagery across Years, Cultivars and Sensors
by Hengbiao Zheng, Wenhan Ji, Wenhui Wang, Jingshan Lu, Dong Li, Caili Guo, Xia Yao, Yongchao Tian, Weixing Cao, Yan Zhu and Tao Cheng
Drones 2022, 6(12), 423; https://doi.org/10.3390/drones6120423 - 16 Dec 2022
Cited by 3 | Viewed by 2231
Abstract
Timely and accurate prediction of crop yield prior to harvest is vital for precise agricultural management. Unmanned aerial vehicles (UAVs) provide a fast and convenient approach to crop yield prediction, but most existing crop yield models have rarely been tested across different years, [...] Read more.
Timely and accurate prediction of crop yield prior to harvest is vital for precise agricultural management. Unmanned aerial vehicles (UAVs) provide a fast and convenient approach to crop yield prediction, but most existing crop yield models have rarely been tested across different years, cultivars and sensors. This has limited the ability of these yield models to be transferred to other years or regions or to be potentially used with data from other sensors. In this study, UAV-based multispectral imagery was used to predict rice grain yield at the booting and filling stages from four field experiments, involving three years, two rice cultivars, and two UAV sensors. Reflectance and texture features were extracted from the UAV imagery, and vegetation indices (VIs) and normalized difference texture indices (NDTIs) were computed. The models were independently validated to test the stability and transferability across years, rice cultivars, and sensors. The results showed that the red edge normalized difference texture index (RENDTI) was superior to other texture indices and vegetation indices for model regression with grain yield in most cases. However, the green normalized difference texture index (GNDTI) achieved the highest prediction accuracy in model validation across rice cultivars and sensors. The yield prediction model of Japonica rice achieved stronger transferability to Indica rice with root mean square error (RMSE), bias, and relative RMSE (RRMSE) of 1.16 t/ha, 0.08, and 11.04%, respectively. Model transferability was improved significantly between different sensors after band correction with a decrease of 15.05–59.99% in RRMSE. Random forest (RF) was found to be a good solution to improve the model transferability across different years and cultivars and obtained the highest prediction accuracy with RMSE, bias, and RRMSE of 0.94 t/ha, −0.21, and 9.37%, respectively. This study provides a valuable reference for crop yield prediction when existing models are transferred across different years, cultivars and sensors. Full article
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17 pages, 11624 KiB  
Article
Mapping the Leaf Area Index of Castanea sativa Miller Using UAV-Based Multispectral and Geometrical Data
by Luís Pádua, Pamela M. Chiroque-Solano, Pedro Marques, Joaquim J. Sousa and Emanuel Peres
Drones 2022, 6(12), 422; https://doi.org/10.3390/drones6120422 - 16 Dec 2022
Cited by 2 | Viewed by 2295
Abstract
Remote-sensing processes based on unmanned aerial vehicles (UAV) have opened up new possibilities to both map and extract individual plant parameters. This is mainly due to the high spatial data resolution and acquisition flexibility of UAVs. Among the possible plant-related metrics is the [...] Read more.
Remote-sensing processes based on unmanned aerial vehicles (UAV) have opened up new possibilities to both map and extract individual plant parameters. This is mainly due to the high spatial data resolution and acquisition flexibility of UAVs. Among the possible plant-related metrics is the leaf area index (LAI), which has already been successfully estimated in agronomy and forestry studies using the traditional normalized difference vegetation index from multispectral data or using hyperspectral data. However, the LAI has not been estimated in chestnut trees, and few studies have explored the use of multiple vegetation indices to improve LAI estimation from aerial imagery acquired by UAVs. This study uses multispectral UAV-based data from a chestnut grove to estimate the LAI for each tree by combining vegetation indices computed from different segments of the electromagnetic spectrum with geometrical parameters. Machine-learning techniques were evaluated to predict LAI with robust algorithms that consider dimensionality reduction, avoiding over-fitting, and reduce bias and excess variability. The best achieved coefficient of determination (R2) value of 85%, which shows that the biophysical and geometrical parameters can explain the LAI variability. This result proves that LAI estimation is improved when using multiple variables instead of a single vegetation index. Furthermore, another significant contribution is a simple, reliable, and precise model that relies on only two variables to estimate the LAI in individual chestnut trees. Full article
(This article belongs to the Section Drones in Agriculture and Forestry)
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17 pages, 978 KiB  
Article
LightMAN: A Lightweight Microchained Fabric for Assurance- and Resilience-Oriented Urban Air Mobility Networks
by Ronghua Xu, Sixiao Wei, Yu Chen, Genshe Chen and Khanh Pham
Drones 2022, 6(12), 421; https://doi.org/10.3390/drones6120421 - 16 Dec 2022
Cited by 5 | Viewed by 1899
Abstract
Rapid advancements in the fifth generation (5G) communication technology and mobile edge computing (MEC) paradigm have led to the proliferation of unmanned aerial vehicles (UAV) in urban air mobility (UAM) networks, which provide intelligent services for diversified smart city scenarios. Meanwhile, the widely [...] Read more.
Rapid advancements in the fifth generation (5G) communication technology and mobile edge computing (MEC) paradigm have led to the proliferation of unmanned aerial vehicles (UAV) in urban air mobility (UAM) networks, which provide intelligent services for diversified smart city scenarios. Meanwhile, the widely deployed Internet of drones (IoD) in smart cities has also brought up new concerns regarding performance, security, and privacy. The centralized framework adopted by conventional UAM networks is not adequate to handle high mobility and dynamicity. Moreover, it is necessary to ensure device authentication, data integrity, and privacy preservation in UAM networks. Thanks to its characteristics of decentralization, traceability, and unalterability, blockchain is recognized as a promising technology to enhance security and privacy for UAM networks. In this paper, we introduce LightMAN, a lightweight microchained fabric for data assurance and resilience-oriented UAM networks. LightMAN is tailored for small-scale permissioned UAV networks, in which a microchain acts as a lightweight distributed ledger for security guarantees. Thus, participants are enabled to authenticate drones and verify the genuineness of data that are sent to/from drones without relying on a third-party agency. In addition, a hybrid on-chain and off-chain storage strategy is adopted that not only improves performance (e.g., latency and throughput) but also ensures privacy preservation for sensitive information in UAM networks. A proof-of-concept prototype is implemented and tested on a micro-air–vehicle link (MAVLink) simulator. The experimental evaluation validates the feasibility and effectiveness of the proposed LightMAN solution. Full article
(This article belongs to the Special Issue Urban Air Mobility (UAM))
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11 pages, 1544 KiB  
Review
A Synthetic Review of UAS-Based Facility Condition Monitoring
by Kyeongtae Jeong, Jinhyuk Kwon, Sung Lok Do, Donghoon Lee and Sungjin Kim
Drones 2022, 6(12), 420; https://doi.org/10.3390/drones6120420 - 15 Dec 2022
Cited by 1 | Viewed by 1411
Abstract
Facility inspections are mainly carried out through manual visual inspections. However, it is difficult to determine the extent of damages to facilities, and it depends on the subjective opinion of the manager in charge of the monitoring. Additionally, when inspectors inspect facilities that [...] Read more.
Facility inspections are mainly carried out through manual visual inspections. However, it is difficult to determine the extent of damages to facilities, and it depends on the subjective opinion of the manager in charge of the monitoring. Additionally, when inspectors inspect facilities that cannot be safely accessed, such as high-rise buildings, there are high risks of fatal accidents. For this reason, the construction industry conducts research into unmanned aircraft system (UAS)-based facility inspections. These studies have been focusing on developing the technologies or processes for using UAS in facility condition monitoring, ranging from infrastructure systems to commercial buildings. This study conducted extensive and synthetic reviews of the recent studies in UAS-based facility monitoring using a preferred reporting items for systematic reviews and meta-analysis (PRISMA) method. A total of 32 papers were selected and classified through the types of facilities and the technologies addressed in the studies. This paper analyzes the trends of recent studies by synthesizing the selected papers and consolidates the further directions of UAS applications and studies in facility monitoring domains. Full article
(This article belongs to the Special Issue Application of UAS in Construction)
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19 pages, 5783 KiB  
Article
Formation Tracking Control for Multi-Agent Systems with Collision Avoidance and Connectivity Maintenance
by Yitao Qiao, Xuxing Huang, Bin Yang, Feilong Geng, Bingheng Wang, Mingrui Hao and Shuang Li
Drones 2022, 6(12), 419; https://doi.org/10.3390/drones6120419 - 15 Dec 2022
Cited by 3 | Viewed by 1680
Abstract
This paper investigates the formation tracking control of multiple agents with a double-integrator model and presents a novel distributed control framework composed of three items: a potential-based gradient term, a formation term, and a navigation term. Considering the practical situation, each agent is [...] Read more.
This paper investigates the formation tracking control of multiple agents with a double-integrator model and presents a novel distributed control framework composed of three items: a potential-based gradient term, a formation term, and a navigation term. Considering the practical situation, each agent is regarded as a rigid-body with a safe radius and a sensing region. To enable collision avoidance and connectivity maintenance among multiple agents, a new potential function with fewer parameters is established. The predetermined formation is also achieved by taking the difference between the actual displacement and the desired displacement as a consensus variable. Lastly, the virtual navigator provides trajectory signals and guides the multiple agent movement. Two instances of an equilateral triangle formation and a hexagonal formation are used in the simulation to verify the proposed method. Full article
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27 pages, 6957 KiB  
Article
Study of Urban Logistics Drone Path Planning Model Incorporating Service Benefit and Risk Cost
by Quan Shao, Jiaming Li, Ruoheng Li, Jiangao Zhang and Xiaobo Gao
Drones 2022, 6(12), 418; https://doi.org/10.3390/drones6120418 - 15 Dec 2022
Cited by 5 | Viewed by 2411
Abstract
The application of drones provides a powerful solution for “the last-mile” logistics services, while the large-scale implementation of logistics drone services will threaten the safety of buildings, pedestrians, vehicles, and other elements in the urban environment. The balance of risk cost and service [...] Read more.
The application of drones provides a powerful solution for “the last-mile” logistics services, while the large-scale implementation of logistics drone services will threaten the safety of buildings, pedestrians, vehicles, and other elements in the urban environment. The balance of risk cost and service benefit is accordingly crucial to managing logistics drones. In this study, we proposed a cost-benefit assessment model for quantifying risk cost and service benefit in the urban environment. In addition, a global heuristic path search rule was developed to solve the path planning problem based on risk mitigation and customer service. The cost-benefit assessment model quantifies the risk cost from three environmental elements (buildings, pedestrians, and vehicles) threatened by drone operations based on the collision probability, and the service benefit based on the characteristics of logistics service customers. To explore the effectiveness of the model in this paper, we simulate and analyse the effects of different risk combinations, unknown risk zones, and risk-benefit preferences on the path planning results. The results show that compared with the traditional shortest-distance method, the drone path planning method proposed in this paper can accurately capture the distribution of risks and customers in the urban environment. It is highly reusable in ensuring service benefits while reducing risk costs and generating a cost-effective path for logistics drones. We also compare the algorithm in this paper with the A* algorithm and verify that our algorithm improves the solution quality in complex environments. Full article
(This article belongs to the Special Issue Urban Air Mobility (UAM))
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12 pages, 3205 KiB  
Article
RF Source Localization Using Multiple UAVs through a Novel Geometrical RSSI Approach
by Nurbanu Güzey
Drones 2022, 6(12), 417; https://doi.org/10.3390/drones6120417 - 15 Dec 2022
Cited by 3 | Viewed by 1830
Abstract
In this paper, a novel geometrical localization scheme based on the Received Signal Strength Indicator (RSSI) is developed for a group of unmanned aerial vehicles (UAVs). Since RSSI-based localization does not require complicated hardware, it is the correct choice for RF target localization. [...] Read more.
In this paper, a novel geometrical localization scheme based on the Received Signal Strength Indicator (RSSI) is developed for a group of unmanned aerial vehicles (UAVs). Since RSSI-based localization does not require complicated hardware, it is the correct choice for RF target localization. In this promising work, unlike the other techniques given in the literature, transmit power or path loss exponent information is not needed. The procedure depends on the received power difference of each receiver in UAVs. In the developed scheme, four UAVs forming two groups fly in perpendicular planes. Each UAV in the group moves in a circle, keeping its distance from the plane’s center until it gets equal power with the other members of its group. Using this movement rate, lines passing through the source position are calculated. The intersection of these lines gives the position of the RF target. However, in a noisy environment, the lines do not intersect at one point. Therefore, the algorithm given in the manuscript finds a point that has a minimum distance to all lines and is also developed. Simulation results are provided at the end of the manuscript to verify our theoretical claims. Full article
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20 pages, 1293 KiB  
Article
Autonomous Landing of an UAV Using H Based Model Predictive Control
by Zohaib Latif, Amir Shahzad, Aamer Iqbal Bhatti, James Ferris Whidborne and Raza Samar
Drones 2022, 6(12), 416; https://doi.org/10.3390/drones6120416 - 15 Dec 2022
Cited by 4 | Viewed by 2662
Abstract
Possibly the most critical phase of an Unmanned Air Vehicle (UAV) flight is landing. To reduce the risk due to pilot error, autonomous landing systems can be used. Environmental disturbances such as wind shear can jeopardize safe landing, therefore a well-adjusted and robust [...] Read more.
Possibly the most critical phase of an Unmanned Air Vehicle (UAV) flight is landing. To reduce the risk due to pilot error, autonomous landing systems can be used. Environmental disturbances such as wind shear can jeopardize safe landing, therefore a well-adjusted and robust control system is required to maintain the performance requirements during landing. The paper proposes a loop-shaping-based Model Predictive Control (MPC) approach for autonomous UAV landings. Instead of conventional MPC plant model augmentation, the input and output weights are designed in the frequency domain to meet the transient and steady-state performance requirements. Then, the H loop shaping design procedure is used to synthesize the state-feedback controller for the shaped plant. This linear state-feedback control law is then used to solve an inverse optimization problem to design the cost function matrices for MPC. The designed MPC inherits the small-signal characteristics of the H controller when constraints are inactive (i.e., perturbation around equilibrium points that keep the system within saturation limits). The H loop shaping synthesis results in an observer plus state feedback structure. This state estimator initializes the MPC problem at each time step. The control law is successfully evaluated in a non-linear simulation environment under moderate and severe wind downburst. It rejects unmeasured disturbances, has good transient performance, provides an excellent stability margin, and enforces input constraints. Full article
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26 pages, 9278 KiB  
Article
Helmet Wearing Detection of Motorcycle Drivers Using Deep Learning Network with Residual Transformer-Spatial Attention
by Shuai Chen, Jinhui Lan, Haoting Liu, Chengkai Chen and Xiaohan Wang
Drones 2022, 6(12), 415; https://doi.org/10.3390/drones6120415 - 15 Dec 2022
Cited by 9 | Viewed by 4698
Abstract
Aiming at the existing problem of unmanned aerial vehicle (UAV) aerial photography for riders’ helmet wearing detection, a novel aerial remote sensing detection paradigm is proposed by combining super-resolution reconstruction, residual transformer-spatial attention, and you only look once version 5 (YOLOv5) image classifier. [...] Read more.
Aiming at the existing problem of unmanned aerial vehicle (UAV) aerial photography for riders’ helmet wearing detection, a novel aerial remote sensing detection paradigm is proposed by combining super-resolution reconstruction, residual transformer-spatial attention, and you only look once version 5 (YOLOv5) image classifier. Due to its small target size, significant size change, and strong motion blur in UAV aerial images, the helmet detection model for riders has weak generalization ability and low accuracy. First, a ladder-type multi-attention network (LMNet) for target detection is designed to conquer these difficulties. The LMNet enables information interaction and fusion at each stage, fully extracts image features, and minimizes information loss. Second, the Residual Transformer 3D-spatial Attention Module (RT3DsAM) is proposed in this work, which digests information from global data that is important for feature representation and final classification detection. It also builds self-attention and enhances correlation between information. Third, the rider images detected by LMNet are cropped out and reconstructed by the enhanced super-resolution generative adversarial networks (ESRGAN) to restore more realistic texture information and sharp edges. Finally, the reconstructed images of riders are classified by the YOLOv5 classifier. The results of the experiment show that, when compared with the existing methods, our method improves the detection accuracy of riders’ helmets in aerial photography scenes, with the target detection mean average precision (mAP) evaluation indicator reaching 91.67%, and the image classification top1 accuracy (TOP1 ACC) gaining 94.23%. Full article
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20 pages, 8188 KiB  
Article
Building Change Detection Based on a Gray-Level Co-Occurrence Matrix and Artificial Neural Networks
by Marianna Christaki, Christos Vasilakos, Ermioni-Eirini Papadopoulou, Georgios Tataris, Ilias Siarkos and Nikolaos Soulakellis
Drones 2022, 6(12), 414; https://doi.org/10.3390/drones6120414 - 15 Dec 2022
Cited by 2 | Viewed by 1954
Abstract
The recovery phase following an earthquake event is essential for urban areas with a significant number of damaged buildings. A lot of changes can take place in such a landscape within the buildings’ footprints, such as total or partial collapses, debris removal and [...] Read more.
The recovery phase following an earthquake event is essential for urban areas with a significant number of damaged buildings. A lot of changes can take place in such a landscape within the buildings’ footprints, such as total or partial collapses, debris removal and reconstruction. Remote sensing data and methodologies can considerably contribute to site monitoring. The main objective of this paper is the change detection of the building stock in the settlement of Vrissa on Lesvos Island during the recovery phase after the catastrophic earthquake of 12 June 2017, through the analysis and processing of UAV (unmanned aerial vehicle) images and the application of Artificial Neural Networks (ANNs). More specifically, change detection of the settlement’s building stock by applying an ANN on Gray-Level Co-occurrence Matrix (GLCM) texture features of orthophotomaps acquired by UAVs was performed. For the training of the ANN, a number of GLCM texture features were defined as the independent variable, while the existence or not of structural changes in the buildings were defined as the dependent variable, assigning, respectively, the values 1 or 0 (binary classification). The ANN was trained based on the Levenberg–Marquardt algorithm, and its ability to detect changes was evaluated on the basis of the buildings’ condition, as derived from the binary classification. In conclusion, the GLCM texture feature changes in conjunction with the ANN can provide satisfactory results in predicting the structural changes of buildings with an accuracy of almost 92%. Full article
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29 pages, 7103 KiB  
Article
A New Multi-Criteria Tie Point Filtering Approach to Increase the Accuracy of UAV Photogrammetry Models
by Vahid Mousavi, Masood Varshosaz, Maria Rashidi and Weilian Li
Drones 2022, 6(12), 413; https://doi.org/10.3390/drones6120413 - 14 Dec 2022
Cited by 6 | Viewed by 1595
Abstract
Extracting accurate tie points plays an essential role in the accuracy of image orientation in Unmanned Aerial Vehicle (UAV) photogrammetry. In this study, a Multi-Criteria Decision Making (MCDM) automatic filtering method is presented. Based on the quality features of a photogrammetric model, the [...] Read more.
Extracting accurate tie points plays an essential role in the accuracy of image orientation in Unmanned Aerial Vehicle (UAV) photogrammetry. In this study, a Multi-Criteria Decision Making (MCDM) automatic filtering method is presented. Based on the quality features of a photogrammetric model, the proposed method works at the level of sparse point cloud to remove low-quality tie points for refining the orientation results. In the proposed algorithm, different factors that affect the quality of tie points are identified. The quality measures are then aggregated by applying MCDM methods and a competency score for each 3D tie point. These scores are employed in an automatic filtering approach that selects a subset of high-quality points which are then used to repeat the bundle adjustment. To evaluate the proposed algorithm, various internal and external studies were conducted on different datasets. The findings suggest that our method is both effective and reliable. In addition, in comparison to the existing filtering techniques, the proposed strategy increases the accuracy of bundle adjustment and dense point cloud generation by about 40% and 70%, respectively. Full article
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19 pages, 7236 KiB  
Article
Selection of Take-Off and Landing Sites for Firefighter Drones in Urban Areas Using a GIS-Based Multi-Criteria Model
by Min-Seok Kim, Won-Hwa Hong, Yoon-Ha Lee and Seung-Chan Baek
Drones 2022, 6(12), 412; https://doi.org/10.3390/drones6120412 - 14 Dec 2022
Cited by 2 | Viewed by 1908
Abstract
Currently, firefighter drones in Republic of Korea underperform due to the lack of take-off site reservations in advance. In order to address this issue, this study proposes a GIS-based multi-criteria model for selecting take-off and landing sites for firefighter drones in urban areas. [...] Read more.
Currently, firefighter drones in Republic of Korea underperform due to the lack of take-off site reservations in advance. In order to address this issue, this study proposes a GIS-based multi-criteria model for selecting take-off and landing sites for firefighter drones in urban areas. Seven criteria were set for the selection of take-off and landing sites based on building roofs. Buildings at 318 sites in the target area that satisfy all seven criteria were extracted and grouped according to the geographical location. Among the grouped buildings, 11 sites were reselected through network analysis and central feature methods. In addition, two more sites were selected through the relaxation of criteria for take-off and landing sites for firefighter drones. Validation was performed using the data of building fires that occurred in the target area in the past. The results confirmed the effectiveness of the method applied in this study, as potential responses could be verified for ≥95% of the buildings with a past fire incidence. By introducing a simple methodology in which a multi-criteria model is built through spatial information, this study contributes to the literature on improving operational firefighting strategies and provides practitioners and policymakers with valuable insights to support decision-making. Full article
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24 pages, 592 KiB  
Article
Sensorless and Coordination-Free Lane Switching on a Drone Road Segment—A Simulation Study
by Zhouyu Qu and Andreas Willig
Drones 2022, 6(12), 411; https://doi.org/10.3390/drones6120411 - 14 Dec 2022
Cited by 1 | Viewed by 1664
Abstract
Copter-type UAVs (unmanned aerial vehicles) or drones are expected to become more and more popular for deliveries of small goods in urban areas. One strategy to reduce the risks of drone collisions is to constrain their movements to a drone road system as [...] Read more.
Copter-type UAVs (unmanned aerial vehicles) or drones are expected to become more and more popular for deliveries of small goods in urban areas. One strategy to reduce the risks of drone collisions is to constrain their movements to a drone road system as far as possible. In this paper, for reasons of scalability, we assume that path-planning decisions for drones are not made centrally but rather autonomously by each individual drone, based solely on position/speed/heading information received from other drones through WiFi-based communications. We present a system model for moving drones along a straight road segment or tube, in which the tube is partitioned into lanes. We furthermore present a cost-based algorithm by which drones make lane-switching decisions, and evaluate the performance of differently parameterized versions of this algorithm, highlighting some of the involved tradeoffs. Our algorithm and results can serve as a baseline for more advanced algorithms, for example, including more elaborate sensors. Full article
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19 pages, 6129 KiB  
Article
Computer Vision Based Path Following for Autonomous Unmanned Aerial Systems in Unburied Pipeline Onshore Inspection
by Yago M. R. da Silva, Fabio A. A. Andrade, Lucas Sousa, Gabriel G. R. de Castro, João T. Dias, Guido Berger, José Lima and Milena F. Pinto
Drones 2022, 6(12), 410; https://doi.org/10.3390/drones6120410 - 14 Dec 2022
Cited by 9 | Viewed by 4480
Abstract
Unmanned Aerial Systems (UAS) are becoming more attractive in diverse applications due to their efficiency in performing tasks with a reduced time execution, covering a larger area, and lowering human risks at harmful tasks. In the context of Oil & Gas (O&G), the [...] Read more.
Unmanned Aerial Systems (UAS) are becoming more attractive in diverse applications due to their efficiency in performing tasks with a reduced time execution, covering a larger area, and lowering human risks at harmful tasks. In the context of Oil & Gas (O&G), the scenario is even more attractive for the application of UAS for inspection activities due to the large extension of these facilities and the operational risks involved in the processes. Many authors proposed solutions to detect gas leaks regarding the onshore unburied pipeline structures. However, only a few addressed the navigation and tracking problem for the autonomous navigation of UAS over these structures. Most proposed solutions rely on traditional computer vision strategies for tracking. As a drawback, depending on lighting conditions, the obtained path line may be inaccurate, making a strategy to force the UAS to continue on the path necessary. Therefore, this research describes the potential of an autonomous UAS based on image processing technique and Convolutional Neural Network (CNN) strategy to navigate appropriately in complex unburied pipeline networks contributing to the monitoring procedure of the Oil & Gas Industry structures. A CNN is used to detect the pipe, while image processing techniques such as Canny edge detection and Hough Transform are used to detect the pipe line reference, which is used by a line following algorithm to guide the UAS along the pipe. The framework is assessed by a PX4 flight controller Software-in-The-Loop (SITL) simulations performed with the Robot Operating System (ROS) along with the Gazebo platform to simulate the proposed operational environment and verify the approach’s functionality as a proof of concept. Real tests were also conducted. The results showed that the solution is robust and feasible to deploy in this proposed task, achieving 72% of mean average precision on detecting different types of pipes and 0.0111 m of mean squared error on the path following with a drone 2 m away from a tube. Full article
(This article belongs to the Section Drone Design and Development)
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18 pages, 5259 KiB  
Article
Hidden Hippos: Using Photogrammetry and Multiple Imputation to Determine the Age, Sex, and Body Condition of an Animal Often Partially Submerged
by Victoria L. Inman and Keith E. A. Leggett
Drones 2022, 6(12), 409; https://doi.org/10.3390/drones6120409 - 12 Dec 2022
Cited by 2 | Viewed by 2074
Abstract
Demographic Information on threatened species is important to plan conservation actions. Due to their aquatic lifestyle, the subtle nature of hippo sexual dimorphism, and their occurrence in inaccessible areas, it is difficult to visually determine hippo ages and sexes. Previously, hippo body lengths [...] Read more.
Demographic Information on threatened species is important to plan conservation actions. Due to their aquatic lifestyle, the subtle nature of hippo sexual dimorphism, and their occurrence in inaccessible areas, it is difficult to visually determine hippo ages and sexes. Previously, hippo body lengths have been measured from drone images and used to estimate age. However, due to hippos’ propensity to be partially submerged, it is often difficult to obtain the required measurements. We used the novel technique of multiple imputation to estimate missing body measurements. Further, we explored if male and female hippos could be differentiated in drone images based on body proportions, also examining body condition indices and how these varied seasonally. Multiple imputation increased the number of hippos that we aged threefold, and the body lengths we obtained fell within the range provided in literature, supporting their validity. We provide one of the first age structure breakdowns of a hippo population not from culled hippos. Accounting for overall size, males had wider necks and snouts than females. Hippo body condition varied seasonally, indicating responses to resources and reproduction. We provide a new technique and demonstrate the utility of drones to determine age and sex structures of hippo populations. Full article
(This article belongs to the Special Issue Drone Advances in Wildlife Research)
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25 pages, 1028 KiB  
Article
Ergodic Performance Analysis of Double Intelligent Reflecting Surfaces-Aided NOMA–UAV Systems with Hardware Impairment
by Minh-Sang Van Nguyen, Dinh-Thuan Do, Van-Duc Phan, Wali Ullah Khan, Agbotiname Lucky Imoize and Mostafa M. Fouda
Drones 2022, 6(12), 408; https://doi.org/10.3390/drones6120408 - 12 Dec 2022
Cited by 8 | Viewed by 1717
Abstract
In this work, we design an intelligent reflecting surface (IRS)-assisted Internet of Things (IoT) by enabling non-orthogonal multiple access (NOMA) and unmanned aerial vehicles (UAV) approaches. We pay attention to studying the achievable rates for the ground users. A practical system model takes [...] Read more.
In this work, we design an intelligent reflecting surface (IRS)-assisted Internet of Things (IoT) by enabling non-orthogonal multiple access (NOMA) and unmanned aerial vehicles (UAV) approaches. We pay attention to studying the achievable rates for the ground users. A practical system model takes into account the presence of hardware impairment when Rayleigh and Rician channels are adopted for the IRS–NOMA–UAV system. Our main findings are presented to showcase the exact expressions for achievable rates, and then we derive their simple approximations for a more insightful performance evaluation. The validity of these approximations is demonstrated using extensive Monte Carlo simulations. We confirm the achievable rate improvement decided by main parameters such as the average signal to noise ratio at source, the position of IRS with respect to the source and destination and the number of IRS elements. As a suggestion for the deployment of a low-cost IoT system, the double-IRS model is a reliable approach to realizing the system as long as the hardware impairment level is controlled. The results show that the proposed scheme can greatly improve achievable rates, obtain optimal performance at one of two devices and exhibit a small performance gap compared with the other benchmark scheme. Full article
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26 pages, 23019 KiB  
Article
Texture Analysis to Enhance Drone-Based Multi-Modal Inspection of Structures
by Parham Nooralishahi, Gabriel Ramos, Sandra Pozzer, Clemente Ibarra-Castanedo, Fernando Lopez and Xavier P. V. Maldague
Drones 2022, 6(12), 407; https://doi.org/10.3390/drones6120407 - 11 Dec 2022
Cited by 7 | Viewed by 2452
Abstract
The drone-based multi-modal inspection of industrial structures is a relatively new field of research gaining interest among companies. Multi-modal inspection can significantly enhance data analysis and provide a more accurate assessment of the components’ operability and structural integrity, which can assist in avoiding [...] Read more.
The drone-based multi-modal inspection of industrial structures is a relatively new field of research gaining interest among companies. Multi-modal inspection can significantly enhance data analysis and provide a more accurate assessment of the components’ operability and structural integrity, which can assist in avoiding data misinterpretation and providing a more comprehensive evaluation, which is one of the NDT4.0 objectives. This paper investigates the use of coupled thermal and visible images to enhance abnormality detection accuracy in drone-based multi-modal inspections. Four use cases are presented, introducing novel process pipelines for enhancing defect detection in different scenarios. The first use case presents a process pipeline to enhance the feature visibility on visible images using thermal images in pavement crack detection. The second use case proposes an abnormality classification method for surface and subsurface defects using both modalities and texture segmentation for piping inspections. The third use case introduces a process pipeline for road inspection using both modalities. A texture segmentation method is proposed to extract the pavement regions in thermal and visible images. Further, the combination of both modalities is used to detect surface and subsurface defects. The texture segmentation approach is employed for bridge inspection in the fourth use case to extract concrete surfaces in both modalities. Full article
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22 pages, 4953 KiB  
Article
FRCNN-Based Reinforcement Learning for Real-Time Vehicle Detection, Tracking and Geolocation from UAS
by Chandra Has Singh, Vishal Mishra, Kamal Jain and Anoop Kumar Shukla
Drones 2022, 6(12), 406; https://doi.org/10.3390/drones6120406 - 9 Dec 2022
Cited by 19 | Viewed by 2555
Abstract
In the last few years, uncrewed aerial systems (UASs) have been broadly employed for many applications including urban traffic monitoring. However, in the detection, tracking, and geolocation of moving vehicles using UAVs there are problems to be encountered such as low-accuracy sensors, complex [...] Read more.
In the last few years, uncrewed aerial systems (UASs) have been broadly employed for many applications including urban traffic monitoring. However, in the detection, tracking, and geolocation of moving vehicles using UAVs there are problems to be encountered such as low-accuracy sensors, complex scenes, small object sizes, and motion-induced noises. To address these problems, this study presents an intelligent, self-optimised, real-time framework for automated vehicle detection, tracking, and geolocation in UAV-acquired images which enlist detection, location, and tracking features to improve the final decision. The noise is initially reduced by applying the proposed adaptive filtering, which makes the detection algorithm more versatile. Thereafter, in the detection step, top-hat and bottom-hat transformations are used, assisted by the Overlapped Segmentation-Based Morphological Operation (OSBMO). Following the detection phase, the background regions are obliterated through an analysis of the motion feature points of the obtained object regions using a method that is a conjugation between the Kanade–Lucas–Tomasi (KLT) trackers and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering. The procured object features are clustered into separate objects on the basis of their motion characteristics. Finally, the vehicle labels are designated to their corresponding cluster trajectories by employing an efficient reinforcement connecting algorithm. The policy-making possibilities of the reinforcement connecting algorithm are evaluated. The Fast Regional Convolutional Neural Network (Fast-RCNN) is designed and trained on a small collection of samples, then utilised for removing the wrong targets. The proposed framework was tested on videos acquired through various scenarios. The methodology illustrates its capacity through the automatic supervision of target vehicles in real-world trials, which demonstrates its potential applications in intelligent transport systems and other surveillance applications. Full article
(This article belongs to the Special Issue Advances in Deep Learning for Drones and Its Applications)
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13 pages, 995 KiB  
Article
Performance of Reconfigurable-Intelligent-Surface-Assisted Satellite Quasi-Stationary Aircraft–Terrestrial Laser Communication System
by Yi Wang, Haibo Wang and XueWen Jiang
Drones 2022, 6(12), 405; https://doi.org/10.3390/drones6120405 - 8 Dec 2022
Cited by 3 | Viewed by 1429
Abstract
This paper proposes the use of quasi-stationary aircraft and reconfigurable intelligent surfaces (RIS) to improve the system performance in satellite–terrestrial laser communication downlink. Single-input multiple-output (SIMO) technology is applied to the relay node of a quasi-stationary aircraft. The closed expression of the bit [...] Read more.
This paper proposes the use of quasi-stationary aircraft and reconfigurable intelligent surfaces (RIS) to improve the system performance in satellite–terrestrial laser communication downlink. Single-input multiple-output (SIMO) technology is applied to the relay node of a quasi-stationary aircraft. The closed expression of the bit error rate (BER) of an RIS-assisted satellite quasi-stationary aircraft–terrestrial laser communication system (RIS-SHTLC) is derived under the M-distributed atmospheric turbulence model while considering the influence of atmospheric turbulence and pointing errors caused by RIS jitter. The effects of coherent binary frequency shift keying (CBFSK), coherent binary phase-shift keying (CBPSK), non-coherent binary frequency shift keying (NBFSK), and differential binary phase-shift keying (DBPSK) on the performance of an RIS-SHTLC system are simulated and analyzed under weak turbulence. The results show that the RIS-SHTLC system with CBPSK modulation has the best communication performance. Simultaneously, the relationships between the average signal-to-noise ratio (SNR) and BER of the RIS-SHTLC system under different RIS elements are simulated and analyzed, and compared with the traditional SHTLC system. In addition, the influence of the zenith angle, receiving aperture and divergence angle on the performance of the system is studied. Finally, Monte Carlo simulations are used to validate the analytical results. Full article
(This article belongs to the Section Drone Communications)
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24 pages, 16230 KiB  
Article
Drone-Based Atmospheric Soundings Up to an Altitude of 10 km-Technical Approach towards Operations
by Konrad Bärfuss, Ruud Dirksen, Holger Schmithüsen, Lutz Bretschneider, Falk Pätzold, Sven Bollmann, Philippe Panten, Thomas Rausch and Astrid Lampert
Drones 2022, 6(12), 404; https://doi.org/10.3390/drones6120404 - 8 Dec 2022
Cited by 3 | Viewed by 2461
Abstract
Currently, the main in situ upper air database for numerical weather prediction relies on radiosonde and aircraft-based information. Typically, radiosondes are launched at specific sites daily, up to four times per day, and data are distributed worldwide via the GTS net. Aircraft observations [...] Read more.
Currently, the main in situ upper air database for numerical weather prediction relies on radiosonde and aircraft-based information. Typically, radiosondes are launched at specific sites daily, up to four times per day, and data are distributed worldwide via the GTS net. Aircraft observations are limited to frequent flight routes, and vertical profiles are provided in the vicinity of large cities. However, there are large areas with few radiosonde launches, in particular above the oceans and in the polar areas. In this article, the development and technical details of the unmanned aerial system LUCA (Lightweight Unmanned high Ceiling Aerial system) are described. LUCA has the potential to complement radiosonde and aircraft-based observations up to 10 km in altitude. The system ascends and descends (by electrical power) in spiral trajectories and returns to the launching site. This article discusses the requirements for obtaining high data availability under mid-European and Antarctic conditions, with highly automated take-offs and landings under high surface winds, the capacity to deal with icing, and the ability to operate under high wind speeds. The article presents technical solutions for the design and construction of the system and demonstrates its potential. Full article
(This article belongs to the Special Issue UAV Design and Applications in Antarctic Research)
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18 pages, 6632 KiB  
Article
Design and Experimental Study on an Innovative UAV-LiDAR Topographic Mapping System for Precision Land Levelling
by Mengmeng Du, Hanyuan Li and Ali Roshanianfard
Drones 2022, 6(12), 403; https://doi.org/10.3390/drones6120403 - 8 Dec 2022
Cited by 7 | Viewed by 1544
Abstract
Topographic maps provide detailed information on variations in ground elevation, which is essential for precision farmland levelling. This paper reports the development and experimental study on an innovative approach of generating topographic maps at farmland-level with the advantages of high efficiency and simplicity [...] Read more.
Topographic maps provide detailed information on variations in ground elevation, which is essential for precision farmland levelling. This paper reports the development and experimental study on an innovative approach of generating topographic maps at farmland-level with the advantages of high efficiency and simplicity of implementation. The experiment uses a low-altitude Unmanned Aerial Vehicle (UAV) as a platform and integrates Light Detection and Ranging (LiDAR) distance measurements with Post-Processing Kinematic Global Positioning System (PPK-GNSS) coordinates. A topographic mapping experiment was conducted over two fields in Henan Province, China, and primitive errors of the topographic surveying data were evaluated. The Root Mean Square Error (RMSE) between elevation data of the UAV-LiDAR topographic mapping system and ground truth data was calculated as 4.1 cm and 3.6 cm for Field 1 and Field 2, respectively, which proved the feasibility and high accuracy of the topographic mapping system. Furthermore, the accuracies of topographic maps generated using different geo-spatial interpolation models were also evaluated. The results showed that a TIN (Triangulated Irregular Network) interpolation model expressed the best performances for both Field 1 with sparse topographic surveying points, and Field 2 with relatively dense topographic surveying points, when compared with other interpolation models. Moreover, we concluded that as the spatial resolution of topographic surveying points is intensified from 5 m × 0.5 m to 2.5 m × 0.5 m, the accuracy of the topographic map based on the TIN model improves drastically from 7.7 cm to 4.6 cm. Cut-fill analysis was also implemented based on the topographic maps of the TIN interpolation model. The result indicated that the UAV-LiDAR topographic mapping system could be successfully used to generate topographic maps with high accuracy, which could provide instructive information for precision farmland levelling. Full article
(This article belongs to the Section Drones in Agriculture and Forestry)
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11 pages, 1435 KiB  
Article
Consensus Control of Large-Scale UAV Swarm Based on Multi-Layer Graph
by Taiqi Wang, Shuaihe Zhao, Yuanqing Xia, Zhenhua Pan and Hanwen Tian
Drones 2022, 6(12), 402; https://doi.org/10.3390/drones6120402 - 7 Dec 2022
Cited by 2 | Viewed by 1909
Abstract
An efficient control of large-scale unmanned aerial vehicle (UAV) swarm to establish a complex formation is one of the most challenging tasks. This paper investigates a novel multi-layer topology network and consensus control approach for a large-scale UAV swarm moving under a stable [...] Read more.
An efficient control of large-scale unmanned aerial vehicle (UAV) swarm to establish a complex formation is one of the most challenging tasks. This paper investigates a novel multi-layer topology network and consensus control approach for a large-scale UAV swarm moving under a stable configuration. The proposed topology can make the swarm remain robust in spite of the large number of UAVs. Then a potential function-based controller is developed to control the UAVs in realizing autonomous configuration swarming under the consideration of mutual collision, and the stability of the controller from the individual UAV to the entire swarm system is analyzed by a Lyapunov approach. Afterwards, a yaw angle adjustment approach for the UAVs to reach consensus is developed for the multi-layer swarm, then the direction state of each UAV converges with a fast rate. Finally, simulations are performed on the large-scale UAV swarm system to demonstrate the effectiveness of the proposed scheme. Full article
(This article belongs to the Special Issue Multi-UAV Networks)
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