This study explored the innovative use of multiple remote sensing satellites and unmanned aerial vehicles to calculate soil losses in the Loess Plateau of Iran. This finding emphasized the importance of using advanced technologies to develop accurate and efficient soil erosion assessment techniques. Accordingly, this study developed an approach to compare sinkholes and gully heads in hilly regions on the Loess Plateau of northeast Iran using convolutional neural network (CNN or ConvNet). This method involved coupling data from UAV, Sentinel-2, and SPOT-6 satellite data. The soil erosion computed using UAV data showed AUC values of 0.9247 and 0.9189 for the gully head and the sinkhole, respectively. The use of SPOT-6 data in gully head and sinkhole computations showed AUC values of 0.9105 and 0.9123, respectively. The AUC values were 0.8978 and 0.9001 for the gully head and the sinkhole using Sentinel-2, respectively. Comparison of the results from the calculated UAV, SPOT-6, and Sentinel-2 data showed that the UAV had the highest accuracy for calculating sinkhole and gully head soil features, although Sentinel-2 and SPOT-6 showed good results. Overall, the combination of multiple remote sensing satellites and UAVs offers improved accuracy, timeliness, cost effectiveness, accessibility, and long-term monitoring capabilities, making it a powerful approach for calculating soil loss in the Loess Plateau of Iran.
{"title":"Harnessing the Power of Remote Sensing and Unmanned Aerial Vehicles: A Comparative Analysis for Soil Loss Estimation on the Loess Plateau","authors":"Narges Kariminejad, Mohammad Kazemi Kazemi Garajeh, Mohsen Hosseinalizadeh, Foroogh Golkar, Hamid Reza Pourghasemi","doi":"10.3390/drones7110659","DOIUrl":"https://doi.org/10.3390/drones7110659","url":null,"abstract":"This study explored the innovative use of multiple remote sensing satellites and unmanned aerial vehicles to calculate soil losses in the Loess Plateau of Iran. This finding emphasized the importance of using advanced technologies to develop accurate and efficient soil erosion assessment techniques. Accordingly, this study developed an approach to compare sinkholes and gully heads in hilly regions on the Loess Plateau of northeast Iran using convolutional neural network (CNN or ConvNet). This method involved coupling data from UAV, Sentinel-2, and SPOT-6 satellite data. The soil erosion computed using UAV data showed AUC values of 0.9247 and 0.9189 for the gully head and the sinkhole, respectively. The use of SPOT-6 data in gully head and sinkhole computations showed AUC values of 0.9105 and 0.9123, respectively. The AUC values were 0.8978 and 0.9001 for the gully head and the sinkhole using Sentinel-2, respectively. Comparison of the results from the calculated UAV, SPOT-6, and Sentinel-2 data showed that the UAV had the highest accuracy for calculating sinkhole and gully head soil features, although Sentinel-2 and SPOT-6 showed good results. Overall, the combination of multiple remote sensing satellites and UAVs offers improved accuracy, timeliness, cost effectiveness, accessibility, and long-term monitoring capabilities, making it a powerful approach for calculating soil loss in the Loess Plateau of Iran.","PeriodicalId":36448,"journal":{"name":"Drones","volume":"39 28","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135774010","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}
Katherine Theobald, Wanqing Zhu, Timothy Waters, Thomas Cherrett, Andy Oakey, Paul G. Royall
The timely distribution of medicines to patients is an essential part of the patient care plan, and maximising efficiency in the logistics systems behind these movements is vital to minimise cost. Before drones can be used for moving medical cargo, medical regulatory authorities require assurance that the transported products will not be adversely affected by in-flight conditions unique to each drone. This study set out to (i) quantify the vibration profile by phases of flight, (ii) determine to what extent there were significant differences in the observed vibration between the phases, and (iii) assess the quality of flown monoclonal antibody (mAb) infusions used in the treatment of cancer. Vibrations emanating from the drone and transmitted through standard medical packaging were monitored with the storage specifications for mean kinematic temperature (2–8 °C) being met. Vibration levels were recorded between 1.5 and 3 g, with the dominant octave band being 250 Hz. After 60 flights, the quality attributes of flown infusions regarding size integrity were found to be no different from those of the control infusions. For example, the particle size had a variation of less than 1 nm; one peak for Trastuzumab was 14.6 ± 0.07 nm, and Rituximab was 13.3 ± 0.90 nm. The aggregation (%) and fragmentation (%) remained at 0.18 ± 0.01% and 0.11 ± 0.02% for Trastuzumab, 0.11 ± 0.01% and 2.82 ± 0.15% for Rituximab. The results indicated that in the case of mAbs, the quality assurance specifications were met and that drone vibration did not adversely affect the quality of drone-flown medicines.
{"title":"Stability of Medicines Transported by Cargo Drones: Investigating the Effects of Vibration from Multi-Stage Flight","authors":"Katherine Theobald, Wanqing Zhu, Timothy Waters, Thomas Cherrett, Andy Oakey, Paul G. Royall","doi":"10.3390/drones7110658","DOIUrl":"https://doi.org/10.3390/drones7110658","url":null,"abstract":"The timely distribution of medicines to patients is an essential part of the patient care plan, and maximising efficiency in the logistics systems behind these movements is vital to minimise cost. Before drones can be used for moving medical cargo, medical regulatory authorities require assurance that the transported products will not be adversely affected by in-flight conditions unique to each drone. This study set out to (i) quantify the vibration profile by phases of flight, (ii) determine to what extent there were significant differences in the observed vibration between the phases, and (iii) assess the quality of flown monoclonal antibody (mAb) infusions used in the treatment of cancer. Vibrations emanating from the drone and transmitted through standard medical packaging were monitored with the storage specifications for mean kinematic temperature (2–8 °C) being met. Vibration levels were recorded between 1.5 and 3 g, with the dominant octave band being 250 Hz. After 60 flights, the quality attributes of flown infusions regarding size integrity were found to be no different from those of the control infusions. For example, the particle size had a variation of less than 1 nm; one peak for Trastuzumab was 14.6 ± 0.07 nm, and Rituximab was 13.3 ± 0.90 nm. The aggregation (%) and fragmentation (%) remained at 0.18 ± 0.01% and 0.11 ± 0.02% for Trastuzumab, 0.11 ± 0.01% and 2.82 ± 0.15% for Rituximab. The results indicated that in the case of mAbs, the quality assurance specifications were met and that drone vibration did not adversely affect the quality of drone-flown medicines.","PeriodicalId":36448,"journal":{"name":"Drones","volume":"14 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135821650","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}
Jin-Hwan Lee, Gi-Hun Gwon, In-Ho Kim, Hyung-Jo Jung
Unmanned aerial vehicles (UAVs) have been increasingly utilized for facility safety inspections due to their superior safety, cost effectiveness, and inspection accuracy compared to traditional manpower-based methods. High-resolution images captured by UAVs directly contribute to identifying and quantifying structural defects on facility exteriors, making image quality a critical factor in achieving accurate results. However, motion blur induced by external factors such as vibration, low light conditions, and wind during UAV operation significantly degrades image quality, leading to inaccurate defect detection and quantification. To address this issue, this research proposes a deblurring network using a Generative Adversarial Network (GAN) to eliminate the motion blur effect in UAV images. The GAN-based motion deblur network represents an image inpainting method that leverages generative models to correct blurry artifacts, thereby generating clear images. Unlike previous studies, this proposed approach incorporates deblur and blur learning modules to realistically generate blur images required for training the generative models. The UAV images processed using the motion deblur network are evaluated using a quality assessment method based on local blur map and other well-known image quality assessment (IQA) metrics. Moreover, in the experiment of crack detection utilizing the object detection system, improved detection results are observed when using enhanced images. Overall, this research contributes to improving the quality and accuracy of facility safety inspections conducted with UAV-based inspections by effectively addressing the challenges associated with motion blur effects in UAV-captured images.
{"title":"A Motion Deblurring Network for Enhancing UAV Image Quality in Bridge Inspection","authors":"Jin-Hwan Lee, Gi-Hun Gwon, In-Ho Kim, Hyung-Jo Jung","doi":"10.3390/drones7110657","DOIUrl":"https://doi.org/10.3390/drones7110657","url":null,"abstract":"Unmanned aerial vehicles (UAVs) have been increasingly utilized for facility safety inspections due to their superior safety, cost effectiveness, and inspection accuracy compared to traditional manpower-based methods. High-resolution images captured by UAVs directly contribute to identifying and quantifying structural defects on facility exteriors, making image quality a critical factor in achieving accurate results. However, motion blur induced by external factors such as vibration, low light conditions, and wind during UAV operation significantly degrades image quality, leading to inaccurate defect detection and quantification. To address this issue, this research proposes a deblurring network using a Generative Adversarial Network (GAN) to eliminate the motion blur effect in UAV images. The GAN-based motion deblur network represents an image inpainting method that leverages generative models to correct blurry artifacts, thereby generating clear images. Unlike previous studies, this proposed approach incorporates deblur and blur learning modules to realistically generate blur images required for training the generative models. The UAV images processed using the motion deblur network are evaluated using a quality assessment method based on local blur map and other well-known image quality assessment (IQA) metrics. Moreover, in the experiment of crack detection utilizing the object detection system, improved detection results are observed when using enhanced images. Overall, this research contributes to improving the quality and accuracy of facility safety inspections conducted with UAV-based inspections by effectively addressing the challenges associated with motion blur effects in UAV-captured images.","PeriodicalId":36448,"journal":{"name":"Drones","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135933018","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 multi-sensors embedded, flexible unmanned aerial vehicles (UAVs) can collect sensory data and provide various services for all walks of life. However, limited computing capability and battery energy put a great burden on UAVs to handle emerging compute-intensive applications, necessitating them to resort to innovative computation offloading technique to guarantee quality of service. Existing research mainly focuses on solving the offloading problem under known global information, or applying centralized offloading frameworks when facing dynamic environments. Yet, the maneuverability of today’s UAVs, their large-scale clustering, and their increasing operation in the environment with unrevealed information pose huge challenges to previous work. In this paper, in order to enhance the long-term offloading performance and scalability for multi-UAVs, we develop a decentralized offloading scheme named DELOFF with the support of mobile edge computing (MEC). DELOFF considers the information uncertainty caused by the dynamic environment, uses UAV-to-everything (U2X)-assisted heterogeneous networks to extend network resources and offloading flexibility, and tackles the joint strategy making related to computation mode, network selection, and offloading allocation for multi-UAVs. Specifically, the optimization problem of multi-UAVs is addressed by the proposed offloading algorithm based on a multi-arm bandit learning model, where each UAV itself can adaptively assess the offloading link quality through the fuzzy logic-based pre-screening mechanism designed. The convergence and effectiveness of the DELOFF proposed are also demonstrated in simulations. And, the results confirm that DELOFF is superior to the four benchmarks in many respects, such as reduced consumed energy and delay in the task completion of UAVs.
{"title":"DELOFF: Decentralized Learning-Based Task Offloading for Multi-UAVs in U2X-Assisted Heterogeneous Networks","authors":"Anqi Zhu, Huimin Lu, Mingfang Ma, Zongtan Zhou, Zhiwen Zeng","doi":"10.3390/drones7110656","DOIUrl":"https://doi.org/10.3390/drones7110656","url":null,"abstract":"With multi-sensors embedded, flexible unmanned aerial vehicles (UAVs) can collect sensory data and provide various services for all walks of life. However, limited computing capability and battery energy put a great burden on UAVs to handle emerging compute-intensive applications, necessitating them to resort to innovative computation offloading technique to guarantee quality of service. Existing research mainly focuses on solving the offloading problem under known global information, or applying centralized offloading frameworks when facing dynamic environments. Yet, the maneuverability of today’s UAVs, their large-scale clustering, and their increasing operation in the environment with unrevealed information pose huge challenges to previous work. In this paper, in order to enhance the long-term offloading performance and scalability for multi-UAVs, we develop a decentralized offloading scheme named DELOFF with the support of mobile edge computing (MEC). DELOFF considers the information uncertainty caused by the dynamic environment, uses UAV-to-everything (U2X)-assisted heterogeneous networks to extend network resources and offloading flexibility, and tackles the joint strategy making related to computation mode, network selection, and offloading allocation for multi-UAVs. Specifically, the optimization problem of multi-UAVs is addressed by the proposed offloading algorithm based on a multi-arm bandit learning model, where each UAV itself can adaptively assess the offloading link quality through the fuzzy logic-based pre-screening mechanism designed. The convergence and effectiveness of the DELOFF proposed are also demonstrated in simulations. And, the results confirm that DELOFF is superior to the four benchmarks in many respects, such as reduced consumed energy and delay in the task completion of UAVs.","PeriodicalId":36448,"journal":{"name":"Drones","volume":"9 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135166160","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}
To realize unmanned aerial vehicle (UAV) situation assessment, a Bayesian network (BN) for situation assessment is established. Aimed at the problem that the parameters of the BN are difficult to obtain, an improved whale optimization algorithm based on prior parameter intervals (IWOA-PPI) for parameter learning is proposed. Firstly, according to the dependencies between the situation and its related factors, the structure of the BN is established. Secondly, in order to fully mine the prior knowledge of parameters, the parameter constraints are transformed into parameter prior intervals using Monte Carlo sampling and interval transformation formulas. Thirdly, a variable encircling factor and a nonlinear convergence factor are proposed. The former and the latter enhance the local and global search capabilities of the whale optimization algorithm (WOA), respectively. Finally, a simulated annealing strategy incorporating Levy flight is introduced to enable the WOA to jump out of the local optimum. In the experiment for the standard BNs, five parameter-learning algorithms are applied, and the results prove that the IWOA-PPI is not only effective but also the most accurate. In the experiment for the situation BN, the situations of the assumed mission scenario are evaluated, and the results show that the situation assessment method proposed in this article is correct and feasible.
{"title":"The Situation Assessment of UAVs Based on an Improved Whale Optimization Bayesian Network Parameter-Learning Algorithm","authors":"Weinan Li, Weiguo Zhang, Baoning Liu, Yicong Guo","doi":"10.3390/drones7110655","DOIUrl":"https://doi.org/10.3390/drones7110655","url":null,"abstract":"To realize unmanned aerial vehicle (UAV) situation assessment, a Bayesian network (BN) for situation assessment is established. Aimed at the problem that the parameters of the BN are difficult to obtain, an improved whale optimization algorithm based on prior parameter intervals (IWOA-PPI) for parameter learning is proposed. Firstly, according to the dependencies between the situation and its related factors, the structure of the BN is established. Secondly, in order to fully mine the prior knowledge of parameters, the parameter constraints are transformed into parameter prior intervals using Monte Carlo sampling and interval transformation formulas. Thirdly, a variable encircling factor and a nonlinear convergence factor are proposed. The former and the latter enhance the local and global search capabilities of the whale optimization algorithm (WOA), respectively. Finally, a simulated annealing strategy incorporating Levy flight is introduced to enable the WOA to jump out of the local optimum. In the experiment for the standard BNs, five parameter-learning algorithms are applied, and the results prove that the IWOA-PPI is not only effective but also the most accurate. In the experiment for the situation BN, the situations of the assumed mission scenario are evaluated, and the results show that the situation assessment method proposed in this article is correct and feasible.","PeriodicalId":36448,"journal":{"name":"Drones","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135166026","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}
This work examines the competition and allocation of multiple solar-powered unmanned aerial vehicles (SUAVs) to a single thermal since multiple SUAVs often demonstrate superior mission performance compared to a single SUAV. Additionally, they can harvest extra energy from thermal updrafts. This work considers two conditions, a non-cooperative competition and a cooperative allocation of thermal. In each case, corresponding objective functions and constraints are established, and assignment schemes are derived by solving these objective functions. The allocation results are simulated and integrated with the dynamics and solar energy model. The numerical results show that, in the non-cooperative mode, the first vehicle to reach the thermal can occupy it for soaring, while the remaining SUAVs will fly towards the destination directly. But in the cooperative mode, the multiple SUAVs will allocate the thermal to the SUAV with the highest energy gain through soaring, to maximize the overall electric energy storage of the SUAV group.
{"title":"Competition and Cooperation for Multiple Solar Powered Unmanned Aerial Vehicles under Static Soaring","authors":"Yansen Wu, Ke Li, Anmin Zhao, Shaofan Wang, Yuangan Li, Xiaodan Chen","doi":"10.3390/drones7110653","DOIUrl":"https://doi.org/10.3390/drones7110653","url":null,"abstract":"This work examines the competition and allocation of multiple solar-powered unmanned aerial vehicles (SUAVs) to a single thermal since multiple SUAVs often demonstrate superior mission performance compared to a single SUAV. Additionally, they can harvest extra energy from thermal updrafts. This work considers two conditions, a non-cooperative competition and a cooperative allocation of thermal. In each case, corresponding objective functions and constraints are established, and assignment schemes are derived by solving these objective functions. The allocation results are simulated and integrated with the dynamics and solar energy model. The numerical results show that, in the non-cooperative mode, the first vehicle to reach the thermal can occupy it for soaring, while the remaining SUAVs will fly towards the destination directly. But in the cooperative mode, the multiple SUAVs will allocate the thermal to the SUAV with the highest energy gain through soaring, to maximize the overall electric energy storage of the SUAV group.","PeriodicalId":36448,"journal":{"name":"Drones","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135872489","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 method is proposed to estimate the swing state of a suspended payload in multirotor drone delivery scenarios. Starting from the equations of motion of the coupled slung load system, defined by two point masses interconnected by a rigid link, a recursive algorithm is developed to estimate cable swing angle and rate from acceleration measurements available from an onboard Inertial Measurement Unit, without the need for extra sensors. The estimation problem is addressed according to the Extended Kalman Filter structure. With respect to the classical linear formulation, the proposed approach allows for improved estimation accuracy in both stationary and maneuvering flight. As an additional contribution, filter performance is enhanced by accounting for aerodynamic disturbance force, which largely affects the estimation accuracy in windy flight conditions. The validity of the proposed methodology is demonstrated as follows. First, it is applied to an octarotor platform where propellers are modeled according to blade element theory and the load is suspended by an elastic cable. Numerical simulations show that estimated swing angle and rate represent suitable feedback variables for payload stabilization, with benefits on flying qualities and energy demand. The algorithm is finally implemented on a small-scale quadrotor and is investigated through an outdoor experimental campaign, thus proving the effectiveness of the approach in a real application scenario.
{"title":"An Improved Method for Swing State Estimation in Multirotor Slung Load Applications","authors":"Emanuele Luigi de de Angelis, Fabrizio Giulietti","doi":"10.3390/drones7110654","DOIUrl":"https://doi.org/10.3390/drones7110654","url":null,"abstract":"A method is proposed to estimate the swing state of a suspended payload in multirotor drone delivery scenarios. Starting from the equations of motion of the coupled slung load system, defined by two point masses interconnected by a rigid link, a recursive algorithm is developed to estimate cable swing angle and rate from acceleration measurements available from an onboard Inertial Measurement Unit, without the need for extra sensors. The estimation problem is addressed according to the Extended Kalman Filter structure. With respect to the classical linear formulation, the proposed approach allows for improved estimation accuracy in both stationary and maneuvering flight. As an additional contribution, filter performance is enhanced by accounting for aerodynamic disturbance force, which largely affects the estimation accuracy in windy flight conditions. The validity of the proposed methodology is demonstrated as follows. First, it is applied to an octarotor platform where propellers are modeled according to blade element theory and the load is suspended by an elastic cable. Numerical simulations show that estimated swing angle and rate represent suitable feedback variables for payload stabilization, with benefits on flying qualities and energy demand. The algorithm is finally implemented on a small-scale quadrotor and is investigated through an outdoor experimental campaign, thus proving the effectiveness of the approach in a real application scenario.","PeriodicalId":36448,"journal":{"name":"Drones","volume":"56 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135869448","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}
Juan Pablo Arroyo-Mora, Margaret Kalacska, Oliver Lucanus, René Laliberté, Yong Chen, Janine Gorman, Alexandra Marion, Landen Coulas, Hali Barber, Iryna Borshchova, Raymond J. Soffer, George Leblanc, Daniel Lavigne, Ludovic Girard, Martin Bérubé
A main aspect limiting the operation of low-altitude remotely piloted aircraft systems (RPAS) over 25 kg, integrating pushbroom hyperspectral sensors, comes from the challenges related to aircraft performance (e.g., flight time) and regulatory aspects deterring the users from pushing beyond this weight limit. In this study, we showcase a novel implementation using the DJI Agras T30 as an aerial system for integrating an advanced hyperspectral imager (HSI, Hyspex VS-620). We present the design and fabrication approach applied to integrate the HSI payload, the key considerations for powering the HSI and its gimbal, and the results from vibration and wind tunnel tests. We also evaluate the system’s flight capacity and the HSI’s geometric and radiometric data qualities. The final weight of the T30 after the integration of the HSI payload and ancillary hardware was 43 kg. Our vibration test showed that the vibration isolator and the gimbal reduced the vibration transmission to above 15 Hz but also introduced a resonant peak at 9.6 Hz that led to vibration amplification in the low-frequency range near 9.6 Hz (on the order of an RMS of ~0.08 g). The wind tunnel test revealed that the system is stable up to nearly twice the wind speed rating of the manufacturer’s specifications (i.e., 8 m/s). Based on the requirements of the Canadian Special Flight Operations Certificate (RPAS > 25 kg) to land at a minimal battery level of ≥30%, the system was able to cover an area of ~2.25 ha at a speed of 3.7 m/s and an altitude of 100 m above ground level (AGL) in 7 min. The results with the HSI payload at different speeds and altitudes from 50 m to 100 m AGL show hyperspectral imagery with minimal roll–pitch–yaw artefacts prior to geocorrection and consistent spectra when compared to nominal reflectance targets. Finally, we discuss the steps followed to deal with the continuously evolving regulatory framework developed by Transport Canada for systems > 25 kg. Our work advances low-altitude HSI applications and encourages remote sensing scientists to take advantage of national regulatory frameworks, which ultimately improve the overall quality of HSI data and safety of operations with RPAS > 25 kg.
{"title":"Development of a Novel Implementation of a Remotely Piloted Aircraft System over 25 kg for Hyperspectral Payloads","authors":"Juan Pablo Arroyo-Mora, Margaret Kalacska, Oliver Lucanus, René Laliberté, Yong Chen, Janine Gorman, Alexandra Marion, Landen Coulas, Hali Barber, Iryna Borshchova, Raymond J. Soffer, George Leblanc, Daniel Lavigne, Ludovic Girard, Martin Bérubé","doi":"10.3390/drones7110652","DOIUrl":"https://doi.org/10.3390/drones7110652","url":null,"abstract":"A main aspect limiting the operation of low-altitude remotely piloted aircraft systems (RPAS) over 25 kg, integrating pushbroom hyperspectral sensors, comes from the challenges related to aircraft performance (e.g., flight time) and regulatory aspects deterring the users from pushing beyond this weight limit. In this study, we showcase a novel implementation using the DJI Agras T30 as an aerial system for integrating an advanced hyperspectral imager (HSI, Hyspex VS-620). We present the design and fabrication approach applied to integrate the HSI payload, the key considerations for powering the HSI and its gimbal, and the results from vibration and wind tunnel tests. We also evaluate the system’s flight capacity and the HSI’s geometric and radiometric data qualities. The final weight of the T30 after the integration of the HSI payload and ancillary hardware was 43 kg. Our vibration test showed that the vibration isolator and the gimbal reduced the vibration transmission to above 15 Hz but also introduced a resonant peak at 9.6 Hz that led to vibration amplification in the low-frequency range near 9.6 Hz (on the order of an RMS of ~0.08 g). The wind tunnel test revealed that the system is stable up to nearly twice the wind speed rating of the manufacturer’s specifications (i.e., 8 m/s). Based on the requirements of the Canadian Special Flight Operations Certificate (RPAS > 25 kg) to land at a minimal battery level of ≥30%, the system was able to cover an area of ~2.25 ha at a speed of 3.7 m/s and an altitude of 100 m above ground level (AGL) in 7 min. The results with the HSI payload at different speeds and altitudes from 50 m to 100 m AGL show hyperspectral imagery with minimal roll–pitch–yaw artefacts prior to geocorrection and consistent spectra when compared to nominal reflectance targets. Finally, we discuss the steps followed to deal with the continuously evolving regulatory framework developed by Transport Canada for systems > 25 kg. Our work advances low-altitude HSI applications and encourages remote sensing scientists to take advantage of national regulatory frameworks, which ultimately improve the overall quality of HSI data and safety of operations with RPAS > 25 kg.","PeriodicalId":36448,"journal":{"name":"Drones","volume":"BC-27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136235036","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}
Jun Lai, Suyang Liu, Xiaojia Xiang, Chaoran Li, Dengqing Tang, Han Zhou
The swarm of small UAVs is an emerging technology that will enable abundant cooperative tasks. To tackle the positioning problem for the UAV swarm, cooperative localization (CL) has been intensively studied since it uses relative measurement to improve the positioning availability and accuracy for the swarm in GPS-denied environments. Besides relying on inter-UAV range measurement, traditional CL algorithms need to place anchors as location references, which limits their applicability. To implement an infrastructure-less swarm navigation system, a consumer-grade camera together with an inertial device can provide rich environment information, which can be recognized as a kind of local location reference. This paper aims to analyze the fundamental performance of visual–inertial–range CL, which is also a popular metric for UAV planning and sensing optimizing, especially for resource-limited environments. Specifically, a closed-form Fisher information matrix (FIM) of visual–inertial–range CL is constructed in Rn×SO(n) manifold. By introducing an equivalent FIM and utilizing of the sparsity of the FIM, the performance of pose estimation can be efficiently calculated. A series of numerical simulations validate its effectiveness for analyzing the CL performance.
{"title":"Performance Analysis of Visual–Inertial–Range Cooperative Localization for Unmanned Autonomous Vehicle Swarm","authors":"Jun Lai, Suyang Liu, Xiaojia Xiang, Chaoran Li, Dengqing Tang, Han Zhou","doi":"10.3390/drones7110651","DOIUrl":"https://doi.org/10.3390/drones7110651","url":null,"abstract":"The swarm of small UAVs is an emerging technology that will enable abundant cooperative tasks. To tackle the positioning problem for the UAV swarm, cooperative localization (CL) has been intensively studied since it uses relative measurement to improve the positioning availability and accuracy for the swarm in GPS-denied environments. Besides relying on inter-UAV range measurement, traditional CL algorithms need to place anchors as location references, which limits their applicability. To implement an infrastructure-less swarm navigation system, a consumer-grade camera together with an inertial device can provide rich environment information, which can be recognized as a kind of local location reference. This paper aims to analyze the fundamental performance of visual–inertial–range CL, which is also a popular metric for UAV planning and sensing optimizing, especially for resource-limited environments. Specifically, a closed-form Fisher information matrix (FIM) of visual–inertial–range CL is constructed in Rn×SO(n) manifold. By introducing an equivalent FIM and utilizing of the sparsity of the FIM, the performance of pose estimation can be efficiently calculated. A series of numerical simulations validate its effectiveness for analyzing the CL performance.","PeriodicalId":36448,"journal":{"name":"Drones","volume":"1 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":"134906966","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}
Xiao Jia, Dameng Yin, Yali Bai, Xun Yu, Yang Song, Minghan Cheng, Shuaibing Liu, Yi Bai, Lin Meng, Yadong Liu, Qian Liu, Fei Nan, Chenwei Nie, Lei Shi, Ping Dong, Wei Guo, Xiuliang Jin
Maize leaf spot is a common disease that hampers the photosynthesis of maize by destroying the pigment structure of maize leaves, thus reducing the yield. Traditional disease monitoring is time-consuming and laborious. Therefore, a fast and effective method for maize leaf spot disease monitoring is needed to facilitate the efficient management of maize yield and safety. In this study, we adopted UAV multispectral and thermal remote sensing techniques to monitor two types of maize leaf spot diseases, i.e., southern leaf blight caused by Bipolaris maydis and Curvularia leaf spot caused by Curvularia lutana. Four state-of-the-art classifiers (back propagation neural network, random forest (RF), support vector machine, and extreme gradient boosting) were compared to establish an optimal classification model to monitor the incidence of these diseases. Recursive feature elimination (RFE) was employed to select features that are most effective in maize leaf spot disease identification in four stages (4, 12, 19, and 30 days after inoculation). The results showed that multispectral indices involving the red, red edge, and near-infrared bands were the most sensitive to maize leaf spot incidence. In addition, the two thermal features tested (i.e., canopy temperature and normalized canopy temperature) were both found to be important to identify maize leaf spot. Using features filtered with the RFE algorithm and the RF classifier, maize infected with leaf spot diseases were successfully distinguished from healthy maize after 19 days of inoculation, with precision >0.9 and recall >0.95. Nevertheless, the accuracy was much lower (precision = 0.4, recall = 0.53) when disease development was in the early stages. We anticipate that the monitoring of maize leaf spot disease at the early stages might benefit from using hyperspectral and oblique observations.
玉米叶斑病是一种常见的病害，它通过破坏玉米叶片的色素结构来阻碍玉米的光合作用，从而降低产量。传统的疾病监测既费时又费力。因此，需要一种快速有效的玉米叶斑病监测方法，以便于玉米产量和安全的高效管理。本研究采用无人机多光谱和热遥感技术，对两种玉米叶斑病进行了监测，即双极星(Bipolaris maydis)引起的南方叶枯病和曲霉(Curvularia lutana)引起的曲霉(Curvularia lutana)叶斑病。通过比较四种最先进的分类器(反向传播神经网络、随机森林(RF)、支持向量机和极端梯度增强)，建立了一个最优的分类模型来监测这些疾病的发病率。采用递归特征消去法(RFE)筛选接种后4、12、19、30 d 4个阶段玉米叶斑病鉴定最有效的特征。结果表明，红色、红边和近红外波段的多光谱指标对玉米叶斑病的发生最为敏感。此外，还发现冠层温度和归一化冠层温度这两种热特征对鉴定玉米叶斑病具有重要意义。利用RFE算法和RF分类器过滤的特征，接种19 d后，成功地将感染叶斑病的玉米与健康玉米区分开来，准确率>0.9，召回率>0.95。然而，当疾病发展处于早期阶段时，准确率要低得多(准确率= 0.4，召回率= 0.53)。我们预计，在玉米叶斑病的早期监测可能受益于使用高光谱和斜向观测。
{"title":"Monitoring Maize Leaf Spot Disease Using Multi-Source UAV Imagery","authors":"Xiao Jia, Dameng Yin, Yali Bai, Xun Yu, Yang Song, Minghan Cheng, Shuaibing Liu, Yi Bai, Lin Meng, Yadong Liu, Qian Liu, Fei Nan, Chenwei Nie, Lei Shi, Ping Dong, Wei Guo, Xiuliang Jin","doi":"10.3390/drones7110650","DOIUrl":"https://doi.org/10.3390/drones7110650","url":null,"abstract":"Maize leaf spot is a common disease that hampers the photosynthesis of maize by destroying the pigment structure of maize leaves, thus reducing the yield. Traditional disease monitoring is time-consuming and laborious. Therefore, a fast and effective method for maize leaf spot disease monitoring is needed to facilitate the efficient management of maize yield and safety. In this study, we adopted UAV multispectral and thermal remote sensing techniques to monitor two types of maize leaf spot diseases, i.e., southern leaf blight caused by Bipolaris maydis and Curvularia leaf spot caused by Curvularia lutana. Four state-of-the-art classifiers (back propagation neural network, random forest (RF), support vector machine, and extreme gradient boosting) were compared to establish an optimal classification model to monitor the incidence of these diseases. Recursive feature elimination (RFE) was employed to select features that are most effective in maize leaf spot disease identification in four stages (4, 12, 19, and 30 days after inoculation). The results showed that multispectral indices involving the red, red edge, and near-infrared bands were the most sensitive to maize leaf spot incidence. In addition, the two thermal features tested (i.e., canopy temperature and normalized canopy temperature) were both found to be important to identify maize leaf spot. Using features filtered with the RFE algorithm and the RF classifier, maize infected with leaf spot diseases were successfully distinguished from healthy maize after 19 days of inoculation, with precision >0.9 and recall >0.95. Nevertheless, the accuracy was much lower (precision = 0.4, recall = 0.53) when disease development was in the early stages. We anticipate that the monitoring of maize leaf spot disease at the early stages might benefit from using hyperspectral and oblique observations.","PeriodicalId":36448,"journal":{"name":"Drones","volume":"31 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134909123","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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