The intelligent terminals deployed in hydropower IoT can quickly sense the status of hydropower equipment, thus improving the efficiency of system control and operation. However, communication security between the base station and intelligent terminals challenges the IoT hydropower plant. In this paper, we propose a UAV-assisted covert communication system (CCS), where a UAV acts as the base station to provide communication service to ground terminals monitored by malicious users. To improve access effectiveness, we adopt non-orthogonal multiple access (NOMA) for intelligent terminals to access the hydropower IoT. Since two devices can synchronously access the communication system with the NOMA scheme, we select one terminal to receive covert messages and the other to interfere with the malicious users to detect confidential communications. To maximize the covert rate, we formulate the optimization problem that jointly optimizes the transmit power, the altitude of the UAV, and trajectory under the constraints of covertness and the finite length of the transmission message block. Additionally, we transform the optimization problem into a geometric planning one, which is solved by a developed sequential geometric planning (SGP) approximation algorithm. Simulation results show the proposed algorithm can improve the covert rate compared to the traditional methods.
{"title":"On Joint Optimization of UAV-Assisted Covert Communication Systems with NOMA for Hydropower Internet of Things","authors":"Zhenchun Le, Qing Xu, Yining Wang, Guowen Hao, Weifeng Pan, Yanlin Sun, Yuwen Qian","doi":"10.3390/drones7100610","DOIUrl":"https://doi.org/10.3390/drones7100610","url":null,"abstract":"The intelligent terminals deployed in hydropower IoT can quickly sense the status of hydropower equipment, thus improving the efficiency of system control and operation. However, communication security between the base station and intelligent terminals challenges the IoT hydropower plant. In this paper, we propose a UAV-assisted covert communication system (CCS), where a UAV acts as the base station to provide communication service to ground terminals monitored by malicious users. To improve access effectiveness, we adopt non-orthogonal multiple access (NOMA) for intelligent terminals to access the hydropower IoT. Since two devices can synchronously access the communication system with the NOMA scheme, we select one terminal to receive covert messages and the other to interfere with the malicious users to detect confidential communications. To maximize the covert rate, we formulate the optimization problem that jointly optimizes the transmit power, the altitude of the UAV, and trajectory under the constraints of covertness and the finite length of the transmission message block. Additionally, we transform the optimization problem into a geometric planning one, which is solved by a developed sequential geometric planning (SGP) approximation algorithm. Simulation results show the proposed algorithm can improve the covert rate compared to the traditional methods.","PeriodicalId":36448,"journal":{"name":"Drones","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135579945","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}
Future Unmanned Aerial Vehicle (UAV)-assisted wireless communication systems are expected to utilize wide bandwidths available at terahertz (THz) frequencies to enhance system throughput. To compensate for the severe path loss in the THz band, it is essential to have a multitude of antennas in the UAV to generate narrow beams for directional transmission. However, narrow beams severely limit its spatial coverage, which greatly affects the efficiency of large-scale access UAV-assisted THz systems. Moreover, the combination of massive antennas and large bandwidth at THz makes the misalignment of the beams caused by beam squint non-negligible and also high energy consumption. UAV-assisted communication technology can effectively increase spatial coverage and provide reliable LoS communication links. In addition, reducing the number of radio frequency (RF) chains while ensuring the number of transmitted data streams and space division multiplexing capability is also an effective way to reduce energy consumption in the UAV communication. In this paper, a single RF chain uniform planar array (UPA) with true-time-delays (TTDs) is equipped on the UAV to achieve two dimensional (2D) beams and split spatial beams to improve transmission efficiency. We analyze the 2D beam squint of the UPA and design a time-delay phased UPA for UAV-assisted THz communication systems. By introducing TTDs between the single RF chain and phase shifters, the beam squint can be controlled flexibly by introducing the delay between each antenna. When TTDs are arranged in both the horizontal and vertical dimensions, the coverage of the beams becomes more complicated compared to uniform linear arrays (ULA). Simulation results show that the proposed time-delay phased UPA can achieve better performance in both single-beam and multi-beam modes for single user and multi-user scenarios compared with conventional phased UPA, respectively. In addition, we propose frequency division beam multiple access (FDBMA) multi access technology, which achieves more efficient multi access by reusing resources from different frequency beam pairs. Finally, the results also show that the enlargement of the beamwidth through the proposed FDBMA strategy can also increase the performance in multi-user scenarios.
{"title":"UAV-Assisted Wideband Terahertz Wireless Communications with Time-Delay Phased UPA under Beam Squint","authors":"Hao Huang, Qinghe Zheng, Hikmet Sari","doi":"10.3390/drones7100608","DOIUrl":"https://doi.org/10.3390/drones7100608","url":null,"abstract":"Future Unmanned Aerial Vehicle (UAV)-assisted wireless communication systems are expected to utilize wide bandwidths available at terahertz (THz) frequencies to enhance system throughput. To compensate for the severe path loss in the THz band, it is essential to have a multitude of antennas in the UAV to generate narrow beams for directional transmission. However, narrow beams severely limit its spatial coverage, which greatly affects the efficiency of large-scale access UAV-assisted THz systems. Moreover, the combination of massive antennas and large bandwidth at THz makes the misalignment of the beams caused by beam squint non-negligible and also high energy consumption. UAV-assisted communication technology can effectively increase spatial coverage and provide reliable LoS communication links. In addition, reducing the number of radio frequency (RF) chains while ensuring the number of transmitted data streams and space division multiplexing capability is also an effective way to reduce energy consumption in the UAV communication. In this paper, a single RF chain uniform planar array (UPA) with true-time-delays (TTDs) is equipped on the UAV to achieve two dimensional (2D) beams and split spatial beams to improve transmission efficiency. We analyze the 2D beam squint of the UPA and design a time-delay phased UPA for UAV-assisted THz communication systems. By introducing TTDs between the single RF chain and phase shifters, the beam squint can be controlled flexibly by introducing the delay between each antenna. When TTDs are arranged in both the horizontal and vertical dimensions, the coverage of the beams becomes more complicated compared to uniform linear arrays (ULA). Simulation results show that the proposed time-delay phased UPA can achieve better performance in both single-beam and multi-beam modes for single user and multi-user scenarios compared with conventional phased UPA, respectively. In addition, we propose frequency division beam multiple access (FDBMA) multi access technology, which achieves more efficient multi access by reusing resources from different frequency beam pairs. Finally, the results also show that the enlargement of the beamwidth through the proposed FDBMA strategy can also increase the performance in multi-user scenarios.","PeriodicalId":36448,"journal":{"name":"Drones","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135536908","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}
Kaiyu Hu, Huanlin Li, Jiafan Zhuang, Zhifeng Hao, Zhun Fan
The autonomous navigation of aerial robots in unknown and complex outdoor environments is a challenging problem that typically requires planners to generate collision-free trajectories based on human expert rules for fast navigation. Presently, aerial robots suffer from high latency in acquiring environmental information, which limits the control strategies that the vehicle can implement. In this study, we proposed the SAC_FAE algorithm for high-speed navigation in complex environments using deep reinforcement learning (DRL) policies. Our approach consisted of a soft actor–critic (SAC) algorithm and a focus autoencoder (FAE). Our end-to-end DRL navigation policy enabled a flying robot to efficiently accomplish navigation tasks without prior map information by relying solely on the front-end depth frames and its own pose information. The proposed algorithm outperformed existing trajectory-based optimization approaches at flight speeds exceeding 3 m/s in multiple testing environments, which demonstrates its effectiveness and efficiency.
{"title":"Efficient Focus Autoencoders for Fast Autonomous Flight in Intricate Wild Scenarios","authors":"Kaiyu Hu, Huanlin Li, Jiafan Zhuang, Zhifeng Hao, Zhun Fan","doi":"10.3390/drones7100609","DOIUrl":"https://doi.org/10.3390/drones7100609","url":null,"abstract":"The autonomous navigation of aerial robots in unknown and complex outdoor environments is a challenging problem that typically requires planners to generate collision-free trajectories based on human expert rules for fast navigation. Presently, aerial robots suffer from high latency in acquiring environmental information, which limits the control strategies that the vehicle can implement. In this study, we proposed the SAC_FAE algorithm for high-speed navigation in complex environments using deep reinforcement learning (DRL) policies. Our approach consisted of a soft actor–critic (SAC) algorithm and a focus autoencoder (FAE). Our end-to-end DRL navigation policy enabled a flying robot to efficiently accomplish navigation tasks without prior map information by relying solely on the front-end depth frames and its own pose information. The proposed algorithm outperformed existing trajectory-based optimization approaches at flight speeds exceeding 3 m/s in multiple testing environments, which demonstrates its effectiveness and efficiency.","PeriodicalId":36448,"journal":{"name":"Drones","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135580068","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 leaf area index (LAI) is an important indicator for crop growth monitoring. This study aims to analyze the effects of different data fusion strategies on the performance of LAI prediction models, using multisource images from unmanned aerial vehicles (UAVs). For this purpose, maize field experiments were conducted to obtain plants with different growth status. LAI and corresponding multispectral (MS) and RGB images were collected at different maize growth stages. Based on these data, different model design scenarios, including single-source image scenarios, pixel-level multisource data fusion scenarios, and feature-level multisource data fusion scenarios, were created. Then, stepwise multiple linear regression (SMLR) was used to design LAI prediction models. The performance of models were compared and the results showed that (i) combining spectral and texture features to predict LAI performs better than using only spectral or texture information; (ii) compared with using single-source images, using a multisource data fusion strategy can improve the performance of the model to predict LAI; and (iii) among the different multisource data fusion strategies, the feature-level data fusion strategy performed better than the pixel-level fusion strategy in the LAI prediction models. Thus, a feature-level data fusion strategy is recommended for the creation of maize LAI prediction models using multisource UAV images.
{"title":"A Comparison of Different Data Fusion Strategies’ Effects on Maize Leaf Area Index Prediction Using Multisource Data from Unmanned Aerial Vehicles (UAVs)","authors":"Junwei Ma, Pengfei Chen, Lijuan Wang","doi":"10.3390/drones7100605","DOIUrl":"https://doi.org/10.3390/drones7100605","url":null,"abstract":"The leaf area index (LAI) is an important indicator for crop growth monitoring. This study aims to analyze the effects of different data fusion strategies on the performance of LAI prediction models, using multisource images from unmanned aerial vehicles (UAVs). For this purpose, maize field experiments were conducted to obtain plants with different growth status. LAI and corresponding multispectral (MS) and RGB images were collected at different maize growth stages. Based on these data, different model design scenarios, including single-source image scenarios, pixel-level multisource data fusion scenarios, and feature-level multisource data fusion scenarios, were created. Then, stepwise multiple linear regression (SMLR) was used to design LAI prediction models. The performance of models were compared and the results showed that (i) combining spectral and texture features to predict LAI performs better than using only spectral or texture information; (ii) compared with using single-source images, using a multisource data fusion strategy can improve the performance of the model to predict LAI; and (iii) among the different multisource data fusion strategies, the feature-level data fusion strategy performed better than the pixel-level fusion strategy in the LAI prediction models. Thus, a feature-level data fusion strategy is recommended for the creation of maize LAI prediction models using multisource UAV images.","PeriodicalId":36448,"journal":{"name":"Drones","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135719170","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}
Many birds in the natural world are capable of engaging in sustained soaring within thermal updrafts for extended periods without flapping their wings. Autonomous soaring has the potential to greatly improve both the range and endurance of small drones. In this paper, the extended Kalman filter (EKF) thermal updraft center prediction method based on ordinary least squares (OLS) is proposed to develop the autonomous soaring system for small drones, and an adaptive step size update strategy is incorporated into the EKF. The proposed method is compared with EKF thermal updraft prediction methods through simulated experiments. The results indicate that the proposed prediction method has low computational complexity and fast convergence speed and performs more stably in weak thermal updrafts. The above advantages stem from the OLS providing an approximate distribution of the thermal updraft around the drone for the EKF. This empowers the EKF algorithm with ample information to dynamically update the thermal updraft center in real time. The adaptive step size update strategy further accelerates the convergence speed of this process. In addition, flight experiments were conducted on the Talon fixed-wing drone platform to test the autonomous soaring system. During the flight experiment, the drone successfully engaged in static soaring within thermal updrafts, effectively hovering and gaining energy. Throughout the approximately 40 min flight duration, the drone only utilized its propulsion for about 8 min. This demonstrated the effectiveness of the autonomous soaring system using the EKF thermal updraft center prediction method based on OLS. Finally, by analyzing and discussing the differences between the simulation experiment results and the flight experiment results, some improvement strategies for the current work are proposed.
{"title":"An Autonomous Soaring for Small Drones Using the Extended Kalman Filter Thermal Updraft Center Prediction Method Based on Ordinary Least Squares","authors":"Weigang An, Tianyu Lin, Peng Zhang","doi":"10.3390/drones7100603","DOIUrl":"https://doi.org/10.3390/drones7100603","url":null,"abstract":"Many birds in the natural world are capable of engaging in sustained soaring within thermal updrafts for extended periods without flapping their wings. Autonomous soaring has the potential to greatly improve both the range and endurance of small drones. In this paper, the extended Kalman filter (EKF) thermal updraft center prediction method based on ordinary least squares (OLS) is proposed to develop the autonomous soaring system for small drones, and an adaptive step size update strategy is incorporated into the EKF. The proposed method is compared with EKF thermal updraft prediction methods through simulated experiments. The results indicate that the proposed prediction method has low computational complexity and fast convergence speed and performs more stably in weak thermal updrafts. The above advantages stem from the OLS providing an approximate distribution of the thermal updraft around the drone for the EKF. This empowers the EKF algorithm with ample information to dynamically update the thermal updraft center in real time. The adaptive step size update strategy further accelerates the convergence speed of this process. In addition, flight experiments were conducted on the Talon fixed-wing drone platform to test the autonomous soaring system. During the flight experiment, the drone successfully engaged in static soaring within thermal updrafts, effectively hovering and gaining energy. Throughout the approximately 40 min flight duration, the drone only utilized its propulsion for about 8 min. This demonstrated the effectiveness of the autonomous soaring system using the EKF thermal updraft center prediction method based on OLS. Finally, by analyzing and discussing the differences between the simulation experiment results and the flight experiment results, some improvement strategies for the current work are proposed.","PeriodicalId":36448,"journal":{"name":"Drones","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134958473","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}
Due to the hostile marine environment, there will inevitably be unpredictable factors during the operation of unmanned underwater vehicles, including changes in ocean currents, hull dimensions, and velocity measurement uncertainties. An improved finite-time adaptive tracking control issue is considered for autonomous underwater vehicles (AUVs) with uncertain dynamics, unknown external disturbances, and unavailable speed information. A state observer is designed to estimate the position and velocity of the vehicle via a neural network (NN) approach. The NN is used to estimate uncertainties and external disturbances. A finite-time controller is designed via backstepping and command filter techniques. A multi-input multi-output (MIMO) filter for AUVs is established, and the corresponding MIMO filter compensation signal is constructed to eliminate the effect of filtering error. All the signals of the closed-loop system are proved to be finite-time bounded. An example with comparison is given to show the effectiveness of our method.
{"title":"An Observer-Based Adaptive Neural Network Finite-Time Tracking Control for Autonomous Underwater Vehicles via Command Filters","authors":"Jun Guo, Jun Wang, Yuming Bo","doi":"10.3390/drones7100604","DOIUrl":"https://doi.org/10.3390/drones7100604","url":null,"abstract":"Due to the hostile marine environment, there will inevitably be unpredictable factors during the operation of unmanned underwater vehicles, including changes in ocean currents, hull dimensions, and velocity measurement uncertainties. An improved finite-time adaptive tracking control issue is considered for autonomous underwater vehicles (AUVs) with uncertain dynamics, unknown external disturbances, and unavailable speed information. A state observer is designed to estimate the position and velocity of the vehicle via a neural network (NN) approach. The NN is used to estimate uncertainties and external disturbances. A finite-time controller is designed via backstepping and command filter techniques. A multi-input multi-output (MIMO) filter for AUVs is established, and the corresponding MIMO filter compensation signal is constructed to eliminate the effect of filtering error. All the signals of the closed-loop system are proved to be finite-time bounded. An example with comparison is given to show the effectiveness of our method.","PeriodicalId":36448,"journal":{"name":"Drones","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135719676","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}
Yan Wu, Mingtao Nie, Xiaolei Ma, Yicong Guo, Xiaoxiong Liu
Multi-UAV cooperative path planning is a key technology to carry out multi-UAV tasks, and its research has important practical significance. A multi-UAV cooperative path is a combination of single-UAV paths, so the idea of problem decomposition is effective to deal with multi-UAV cooperative path planning. With this analysis, a multi-UAV cooperative path planning algorithm based on co-evolution optimization was proposed in this paper. Firstly, by analyzing the meaning of multi-UAV cooperative flight, the optimization model of multi-UAV cooperative path planning was given. Secondly, we designed the cost function of multiple UAVs with the penalty function method to deal with multiple constraints and designed two information-sharing strategies to deal with the combination path search between multiple UAVs. The two information-sharing strategies were called the optimal individual selection strategy and the mixed selection strategy. The new cooperative path planning algorithm was presented by combining the above designation and co-evolution algorithm. Finally, the proposed algorithm is applied to a rendezvous task in complex environments and compared with two evolutionary algorithms. The experimental results show that the proposed algorithm can effectively cope with the multi-UAV cooperative path planning problem in complex environments.
{"title":"Co-Evolutionary Algorithm-Based Multi-Unmanned Aerial Vehicle Cooperative Path Planning","authors":"Yan Wu, Mingtao Nie, Xiaolei Ma, Yicong Guo, Xiaoxiong Liu","doi":"10.3390/drones7100606","DOIUrl":"https://doi.org/10.3390/drones7100606","url":null,"abstract":"Multi-UAV cooperative path planning is a key technology to carry out multi-UAV tasks, and its research has important practical significance. A multi-UAV cooperative path is a combination of single-UAV paths, so the idea of problem decomposition is effective to deal with multi-UAV cooperative path planning. With this analysis, a multi-UAV cooperative path planning algorithm based on co-evolution optimization was proposed in this paper. Firstly, by analyzing the meaning of multi-UAV cooperative flight, the optimization model of multi-UAV cooperative path planning was given. Secondly, we designed the cost function of multiple UAVs with the penalty function method to deal with multiple constraints and designed two information-sharing strategies to deal with the combination path search between multiple UAVs. The two information-sharing strategies were called the optimal individual selection strategy and the mixed selection strategy. The new cooperative path planning algorithm was presented by combining the above designation and co-evolution algorithm. Finally, the proposed algorithm is applied to a rendezvous task in complex environments and compared with two evolutionary algorithms. The experimental results show that the proposed algorithm can effectively cope with the multi-UAV cooperative path planning problem in complex environments.","PeriodicalId":36448,"journal":{"name":"Drones","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135719174","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 advantages of long-range flight and high payload capacity, large fixed-wing UAVs are often used in anti-terrorism missions, disaster surveillance, and emergency supply delivery. In the existing research, there is little research on the 3D model design of the V-tail fixed-wing UAV and 3D flight environment modeling. The study focuses on designing a comprehensive simulation environment using Gazebo and ROS, referencing existing large fixed-wing UAVs, to design a V-tail aircraft, incorporating realistic aircraft dynamics, aerodynamics, and flight controls. Additionally, we present a simulation environment modeling approach tailored for obstacle avoidance in no-fly zones, and have created a 3D flight environment in Gazebo, generating a large-scale terrain map based on the original grayscale heightmap. This terrain map is used to simulate potential mountainous terrain threats that a fixed-wing UAV might encounter during mission execution. We have also introduced wind disturbances and other specific no-fly zones. We integrated the V-tail fixed-wing aircraft model into the 3D flight environment in Gazebo and designed PID controllers to stabilize the aircraft’s flight attitude.
{"title":"Research on Scenario Modeling for V-Tail Fixed-Wing UAV Dynamic Obstacle Avoidance","authors":"Peihao Huang, Yong Tang, Bingsan Yang, Tao Wang","doi":"10.3390/drones7100601","DOIUrl":"https://doi.org/10.3390/drones7100601","url":null,"abstract":"With the advantages of long-range flight and high payload capacity, large fixed-wing UAVs are often used in anti-terrorism missions, disaster surveillance, and emergency supply delivery. In the existing research, there is little research on the 3D model design of the V-tail fixed-wing UAV and 3D flight environment modeling. The study focuses on designing a comprehensive simulation environment using Gazebo and ROS, referencing existing large fixed-wing UAVs, to design a V-tail aircraft, incorporating realistic aircraft dynamics, aerodynamics, and flight controls. Additionally, we present a simulation environment modeling approach tailored for obstacle avoidance in no-fly zones, and have created a 3D flight environment in Gazebo, generating a large-scale terrain map based on the original grayscale heightmap. This terrain map is used to simulate potential mountainous terrain threats that a fixed-wing UAV might encounter during mission execution. We have also introduced wind disturbances and other specific no-fly zones. We integrated the V-tail fixed-wing aircraft model into the 3D flight environment in Gazebo and designed PID controllers to stabilize the aircraft’s flight attitude.","PeriodicalId":36448,"journal":{"name":"Drones","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135863891","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}
Brendan P. Kelaher, Tommaso Pappagallo, Sebastian Litchfield, Thomas E. Fellowes
Beach nourishment is a soft engineering technique that is used to combat coastal erosion. To assess the efficacy of a beach nourishment program on the northwest coast of Lord Howe Island, remotely coordinated drone-based monitoring was undertaken at Lagoon Beach. Specifically, hypotheses were tested that beach nourishment could increase the dune height and the width of the beach where the sand was translocated but would not have any long-term impacts on other parts of the beach. During the beach nourishment program, sand was translocated from the north end to the south end of Lagoon Beach, where it was deposited over 2800 m2. Lagoon Beach was monitored using a time series of 3D orthomosaics (2019–2021) based on orthorectified drone imagery. The data were then analysed using a robust before-after-control-impact (BACI) experimental design. Initially, a fully automated drone mapping program and permanent ground control points were set up. After this, a local drone pilot facilitated automated drone mapping for the subsequent times of sampling and transferred data to mainland researchers. As well as being more cost-effective, this approach allowed data collection to continue during Island closures due to the COVID-19 pandemic. After sand translocation, the south end of Lagoon Beach had a lower dune with more vegetation and a more expansive beach with a gentler slope than the prior arrangement. Overall, drone monitoring demonstrated the efficacy of the beach nourishment program on Lord Howe Island and highlighted the capacity for drones to deliver cost-effective data in locations that were difficult for researchers to access.
{"title":"Drone-Based Monitoring to Remotely Assess a Beach Nourishment Program on Lord Howe Island","authors":"Brendan P. Kelaher, Tommaso Pappagallo, Sebastian Litchfield, Thomas E. Fellowes","doi":"10.3390/drones7100600","DOIUrl":"https://doi.org/10.3390/drones7100600","url":null,"abstract":"Beach nourishment is a soft engineering technique that is used to combat coastal erosion. To assess the efficacy of a beach nourishment program on the northwest coast of Lord Howe Island, remotely coordinated drone-based monitoring was undertaken at Lagoon Beach. Specifically, hypotheses were tested that beach nourishment could increase the dune height and the width of the beach where the sand was translocated but would not have any long-term impacts on other parts of the beach. During the beach nourishment program, sand was translocated from the north end to the south end of Lagoon Beach, where it was deposited over 2800 m2. Lagoon Beach was monitored using a time series of 3D orthomosaics (2019–2021) based on orthorectified drone imagery. The data were then analysed using a robust before-after-control-impact (BACI) experimental design. Initially, a fully automated drone mapping program and permanent ground control points were set up. After this, a local drone pilot facilitated automated drone mapping for the subsequent times of sampling and transferred data to mainland researchers. As well as being more cost-effective, this approach allowed data collection to continue during Island closures due to the COVID-19 pandemic. After sand translocation, the south end of Lagoon Beach had a lower dune with more vegetation and a more expansive beach with a gentler slope than the prior arrangement. Overall, drone monitoring demonstrated the efficacy of the beach nourishment program on Lord Howe Island and highlighted the capacity for drones to deliver cost-effective data in locations that were difficult for researchers to access.","PeriodicalId":36448,"journal":{"name":"Drones","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135817829","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}
Sebastian-Marian Zaharia, Ionut Stelian Pascariu, Lucia-Antoneta Chicos, George Razvan Buican, Mihai Alin Pop, Camil Lancea, Valentin Marian Stamate
The additive processes used in the manufacture of components for unmanned aerial vehicles (UAVs), from composite filaments, have an important advantage compared to classical technologies. This study focused on three-dimensional design, preliminary aerodynamic analysis, fabrication and assembly of thermoplastic extruded composite components, flight testing and search-rescue performance of an UAV. The UAV model was designed to have the highest possible structural strength (the fuselage has a structure with stiffening frames and the wing is a tri-spar), but also taking into account the limitations of the thermoplastic extrusion process. From the preliminary aerodynamic analysis of the UAV model, it was found that the maximum lift coefficient of 1.2 and the maximum drag coefficient of 0.06 were obtained at the angle of attack of 12°. After conducting flight tests, it can be stated that the UAV model, with components manufactured by the thermoplastic extrusion process, presented high stability and maneuverability, a wide range of speeds and good aerodynamic characteristics. The lack of this type of aircraft, equipped with electric motors, a traffic management system, and a thermal module designed for search-and-rescue missions, within the additive manufacturing UAV market, validates the uniqueness of the innovation of the UAV model presented in the current paper.
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