Richard A. Pickett, John W. Nowlin, Ahmed A. Hashem, Michele L. Reba, Joseph H. Massey, Scott Alsbrook
Advances in remote sensing and small unmanned aircraft systems (sUAS) have been applied to various precision agriculture applications. However, there has been limited research on the accuracy of real-time kinematic (RTK) sUAS photogrammetric elevation surveys, especially in preparation for precision agriculture practices that require precise topographic surfaces, such as increasing irrigation system efficiency. These practices include, but are not limited to, precision land grading, placement of levees, multiple inlet rice irrigation, and computerized hole size selection for furrow irrigation. All such practices rely, in some way, on the characterization of surface topography. While agro-terrestrial (ground-based) surveying is the dominant method of agricultural surveying, aerial surveying is emerging and attracting potential early adopters. This is the first study of its kind to assess the accuracy, precision, time, and cost efficiency of RTK sUAS surveying in comparison to traditional agro-terrestrial techniques. Our findings suggest sUAS are superior to ground survey methods in terms of relative elevation and produce much more precise raster surfaces than ground-based methods. We also showed that this emergent technology reduces costs and the time it takes to generate agricultural elevation surveys.
{"title":"Small Unmanned Aircraft Systems and Agro-Terrestrial Surveys Comparison for Generating Digital Elevation Surfaces for Irrigation and Precision Grading","authors":"Richard A. Pickett, John W. Nowlin, Ahmed A. Hashem, Michele L. Reba, Joseph H. Massey, Scott Alsbrook","doi":"10.3390/drones7110649","DOIUrl":"https://doi.org/10.3390/drones7110649","url":null,"abstract":"Advances in remote sensing and small unmanned aircraft systems (sUAS) have been applied to various precision agriculture applications. However, there has been limited research on the accuracy of real-time kinematic (RTK) sUAS photogrammetric elevation surveys, especially in preparation for precision agriculture practices that require precise topographic surfaces, such as increasing irrigation system efficiency. These practices include, but are not limited to, precision land grading, placement of levees, multiple inlet rice irrigation, and computerized hole size selection for furrow irrigation. All such practices rely, in some way, on the characterization of surface topography. While agro-terrestrial (ground-based) surveying is the dominant method of agricultural surveying, aerial surveying is emerging and attracting potential early adopters. This is the first study of its kind to assess the accuracy, precision, time, and cost efficiency of RTK sUAS surveying in comparison to traditional agro-terrestrial techniques. Our findings suggest sUAS are superior to ground survey methods in terms of relative elevation and produce much more precise raster surfaces than ground-based methods. We also showed that this emergent technology reduces costs and the time it takes to generate agricultural elevation surveys.","PeriodicalId":36448,"journal":{"name":"Drones","volume":"4 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134908458","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}
Dongyue Du, Min Chang, Linkai Tang, Haodong Zou, Chu Tang, Junqiang Bai
One of the most essential approaches to expanding the capabilities of autonomous systems is through collaborative operation. A separated lift and thrust vertical takeoff and landing mother unmanned aerial vehicle (UAV) and a quadrotor child UAV are used in this study for an autonomous recovery mission in an aerial child–mother unmanned system. We investigate the model predictive control (MPC) trajectory generator and the nonlinear trajectory tracking controller to solve the landing trajectory planning and high-speed trajectory tracking control problems of the child UAV in autonomous recovery missions. On this basis, the estimation of the mother UAV movement state is introduced and the autonomous recovery control framework is formed. The suggested control system framework in this research is validated using software-in-the-loop simulation. The simulation results show that the framework can not only direct the child UAV to complete the autonomous recovery while the mother UAV is hovering but also keep the child UAV tracking the recovery platform at a speed of at least 11 m/s while also guiding the child UAV to a safe landing.
{"title":"Trajectory Planning and Control Design for Aerial Autonomous Recovery of a Quadrotor","authors":"Dongyue Du, Min Chang, Linkai Tang, Haodong Zou, Chu Tang, Junqiang Bai","doi":"10.3390/drones7110648","DOIUrl":"https://doi.org/10.3390/drones7110648","url":null,"abstract":"One of the most essential approaches to expanding the capabilities of autonomous systems is through collaborative operation. A separated lift and thrust vertical takeoff and landing mother unmanned aerial vehicle (UAV) and a quadrotor child UAV are used in this study for an autonomous recovery mission in an aerial child–mother unmanned system. We investigate the model predictive control (MPC) trajectory generator and the nonlinear trajectory tracking controller to solve the landing trajectory planning and high-speed trajectory tracking control problems of the child UAV in autonomous recovery missions. On this basis, the estimation of the mother UAV movement state is introduced and the autonomous recovery control framework is formed. The suggested control system framework in this research is validated using software-in-the-loop simulation. The simulation results show that the framework can not only direct the child UAV to complete the autonomous recovery while the mother UAV is hovering but also keep the child UAV tracking the recovery platform at a speed of at least 11 m/s while also guiding the child UAV to a safe landing.","PeriodicalId":36448,"journal":{"name":"Drones","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134909159","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}
Unoccupied Aerial Vehicles (UAVs) are a widely applied tool used to monitor shallow water habitats. A recurrent issue when conducting UAV-based monitoring of submerged habitats is the collection of ground-truthing data needed as training and validation samples for the classification of aerial imagery, as well as for the identification of ecologically relevant information such as the vegetation depth limit. To address these limitations, a payload system was developed to collect subsurface data in the form of videos and depth measurements. In a 7 ha large study area, 136 point observations were collected and subsequently used to (1) train and validate the object-based classification of aerial imagery, (2) create a class distribution map based on the interpolation of point observations, (3) identify additional ecological relevant information and (4) create a bathymetry map of the study area. The classification based on ground-truthing samples achieved an overall accuracy of 98% and agreed to 84% with the class distribution map based on point interpolation. Additional ecologically relevant information, such as the vegetation depth limit, was recorded, and a bathymetry map of the study site was created. The findings of this study show that UAV-based shallow-water monitoring can be improved by applying the proposed tool.
{"title":"UAV-Based Subsurface Data Collection Using a Low-Tech Ground-Truthing Payload System Enhances Shallow-Water Monitoring","authors":"Aris Thomasberger, Mette Møller Nielsen","doi":"10.3390/drones7110647","DOIUrl":"https://doi.org/10.3390/drones7110647","url":null,"abstract":"Unoccupied Aerial Vehicles (UAVs) are a widely applied tool used to monitor shallow water habitats. A recurrent issue when conducting UAV-based monitoring of submerged habitats is the collection of ground-truthing data needed as training and validation samples for the classification of aerial imagery, as well as for the identification of ecologically relevant information such as the vegetation depth limit. To address these limitations, a payload system was developed to collect subsurface data in the form of videos and depth measurements. In a 7 ha large study area, 136 point observations were collected and subsequently used to (1) train and validate the object-based classification of aerial imagery, (2) create a class distribution map based on the interpolation of point observations, (3) identify additional ecological relevant information and (4) create a bathymetry map of the study area. The classification based on ground-truthing samples achieved an overall accuracy of 98% and agreed to 84% with the class distribution map based on point interpolation. Additional ecologically relevant information, such as the vegetation depth limit, was recorded, and a bathymetry map of the study site was created. The findings of this study show that UAV-based shallow-water monitoring can be improved by applying the proposed tool.","PeriodicalId":36448,"journal":{"name":"Drones","volume":"2 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135170782","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}
Xu Kang, Yu Shao, Guanbing Bai, He Sun, Tao Zhang, Dejiang Wang
Utilizing the optical characteristics of the target for detection and localization does not require actively emitting signals and has the advantage of strong concealment. Once the optoelectronic platform mounted on the unmanned aerial vehicle (UAV) detects the target, the vector pointing to the target in the camera coordinate system can estimate the angle of arrival (AOA) of the target relative to the UAV in the Earth-centered Earth-fixed (ECEF) coordinate system through a series of rotation transformations. By employing two UAVs and the corresponding AOA measurements, passive localization of an unknown target is possible. To achieve high-precision target localization, this paper investigates the following three aspects. Firstly, two error transfer models are established to estimate the noise distributions of the AOA and the UAV position in the ECEF coordinate system. Next, to reduce estimation errors, a weighted least squares (WLS) estimator is designed. Theoretical analysis proves that the mean squared error (MSE) of the target position estimation can reach the Cramér–Rao lower bound (CRLB) under the condition of small noise. Finally, we study the optimal placement problem of two coplanar UAVs relative to the target based on the D-optimality criterion and provide explicit conclusions. Simulation experiments validate the effectiveness of the localization method.
{"title":"Dual-UAV Collaborative High-Precision Passive Localization Method Based on Optoelectronic Platform","authors":"Xu Kang, Yu Shao, Guanbing Bai, He Sun, Tao Zhang, Dejiang Wang","doi":"10.3390/drones7110646","DOIUrl":"https://doi.org/10.3390/drones7110646","url":null,"abstract":"Utilizing the optical characteristics of the target for detection and localization does not require actively emitting signals and has the advantage of strong concealment. Once the optoelectronic platform mounted on the unmanned aerial vehicle (UAV) detects the target, the vector pointing to the target in the camera coordinate system can estimate the angle of arrival (AOA) of the target relative to the UAV in the Earth-centered Earth-fixed (ECEF) coordinate system through a series of rotation transformations. By employing two UAVs and the corresponding AOA measurements, passive localization of an unknown target is possible. To achieve high-precision target localization, this paper investigates the following three aspects. Firstly, two error transfer models are established to estimate the noise distributions of the AOA and the UAV position in the ECEF coordinate system. Next, to reduce estimation errors, a weighted least squares (WLS) estimator is designed. Theoretical analysis proves that the mean squared error (MSE) of the target position estimation can reach the Cramér–Rao lower bound (CRLB) under the condition of small noise. Finally, we study the optimal placement problem of two coplanar UAVs relative to the target based on the D-optimality criterion and provide explicit conclusions. Simulation experiments validate the effectiveness of the localization method.","PeriodicalId":36448,"journal":{"name":"Drones","volume":"25 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135112471","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}
Jokūbas Laukys, Bernardas Maršalka, Ignas Daugėla, Gintautas Stankūnavičius
The accurate and detailed measurement of the vertical temperature, humidity, pressure, and wind profiles of the atmosphere is pivotal for high-resolution numerical weather prediction, the determination of atmospheric stability, as well as investigation of small-scale phenomena such as urban heat islands. Traditional approaches, such as weather balloons, have been indispensable but are constrained by cost, environmental impact, and data sparsity. In this article, we investigate uncrewed aerial systems (UASs) as an innovative platform for in situ atmospheric probing. By comparing data from a drone-mounted semiconductor temperature sensor (TMP117) with traditional radiosonde measurements, we spotlight the UAS-collected atmospheric data’s accuracy and such system suitability for atmospheric surface layer measurement. Our research encountered challenges linked with the inherent delays in achieving ambient temperature readings. However, by applying specific data processing techniques, including smoothing methodologies like the Savitzky–Golay filter, iterative smoothing, time shift, and Newton’s law of cooling, we have improved the data accuracy and consistency. In this article, 28 flights were examined and certain patterns between different methodologies and sensors were observed. Temperature differentials were assessed over a range of 100 m. The article highlights a notable accuracy achievement of 0.16 ± 0.014 °C with 95% confidence when applying Newton’s law of cooling in comparison to a radiosonde RS41’s data. Our findings demonstrate the potential of UASs in capturing accurate high-resolution vertical temperature profiles. This work posits that UASs, with further refinements, could revolutionize atmospheric data collection.
{"title":"Drone-Based Vertical Atmospheric Temperature Profiling in Urban Environments","authors":"Jokūbas Laukys, Bernardas Maršalka, Ignas Daugėla, Gintautas Stankūnavičius","doi":"10.3390/drones7110645","DOIUrl":"https://doi.org/10.3390/drones7110645","url":null,"abstract":"The accurate and detailed measurement of the vertical temperature, humidity, pressure, and wind profiles of the atmosphere is pivotal for high-resolution numerical weather prediction, the determination of atmospheric stability, as well as investigation of small-scale phenomena such as urban heat islands. Traditional approaches, such as weather balloons, have been indispensable but are constrained by cost, environmental impact, and data sparsity. In this article, we investigate uncrewed aerial systems (UASs) as an innovative platform for in situ atmospheric probing. By comparing data from a drone-mounted semiconductor temperature sensor (TMP117) with traditional radiosonde measurements, we spotlight the UAS-collected atmospheric data’s accuracy and such system suitability for atmospheric surface layer measurement. Our research encountered challenges linked with the inherent delays in achieving ambient temperature readings. However, by applying specific data processing techniques, including smoothing methodologies like the Savitzky–Golay filter, iterative smoothing, time shift, and Newton’s law of cooling, we have improved the data accuracy and consistency. In this article, 28 flights were examined and certain patterns between different methodologies and sensors were observed. Temperature differentials were assessed over a range of 100 m. The article highlights a notable accuracy achievement of 0.16 ± 0.014 °C with 95% confidence when applying Newton’s law of cooling in comparison to a radiosonde RS41’s data. Our findings demonstrate the potential of UASs in capturing accurate high-resolution vertical temperature profiles. This work posits that UASs, with further refinements, could revolutionize atmospheric data collection.","PeriodicalId":36448,"journal":{"name":"Drones","volume":"78 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135266042","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 temporal monitoring of indicator plant species in high nature value grassland is crucial for nature conservation. However, traditional monitoring approaches are resource-intensive, straining limited funds and personnel. In this study, we demonstrate the capabilities of a repeated drone-based plant count for monitoring the population development of an indicator plant species (Dactylorhiza majalis (DM)) to address such challenges. We utilized multispectral very high-spatial-resolution drone data from two consecutive flowering seasons for exploiting a Random Forest- and a Neural Network-based remote sensing plant count (RSPC) approach. In comparison to in situ data, Random Forest-based RSPC achieved a better performance than Neural Network-based RSPC. We observed an R² of 0.8 and 0.63 and a RMSE of 8.5 and 11.4 DM individuals/m², respectively. The accuracies indicate a comparable performance to conventional plant count surveys. In a change detection setup, we assessed the population development of DM and observed an overall decline in DM individuals in the study site. Regions with an increasing DM count were small and the increase relatively low in magnitude. Additionally, we documented the success of a manual seed transfer of DM to a previously uninhabited area within our study site. We conclude that repeated drone surveys are indeed suitable to monitor the population development of indicator plant species with a spectrally prominent flower color. They provide a unique spatio-temporal perspective to aid practical nature conservation and document conservation efforts.
{"title":"Monitoring the Population Development of Indicator Plants in High Nature Value Grassland Using Machine Learning and Drone Data","authors":"Kim-Cedric Gröschler, Arnab Muhuri, Swalpa Kumar Roy, Natascha Oppelt","doi":"10.3390/drones7100644","DOIUrl":"https://doi.org/10.3390/drones7100644","url":null,"abstract":"The temporal monitoring of indicator plant species in high nature value grassland is crucial for nature conservation. However, traditional monitoring approaches are resource-intensive, straining limited funds and personnel. In this study, we demonstrate the capabilities of a repeated drone-based plant count for monitoring the population development of an indicator plant species (Dactylorhiza majalis (DM)) to address such challenges. We utilized multispectral very high-spatial-resolution drone data from two consecutive flowering seasons for exploiting a Random Forest- and a Neural Network-based remote sensing plant count (RSPC) approach. In comparison to in situ data, Random Forest-based RSPC achieved a better performance than Neural Network-based RSPC. We observed an R² of 0.8 and 0.63 and a RMSE of 8.5 and 11.4 DM individuals/m², respectively. The accuracies indicate a comparable performance to conventional plant count surveys. In a change detection setup, we assessed the population development of DM and observed an overall decline in DM individuals in the study site. Regions with an increasing DM count were small and the increase relatively low in magnitude. Additionally, we documented the success of a manual seed transfer of DM to a previously uninhabited area within our study site. We conclude that repeated drone surveys are indeed suitable to monitor the population development of indicator plant species with a spectrally prominent flower color. They provide a unique spatio-temporal perspective to aid practical nature conservation and document conservation efforts.","PeriodicalId":36448,"journal":{"name":"Drones","volume":"18 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135412927","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}
Energy consumption is a critical parameter in the development of helicopter Unmanned Aerial Vehicles (UAVs). Today, helicopter UAVs are playing an increasingly pivotal role in various applications, from surveillance and reconnaissance to package delivery and search and rescue missions. However, their energy efficiency remains a pressing issue, as it directly impacts their operational duration and payload capacity. One of the key challenges in optimizing energy consumption is the existence of excess power during flight, arising from the intricate interplay between helicopter aerodynamic behavior and safety design. Typically, this excess energy is dissipated, resulting in a suboptimal performance and efficiency. This study investigated the behavior of excess power in a helicopter Unmanned Aerial Vehicle (UAV). Typically, this excess energy is wasted in conventional helicopters and helicopter UAVs. A dual-method approach, encompassing numerical and experimental methodologies, was employed to provide comprehensive insights into the helicopter UAV’s performance under various conditions. Computational fluid dynamics (CFD) simulations were performed to analyze the UAV’s aerodynamics. The simulations were validated by comparing the lift force with wind tunnel experimental data, resulting in acceptable deviations. The experimental analysis was conducted using a wind tunnel and a small-sized helicopter UAV. The experiments were designed to examine the excess power behavior of the UAV under two distinct flight conditions: hover and forward flight. The power output from the generator and power input from the battery were measured under various angular velocities and pitch angles. The results revealed a maximum excess power of 6.84% for hover conditions and 9.83% for forward flight conditions. This indicates that the maximum excess power percentage attributable to the helicopter UAV’s safety measure is 6.84% and that resulting from aerodynamics is 2.99%. The findings of this study contribute valuable knowledge to the optimization of helicopter UAV performance and the potential for harnessing excess power during flight operations. When this excess energy is harnessed, it can contribute significantly to the overall performance and efficiency of the UAV, potentially extending its flight duration or accommodating additional payload capacity that could potentially pave the way for the development of hybrid helicopter UAV models in the future.
{"title":"Investigating and Analyzing the Potential for Regenerating Excess Energy in a Helicopter UAV","authors":"Chindanai Kodchaniphaphong, Jay-tawee Pukrushpan, Chaiwat Klumpol","doi":"10.3390/drones7100643","DOIUrl":"https://doi.org/10.3390/drones7100643","url":null,"abstract":"Energy consumption is a critical parameter in the development of helicopter Unmanned Aerial Vehicles (UAVs). Today, helicopter UAVs are playing an increasingly pivotal role in various applications, from surveillance and reconnaissance to package delivery and search and rescue missions. However, their energy efficiency remains a pressing issue, as it directly impacts their operational duration and payload capacity. One of the key challenges in optimizing energy consumption is the existence of excess power during flight, arising from the intricate interplay between helicopter aerodynamic behavior and safety design. Typically, this excess energy is dissipated, resulting in a suboptimal performance and efficiency. This study investigated the behavior of excess power in a helicopter Unmanned Aerial Vehicle (UAV). Typically, this excess energy is wasted in conventional helicopters and helicopter UAVs. A dual-method approach, encompassing numerical and experimental methodologies, was employed to provide comprehensive insights into the helicopter UAV’s performance under various conditions. Computational fluid dynamics (CFD) simulations were performed to analyze the UAV’s aerodynamics. The simulations were validated by comparing the lift force with wind tunnel experimental data, resulting in acceptable deviations. The experimental analysis was conducted using a wind tunnel and a small-sized helicopter UAV. The experiments were designed to examine the excess power behavior of the UAV under two distinct flight conditions: hover and forward flight. The power output from the generator and power input from the battery were measured under various angular velocities and pitch angles. The results revealed a maximum excess power of 6.84% for hover conditions and 9.83% for forward flight conditions. This indicates that the maximum excess power percentage attributable to the helicopter UAV’s safety measure is 6.84% and that resulting from aerodynamics is 2.99%. The findings of this study contribute valuable knowledge to the optimization of helicopter UAV performance and the potential for harnessing excess power during flight operations. When this excess energy is harnessed, it can contribute significantly to the overall performance and efficiency of the UAV, potentially extending its flight duration or accommodating additional payload capacity that could potentially pave the way for the development of hybrid helicopter UAV models in the future.","PeriodicalId":36448,"journal":{"name":"Drones","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135462913","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}
Gabriel Fontenla-Carrera, Enrique Aldao, Fernando Veiga, Higinio González-Jorge
Small fixed-wing electric Unmanned Aerial Vehicles (UAVs) are perfect candidates to perform tasks in wide areas, such as photogrammetry, surveillance, monitoring, or search and rescue, among others. They are easy to transport and assemble, have much greater range and autonomy, and reach higher speeds than rotatory-wing UAVs. Aiming to contribute towards their future implementation, the objective of this article is to benchmark commercial, small, fixed-wing, electric UAVs and compatible RGB cameras to find the best combination for photogrammetry and data acquisition of mussel seeds and goose barnacles in a multi-region intertidal zone of the south coast of Galicia (NW of Spain). To compare all the options, a Coverage Path Planning (CPP) algorithm enhanced for fixed-wing UAVs to cover long areas with sharp corners was posed, followed by a Traveling Salesman Problem (TSP) to find the best route between regions. Results show that two options stand out from the rest: the Delair DT26 Open Payload with a PhaseOne iXM-100 camera (shortest path, minimum number of pictures and turns) and the Heliplane LRS 340 PRO with the Sony Alpha 7R IV sensor, finishing the task in the minimum time.
{"title":"A Benchmarking of Commercial Small Fixed-Wing Electric UAVs and RGB Cameras for Photogrammetry Monitoring in Intertidal Multi-Regions","authors":"Gabriel Fontenla-Carrera, Enrique Aldao, Fernando Veiga, Higinio González-Jorge","doi":"10.3390/drones7100642","DOIUrl":"https://doi.org/10.3390/drones7100642","url":null,"abstract":"Small fixed-wing electric Unmanned Aerial Vehicles (UAVs) are perfect candidates to perform tasks in wide areas, such as photogrammetry, surveillance, monitoring, or search and rescue, among others. They are easy to transport and assemble, have much greater range and autonomy, and reach higher speeds than rotatory-wing UAVs. Aiming to contribute towards their future implementation, the objective of this article is to benchmark commercial, small, fixed-wing, electric UAVs and compatible RGB cameras to find the best combination for photogrammetry and data acquisition of mussel seeds and goose barnacles in a multi-region intertidal zone of the south coast of Galicia (NW of Spain). To compare all the options, a Coverage Path Planning (CPP) algorithm enhanced for fixed-wing UAVs to cover long areas with sharp corners was posed, followed by a Traveling Salesman Problem (TSP) to find the best route between regions. Results show that two options stand out from the rest: the Delair DT26 Open Payload with a PhaseOne iXM-100 camera (shortest path, minimum number of pictures and turns) and the Heliplane LRS 340 PRO with the Sony Alpha 7R IV sensor, finishing the task in the minimum time.","PeriodicalId":36448,"journal":{"name":"Drones","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135570074","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) are able to act as movable aerial base stations to enhance wireless coverage for edge users with poor ground communication quality. However, in urban environments, the link between UAVs and ground users can be blocked by obstacles, especially when complicated terrestrial infrastructures increase the probability of non-line-of-sight (NLoS) links. In this paper, in order to improve the average throughput, we propose a multi-UAV multicast system, where a multi-agent reinforcement learning method is utilized to help UAVs determine the optimal altitude and trajectory. Intelligent reflective surfaces (IRSs) are also employed to reflect signals to solve the blocking problem. Furthermore, since the UAV’s onboard power is limited, this paper aims to minimize the UAVs’ energy consumption and maximize the transmission rate for edge users by jointly optimizing the UAVs’ 3D trajectory and transmit power. Firstly, we deduce the channel capacity of ground users in different multicast groups. Subsequently, the K-medoids algorithm is utilized for the multicast grouping problem of edge users based on transmission rate requirements. Then, we employ the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm to learn an optimal solution and eliminate the non-stationarity of multi-agent training. Finally, the simulation results show that the proposed system can increase the average throughput by 14% approximately compared to the non-grouping system, and the MADDPG algorithm can achieve a 20% improvement in reducing the energy consumption of UAVs compared to traditional deep reinforcement learning (DRL) methods.
{"title":"Three-Dimensional Trajectory and Resource Allocation Optimization in Multi-Unmanned Aerial Vehicle Multicast System: A Multi-Agent Reinforcement Learning Method","authors":"Dongyu Wang, Yue Liu, Hongda Yu, Yanzhao Hou","doi":"10.3390/drones7100641","DOIUrl":"https://doi.org/10.3390/drones7100641","url":null,"abstract":"Unmanned aerial vehicles (UAVs) are able to act as movable aerial base stations to enhance wireless coverage for edge users with poor ground communication quality. However, in urban environments, the link between UAVs and ground users can be blocked by obstacles, especially when complicated terrestrial infrastructures increase the probability of non-line-of-sight (NLoS) links. In this paper, in order to improve the average throughput, we propose a multi-UAV multicast system, where a multi-agent reinforcement learning method is utilized to help UAVs determine the optimal altitude and trajectory. Intelligent reflective surfaces (IRSs) are also employed to reflect signals to solve the blocking problem. Furthermore, since the UAV’s onboard power is limited, this paper aims to minimize the UAVs’ energy consumption and maximize the transmission rate for edge users by jointly optimizing the UAVs’ 3D trajectory and transmit power. Firstly, we deduce the channel capacity of ground users in different multicast groups. Subsequently, the K-medoids algorithm is utilized for the multicast grouping problem of edge users based on transmission rate requirements. Then, we employ the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm to learn an optimal solution and eliminate the non-stationarity of multi-agent training. Finally, the simulation results show that the proposed system can increase the average throughput by 14% approximately compared to the non-grouping system, and the MADDPG algorithm can achieve a 20% improvement in reducing the energy consumption of UAVs compared to traditional deep reinforcement learning (DRL) methods.","PeriodicalId":36448,"journal":{"name":"Drones","volume":"194 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135778685","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}
A data-driven geometric guidance method is proposed for the multi-constrained guidance problem of variable-velocity unmanned aerial vehicles (UAVs). Firstly, a two-phase flight trajectory based on a log-aesthetic space curve (LASC) is designed. The impact angle is satisfied by a specified straight-line segment. The impact time is controlled by adjusting the phase switching point. Secondly, a deep neural network is trained offline to establish the mapping relationship between the initial conditions and desired trajectory parameters. Based on this mapping network, the desired flight trajectory can be generated rapidly and precisely. Finally, the pure pursuit and line-of-sight (PLOS) algorithm is employed to generate guidance commands. The numerical simulation results validate the effectiveness and superiority of the proposed method in terms of impact time and angle control under time-varying velocity.
{"title":"Multi-Constrained Geometric Guidance Law with a Data-Driven Method","authors":"Xinghui Yan, Yuzhong Tang, Yulei Xu, Heng Shi, Jihong Zhu","doi":"10.3390/drones7100639","DOIUrl":"https://doi.org/10.3390/drones7100639","url":null,"abstract":"A data-driven geometric guidance method is proposed for the multi-constrained guidance problem of variable-velocity unmanned aerial vehicles (UAVs). Firstly, a two-phase flight trajectory based on a log-aesthetic space curve (LASC) is designed. The impact angle is satisfied by a specified straight-line segment. The impact time is controlled by adjusting the phase switching point. Secondly, a deep neural network is trained offline to establish the mapping relationship between the initial conditions and desired trajectory parameters. Based on this mapping network, the desired flight trajectory can be generated rapidly and precisely. Finally, the pure pursuit and line-of-sight (PLOS) algorithm is employed to generate guidance commands. The numerical simulation results validate the effectiveness and superiority of the proposed method in terms of impact time and angle control under time-varying velocity.","PeriodicalId":36448,"journal":{"name":"Drones","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135882948","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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