Drones have been increasingly used in firefighting to improve the response speed and reduce the dangers to human firefighters. However, few studies simultaneously consider fire spread prediction, drone scheduling, and the configuration of supporting staff and supplies. This paper presents a mathematical model that estimates wildfire spread and economic losses simultaneously. The model can also help us to determine the minimum number of firefighting drones in preparation for wildfire in a given wild area. Next, given a limited number of firefighting drones, we propose a method for scheduling the drones in response to wildfire occurrence to minimize the expected loss using metaheuristic optimization. We demonstrate the performance advantages of water wave optimization over a set of other metaheuristic optimization algorithms on 72 test instances simulated on selected suburb areas of Hangzhou, China. Based on the optimization results, we can pre-define a comprehensive plan of scheduling firefighting drone and configuring support staff in response to a set of scenarios of wildfire occurrences, significantly improving the emergency response efficiency and reducing the potential losses.
{"title":"Firefighting Drone Configuration and Scheduling for Wildfire Based on Loss Estimation and Minimization","authors":"Rong-Yu Wu, Xi-Cheng Xie, Yujun Zheng","doi":"10.3390/drones8010017","DOIUrl":"https://doi.org/10.3390/drones8010017","url":null,"abstract":"Drones have been increasingly used in firefighting to improve the response speed and reduce the dangers to human firefighters. However, few studies simultaneously consider fire spread prediction, drone scheduling, and the configuration of supporting staff and supplies. This paper presents a mathematical model that estimates wildfire spread and economic losses simultaneously. The model can also help us to determine the minimum number of firefighting drones in preparation for wildfire in a given wild area. Next, given a limited number of firefighting drones, we propose a method for scheduling the drones in response to wildfire occurrence to minimize the expected loss using metaheuristic optimization. We demonstrate the performance advantages of water wave optimization over a set of other metaheuristic optimization algorithms on 72 test instances simulated on selected suburb areas of Hangzhou, China. Based on the optimization results, we can pre-define a comprehensive plan of scheduling firefighting drone and configuring support staff in response to a set of scenarios of wildfire occurrences, significantly improving the emergency response efficiency and reducing the potential losses.","PeriodicalId":36448,"journal":{"name":"Drones","volume":"4 10","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139439589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
R. Bardera, Á. Rodríguez-Sevillano, Estela Barroso, Juan Carlos Matías, Suthyvann Sor Mendi
Bird tails play a significant role in aerodynamics and stability during flight. This paper investigates the use of bioinspired horizontal stabilizers for Micro Air Vehicles (MAVs) with Zimmerman wing-body geometry. Five configurations of bioinspired horizontal stabilizers are presented. Then, 3-component external balance force measurements of each horizontal stabilizer are performed in the wind tunnel. The Squared-Fan-Shaped Horizontal Stabilizer (HSF-tail) is selected as the optimal horizontal stabilizer that provides the highest aerodynamic efficiency during cruise flight while maintaining high longitudinal stability on the vehicle. The integration of the HSF-tail increases the aerodynamic efficiency by more than 6% up to a maximum of 17% compared to the other alternatives while maintaining the lowest aerodynamic drag value during the cruise phase. Furthermore, balance measurements to analyze the influence of the HSF-tail deflection on the aerodynamic coefficients are conducted, resulting in increased lift force and reduced aerodynamic drag with negative tail deflections. Lastly, the experimental data is validated with CFD-RANS steady simulations for low angles of attack, obtaining a relative difference on the measurement around 5% for the aerodynamic drag coefficient and around 10% for the lift coefficient during the cruise flight that demonstrates a high degree of accuracy in the aerodynamic coefficients obtained by external balance in the wind tunnel. This work represents a novel approach through the implementation of a horizontal stabilizer inspired by the structure of the tails of birds that is expected to yield significant advancements in both stability and aerodynamic efficiency, with the potential to revolutionize MAV technology.
{"title":"Wind Tunnel Balance Measurements of Bioinspired Tails for a Fixed Wing MAV","authors":"R. Bardera, Á. Rodríguez-Sevillano, Estela Barroso, Juan Carlos Matías, Suthyvann Sor Mendi","doi":"10.3390/drones8010016","DOIUrl":"https://doi.org/10.3390/drones8010016","url":null,"abstract":"Bird tails play a significant role in aerodynamics and stability during flight. This paper investigates the use of bioinspired horizontal stabilizers for Micro Air Vehicles (MAVs) with Zimmerman wing-body geometry. Five configurations of bioinspired horizontal stabilizers are presented. Then, 3-component external balance force measurements of each horizontal stabilizer are performed in the wind tunnel. The Squared-Fan-Shaped Horizontal Stabilizer (HSF-tail) is selected as the optimal horizontal stabilizer that provides the highest aerodynamic efficiency during cruise flight while maintaining high longitudinal stability on the vehicle. The integration of the HSF-tail increases the aerodynamic efficiency by more than 6% up to a maximum of 17% compared to the other alternatives while maintaining the lowest aerodynamic drag value during the cruise phase. Furthermore, balance measurements to analyze the influence of the HSF-tail deflection on the aerodynamic coefficients are conducted, resulting in increased lift force and reduced aerodynamic drag with negative tail deflections. Lastly, the experimental data is validated with CFD-RANS steady simulations for low angles of attack, obtaining a relative difference on the measurement around 5% for the aerodynamic drag coefficient and around 10% for the lift coefficient during the cruise flight that demonstrates a high degree of accuracy in the aerodynamic coefficients obtained by external balance in the wind tunnel. This work represents a novel approach through the implementation of a horizontal stabilizer inspired by the structure of the tails of birds that is expected to yield significant advancements in both stability and aerodynamic efficiency, with the potential to revolutionize MAV technology.","PeriodicalId":36448,"journal":{"name":"Drones","volume":"11 7","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139439857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Unmanned aerial vehicles (UAVs) hold significant potential for various indoor applications, such as mapping, surveillance, navigation, and search and rescue operations. However, indoor positioning is a significant challenge for UAVs, owing to the lack of GPS signals and the complexity of indoor environments. Therefore, this study was aimed at developing a Wi-Fi-based three-dimensional (3D) indoor positioning scheme tailored to time-varying environments, involving human movement and uncertainties in the states of wireless devices. Specifically, we established an innovative 3D indoor positioning system to meet the localisation demands of UAVs in indoor environments. A 3D indoor positioning database was developed using a deep-learning classifier, enabling 3D indoor positioning through Wi-Fi technology. Additionally, through a pioneering integration of fingerprint recognition into wireless positioning technology, we enhanced the precision and reliability of indoor positioning through a detailed analysis and learning process of Wi-Fi signal features. Two test cases (Cases 1 and 2) were designed with positioning height intervals of 0.5 m and 0.8 m, respectively, corresponding to the height of the test scene for positioning simulation and testing. With an error margin of 4 m, the simulation accuracies for the (X, Y) dimension reached 94.08% (Case 1) and 94.95% (Case 2). When the error margin was 0 m, the highest simulation accuracies for the H dimension were 91.84% (Case 1) and 93.61% (Case 2). Moreover, 40 real-time positioning experiments were conducted in the (X, Y, H) dimension. In Case 1, the average positioning success rates were 50.8% (Margin-0), 72.9% (Margin-1), and 81.4% (Margin-2), and the corresponding values for Case 2 were 52.4%, 74.5%, and 82.8%, respectively. The results demonstrated that the proposed method can facilitate 3D indoor positioning based only on Wi-Fi technologies.
{"title":"Three-Dimensional Indoor Positioning Scheme for Drone with Fingerprint-Based Deep-Learning Classifier","authors":"Shuzhi Liu, Houjin Lu, Seung-Hoon Hwang","doi":"10.3390/drones8010015","DOIUrl":"https://doi.org/10.3390/drones8010015","url":null,"abstract":"Unmanned aerial vehicles (UAVs) hold significant potential for various indoor applications, such as mapping, surveillance, navigation, and search and rescue operations. However, indoor positioning is a significant challenge for UAVs, owing to the lack of GPS signals and the complexity of indoor environments. Therefore, this study was aimed at developing a Wi-Fi-based three-dimensional (3D) indoor positioning scheme tailored to time-varying environments, involving human movement and uncertainties in the states of wireless devices. Specifically, we established an innovative 3D indoor positioning system to meet the localisation demands of UAVs in indoor environments. A 3D indoor positioning database was developed using a deep-learning classifier, enabling 3D indoor positioning through Wi-Fi technology. Additionally, through a pioneering integration of fingerprint recognition into wireless positioning technology, we enhanced the precision and reliability of indoor positioning through a detailed analysis and learning process of Wi-Fi signal features. Two test cases (Cases 1 and 2) were designed with positioning height intervals of 0.5 m and 0.8 m, respectively, corresponding to the height of the test scene for positioning simulation and testing. With an error margin of 4 m, the simulation accuracies for the (X, Y) dimension reached 94.08% (Case 1) and 94.95% (Case 2). When the error margin was 0 m, the highest simulation accuracies for the H dimension were 91.84% (Case 1) and 93.61% (Case 2). Moreover, 40 real-time positioning experiments were conducted in the (X, Y, H) dimension. In Case 1, the average positioning success rates were 50.8% (Margin-0), 72.9% (Margin-1), and 81.4% (Margin-2), and the corresponding values for Case 2 were 52.4%, 74.5%, and 82.8%, respectively. The results demonstrated that the proposed method can facilitate 3D indoor positioning based only on Wi-Fi technologies.","PeriodicalId":36448,"journal":{"name":"Drones","volume":"26 8","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139441715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The collection and transportation of samples are crucial steps in stopping the initial spread of infectious diseases. This process demands high levels of safety and timeliness. The rapid advancement of technologies such as the Internet of Things (IoT) and blockchain offers a viable solution to this challenge. To this end, we propose a Blockchain-enabled Infection Sample Collection system (BISC) consisting of a two-echelon drone-assisted mechanism. The system utilizes collector drones to gather samples from user points and transport them to designated transit points, while deliverer drones convey the packaged samples from transit points to testing centers. We formulate the described problem as a Two-Echelon Heterogeneous Drone Routing Problem with Transit point Synchronization (2E-HDRP-TS). To obtain near-optimal solutions to 2E-HDRP-TS, we introduce a multi-objective Adaptive Large Neighborhood Search algorithm for Drone Routing (ALNS-RD). The algorithm’s multi-objective functions are designed to minimize the total collection time of infection samples and the exposure index. In addition to traditional search operators, ALNS-RD incorporates two new search operators based on flight distance and exposure index to enhance solution efficiency and safety. Through a comparison with benchmark algorithms such as NSGA-II and MOLNS, the effectiveness and efficiency of the proposed ALNS-RD algorithm are validated, demonstrating its superior performance across all five instances with diverse complexity levels.
{"title":"Blockchain-Enabled Infection Sample Collection System Using Two-Echelon Drone-Assisted Mechanism","authors":"Shengqi Kang, Xiuwen Fu","doi":"10.3390/drones8010014","DOIUrl":"https://doi.org/10.3390/drones8010014","url":null,"abstract":"The collection and transportation of samples are crucial steps in stopping the initial spread of infectious diseases. This process demands high levels of safety and timeliness. The rapid advancement of technologies such as the Internet of Things (IoT) and blockchain offers a viable solution to this challenge. To this end, we propose a Blockchain-enabled Infection Sample Collection system (BISC) consisting of a two-echelon drone-assisted mechanism. The system utilizes collector drones to gather samples from user points and transport them to designated transit points, while deliverer drones convey the packaged samples from transit points to testing centers. We formulate the described problem as a Two-Echelon Heterogeneous Drone Routing Problem with Transit point Synchronization (2E-HDRP-TS). To obtain near-optimal solutions to 2E-HDRP-TS, we introduce a multi-objective Adaptive Large Neighborhood Search algorithm for Drone Routing (ALNS-RD). The algorithm’s multi-objective functions are designed to minimize the total collection time of infection samples and the exposure index. In addition to traditional search operators, ALNS-RD incorporates two new search operators based on flight distance and exposure index to enhance solution efficiency and safety. Through a comparison with benchmark algorithms such as NSGA-II and MOLNS, the effectiveness and efficiency of the proposed ALNS-RD algorithm are validated, demonstrating its superior performance across all five instances with diverse complexity levels.","PeriodicalId":36448,"journal":{"name":"Drones","volume":"10 7","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139448787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this study, we design and analyze a reliability-oriented downlink wireless network assisted by unmanned aerial vehicles (UAVs). This network employs non-orthogonal multiple access (NOMA) transmission and finite blocklength (FBL) codes. In the network, ground user equipments (GUEs) request content from a remote base station (BS), and there are no direct connections between the BS and the GUEs. To address this, we employ a UAV with a limited caching capacity to assist the BS in completing the communication. The UAV can either request uncached content from the BS and then serve the GUEs or directly transmit cached content to the GUEs. In this paper, we first introduce the decoding error rate within the FBL regime and explore caching policies for the UAV. Subsequently, we formulate an optimization problem aimed at minimizing the average maximum end-to-end decoding error rate across all GUEs while considering the coding length and maximum UAV transmission power constraints. We propose a two-step alternating optimization scheme embedded within a deep deterministic policy gradient (DDPG) algorithm to jointly determine the UAV trajectory and transmission power allocations, as well as blocklength of downloading phase, and our numerical results show that the combined learning-optimization algorithm efficiently addresses the considered problem. In particular, it is shown that a well-designed UAV trajectory, relaxing the FBL constraint, increasing the cache size, and providing a higher UAV transmission power budget all lead to improved performance.
{"title":"Joint Trajectory Design and Resource Optimization in UAV-Assisted Caching-Enabled Networks with Finite Blocklength Transmissions","authors":"Yang Yang, M. C. Gursoy","doi":"10.3390/drones8010012","DOIUrl":"https://doi.org/10.3390/drones8010012","url":null,"abstract":"In this study, we design and analyze a reliability-oriented downlink wireless network assisted by unmanned aerial vehicles (UAVs). This network employs non-orthogonal multiple access (NOMA) transmission and finite blocklength (FBL) codes. In the network, ground user equipments (GUEs) request content from a remote base station (BS), and there are no direct connections between the BS and the GUEs. To address this, we employ a UAV with a limited caching capacity to assist the BS in completing the communication. The UAV can either request uncached content from the BS and then serve the GUEs or directly transmit cached content to the GUEs. In this paper, we first introduce the decoding error rate within the FBL regime and explore caching policies for the UAV. Subsequently, we formulate an optimization problem aimed at minimizing the average maximum end-to-end decoding error rate across all GUEs while considering the coding length and maximum UAV transmission power constraints. We propose a two-step alternating optimization scheme embedded within a deep deterministic policy gradient (DDPG) algorithm to jointly determine the UAV trajectory and transmission power allocations, as well as blocklength of downloading phase, and our numerical results show that the combined learning-optimization algorithm efficiently addresses the considered problem. In particular, it is shown that a well-designed UAV trajectory, relaxing the FBL constraint, increasing the cache size, and providing a higher UAV transmission power budget all lead to improved performance.","PeriodicalId":36448,"journal":{"name":"Drones","volume":"51 2","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139386408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Quantitatively characterizing coastal salt-marsh terrains and the corresponding spatiotemporal changes are crucial for formulating comprehensive management plans and clarifying the dynamic carbon evolution. Multiline light detection and ranging (LiDAR) exhibits great capability for terrain measuring for salt marshes with strong penetration performance and a new scanning mode. The prerequisite to obtaining the high-precision terrain requires accurate filtering of the salt-marsh vegetation points from the ground/mudflat ones in the multiline LiDAR data. In this study, a new alternative salt-marsh vegetation point-cloud filtering method is proposed for drone multiline LiDAR based on the extreme gradient boosting (i.e., XGBoost) model. According to the basic principle that vegetation and the ground exhibit different geometric and radiometric characteristics, the XGBoost is constructed to model the relationships of point categories with a series of selected basic geometric and radiometric metrics (i.e., distance, scan angle, elevation, normal vectors, and intensity), where absent instantaneous scan geometry (i.e., distance and scan angle) for each point is accurately estimated according to the scanning principles and point-cloud spatial distribution characteristics of drone multiline LiDAR. Based on the constructed model, the combination of the selected features can accurately and intelligently predict the category of each point. The proposed method is tested in a coastal salt marsh in Shanghai, China by a drone 16-line LiDAR system. The results demonstrate that the averaged AUC and G-mean values of the proposed method are 0.9111 and 0.9063, respectively. The proposed method exhibits enhanced applicability and versatility and outperforms the traditional and other machine-learning methods in different areas with varying topography and vegetation-growth status, which shows promising potential for point-cloud filtering and classification, particularly in extreme environments where the terrains, land covers, and point-cloud distributions are highly complicated.
{"title":"Drone Multiline Light Detection and Ranging Data Filtering in Coastal Salt Marshes Using Extreme Gradient Boosting Model","authors":"Xixiu Wu, Kai Tan, Shuai Liu, Feng Wang, Pengjie Tao, Yanjun Wang, Xiaolong Cheng","doi":"10.3390/drones8010013","DOIUrl":"https://doi.org/10.3390/drones8010013","url":null,"abstract":"Quantitatively characterizing coastal salt-marsh terrains and the corresponding spatiotemporal changes are crucial for formulating comprehensive management plans and clarifying the dynamic carbon evolution. Multiline light detection and ranging (LiDAR) exhibits great capability for terrain measuring for salt marshes with strong penetration performance and a new scanning mode. The prerequisite to obtaining the high-precision terrain requires accurate filtering of the salt-marsh vegetation points from the ground/mudflat ones in the multiline LiDAR data. In this study, a new alternative salt-marsh vegetation point-cloud filtering method is proposed for drone multiline LiDAR based on the extreme gradient boosting (i.e., XGBoost) model. According to the basic principle that vegetation and the ground exhibit different geometric and radiometric characteristics, the XGBoost is constructed to model the relationships of point categories with a series of selected basic geometric and radiometric metrics (i.e., distance, scan angle, elevation, normal vectors, and intensity), where absent instantaneous scan geometry (i.e., distance and scan angle) for each point is accurately estimated according to the scanning principles and point-cloud spatial distribution characteristics of drone multiline LiDAR. Based on the constructed model, the combination of the selected features can accurately and intelligently predict the category of each point. The proposed method is tested in a coastal salt marsh in Shanghai, China by a drone 16-line LiDAR system. The results demonstrate that the averaged AUC and G-mean values of the proposed method are 0.9111 and 0.9063, respectively. The proposed method exhibits enhanced applicability and versatility and outperforms the traditional and other machine-learning methods in different areas with varying topography and vegetation-growth status, which shows promising potential for point-cloud filtering and classification, particularly in extreme environments where the terrains, land covers, and point-cloud distributions are highly complicated.","PeriodicalId":36448,"journal":{"name":"Drones","volume":"41 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139386653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
When unmanned platforms perform precise target detection, the configuration of detection nodes will significantly impact accuracy. Aiming to obtain the minimum dilution of precision (DOP), this paper innovatively proposes an optimal detection configuration design method focused on the heterogeneous unmanned cooperative swarm based on the nested cone model. The proposed method first divides the swarm into different groups according to the performances of platforms and then uses a conical nested configuration to arrange the placement of each node independently. The paper considers the problem of the inaccurate prior position of the target and replaces the single-point DOP with the average DOP on the prior region of the target as the optimization objective. Considering the unavoidable positioning errors in engineering practice, this paper provides the optimal configuration of the detection group (DG) and anchor group (AG) in the swarm to reduce the impact caused by positioning errors of detection nodes. We set a certain swarm consisting of 3 types of platforms to design the configuration by simulation experiments and find the optimal parameters for nested cones to realize accurate detection.
{"title":"Optimal Configuration of Heterogeneous Swarm for Cooperative Detection with Minimum DOP Based on Nested Cones","authors":"Ruihang Yu, Yilin Liu, Yangtao Meng, Yan Guo, Zhiming Xiong, Pengfei Jiang","doi":"10.3390/drones8010011","DOIUrl":"https://doi.org/10.3390/drones8010011","url":null,"abstract":"When unmanned platforms perform precise target detection, the configuration of detection nodes will significantly impact accuracy. Aiming to obtain the minimum dilution of precision (DOP), this paper innovatively proposes an optimal detection configuration design method focused on the heterogeneous unmanned cooperative swarm based on the nested cone model. The proposed method first divides the swarm into different groups according to the performances of platforms and then uses a conical nested configuration to arrange the placement of each node independently. The paper considers the problem of the inaccurate prior position of the target and replaces the single-point DOP with the average DOP on the prior region of the target as the optimization objective. Considering the unavoidable positioning errors in engineering practice, this paper provides the optimal configuration of the detection group (DG) and anchor group (AG) in the swarm to reduce the impact caused by positioning errors of detection nodes. We set a certain swarm consisting of 3 types of platforms to design the configuration by simulation experiments and find the optimal parameters for nested cones to realize accurate detection.","PeriodicalId":36448,"journal":{"name":"Drones","volume":"107 13","pages":""},"PeriodicalIF":4.8,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139391371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Tree of heaven (Ailanthus altissima) is a highly invasive tree species in the USA and the preferred host of an invasive insect, the spotted lanternfly (Lycorma delicatula). Currently, pest managers rely solely on ground surveys for detecting both A. altissima and spotted lanternflies. This study aimed to develop efficient tools for A. altissima detection using drones equipped with optical sensors. Aerial surveys were conducted to determine the optimal season, sensor type, and flight altitudes for A. altissima detection. The results revealed that A. altissima can be detected during different seasons and at specific flight heights. Male inflorescences were identifiable using an RGB sensor in the spring at <40 m, seed clusters were identifiable in summer and fall at <25 m using an RGB sensor, and remnant seed clusters were identifiable in the winter at <20 m using RGB and thermal sensors. Combining all seasonal data allowed for the identification of both male and female A. altissima. This study suggests that employing drones with optical sensors can provide a near real-time and efficient method for A. altissima detection. Such a tool has the potential to aid in the development of effective strategies for monitoring spotted lanternflies and managing A. altissima.
天堂树（Ailanthus altissima）是美国的高入侵树种，也是入侵昆虫斑灯蝇（Lycorma delicatula）的首选寄主。目前，害虫管理者只能依靠地面调查来检测海棠和斑潜蝇。本研究旨在利用装有光学传感器的无人机开发高效的工具，以检测斑潜蝇。研究人员进行了空中勘测，以确定检测 A. altissima 的最佳季节、传感器类型和飞行高度。结果表明，在不同的季节和特定的飞行高度都能探测到 A. altissima。使用 RGB 传感器可在春季小于 40 米的高度识别雄花序，使用 RGB 传感器可在夏季和秋季小于 25 米的高度识别种子簇，使用 RGB 和热传感器可在冬季小于 20 米的高度识别残余种子簇。综合所有季节数据，可以识别雌雄A. altissima。这项研究表明，使用带有光学传感器的无人机可以提供一种近乎实时和高效的方法来检测 A. altissima。这种工具有可能帮助制定监测斑灯蝇和管理斑灯蝇的有效策略。
{"title":"The Detection of Tree of Heaven (Ailanthus altissima) Using Drones and Optical Sensors: Implications for the Management of Invasive Plants and Insects","authors":"K. Naharki, Cynthia D. Huebner, Yong-Lak Park","doi":"10.3390/drones8010001","DOIUrl":"https://doi.org/10.3390/drones8010001","url":null,"abstract":"Tree of heaven (Ailanthus altissima) is a highly invasive tree species in the USA and the preferred host of an invasive insect, the spotted lanternfly (Lycorma delicatula). Currently, pest managers rely solely on ground surveys for detecting both A. altissima and spotted lanternflies. This study aimed to develop efficient tools for A. altissima detection using drones equipped with optical sensors. Aerial surveys were conducted to determine the optimal season, sensor type, and flight altitudes for A. altissima detection. The results revealed that A. altissima can be detected during different seasons and at specific flight heights. Male inflorescences were identifiable using an RGB sensor in the spring at <40 m, seed clusters were identifiable in summer and fall at <25 m using an RGB sensor, and remnant seed clusters were identifiable in the winter at <20 m using RGB and thermal sensors. Combining all seasonal data allowed for the identification of both male and female A. altissima. This study suggests that employing drones with optical sensors can provide a near real-time and efficient method for A. altissima detection. Such a tool has the potential to aid in the development of effective strategies for monitoring spotted lanternflies and managing A. altissima.","PeriodicalId":36448,"journal":{"name":"Drones","volume":" 1","pages":""},"PeriodicalIF":4.8,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138961825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the continuous expansion of Unmanned Aerial Vehicle (UAV) applications, the threat of icing on UAV flights has garnered increased attention. Understanding the icing principles and developing anti-icing technologies for unmanned aircraft is a crucial step in mitigating the icing threat. However, existing research indicates that changes in Reynolds numbers have a significant impact on the physics of ice accretion. Icing studies on aircraft operating at high Reynolds numbers cannot be directly applied to unmanned aircraft, and mature anti-icing/deicing techniques for manned aircraft cannot be directly utilized for UAVs. This paper firstly provides a comprehensive overview of research on icing for fixed-wing UAVs, including various methods to study unmanned aircraft icing and the identified characteristics of icing on unmanned aircraft. Secondly, this paper focuses on discussing UAV anti-icing/deicing techniques, including those currently applied and under development, and examines the advantages and disadvantages of these techniques. Finally, the paper presents some recommendations regarding UAV icing research and the development of anti-icing/deicing techniques.
{"title":"A Review of Icing Research and Development of Icing Mitigation Techniques for Fixed-Wing UAVs","authors":"Liang Zhou, Xian Yi, Qinglin Liu","doi":"10.3390/drones7120709","DOIUrl":"https://doi.org/10.3390/drones7120709","url":null,"abstract":"With the continuous expansion of Unmanned Aerial Vehicle (UAV) applications, the threat of icing on UAV flights has garnered increased attention. Understanding the icing principles and developing anti-icing technologies for unmanned aircraft is a crucial step in mitigating the icing threat. However, existing research indicates that changes in Reynolds numbers have a significant impact on the physics of ice accretion. Icing studies on aircraft operating at high Reynolds numbers cannot be directly applied to unmanned aircraft, and mature anti-icing/deicing techniques for manned aircraft cannot be directly utilized for UAVs. This paper firstly provides a comprehensive overview of research on icing for fixed-wing UAVs, including various methods to study unmanned aircraft icing and the identified characteristics of icing on unmanned aircraft. Secondly, this paper focuses on discussing UAV anti-icing/deicing techniques, including those currently applied and under development, and examines the advantages and disadvantages of these techniques. Finally, the paper presents some recommendations regarding UAV icing research and the development of anti-icing/deicing techniques.","PeriodicalId":36448,"journal":{"name":"Drones","volume":"11 9","pages":""},"PeriodicalIF":4.8,"publicationDate":"2023-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138995381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Robin John ap Lewis Hartley, I. Henderson, Chris Lewis Jackson
In the original publication, there was a mistake in the legend for Table 1 [...]
在最初的出版物中，表 1 的图例有一处错误[......]
{"title":"Correction: Hartley et al. BVLOS Unmanned Aircraft Operations in Forest Environments. Drones 2022, 6, 167","authors":"Robin John ap Lewis Hartley, I. Henderson, Chris Lewis Jackson","doi":"10.3390/drones7120708","DOIUrl":"https://doi.org/10.3390/drones7120708","url":null,"abstract":"In the original publication, there was a mistake in the legend for Table 1 [...]","PeriodicalId":36448,"journal":{"name":"Drones","volume":"31 2","pages":""},"PeriodicalIF":4.8,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138996491","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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