Disasters, whether natural or man-made, demand rapid and comprehensive responses. Unmanned Aerial Vehicles (UAVs), or drones, have become essential in disaster scenarios, serving as crucial communication relays in areas with compromised infrastructure. They establish temporary networks, aiding coordination among emergency responders and facilitating timely assistance to survivors. Recent advancements in sensing technology have transformed emergency response by combining collaborative power of these networks with real-time data processing. However, challenges still to consider these networks for disaster monitoring applications, particularly in deployment strategies, data processing, routing, and security. Extensive research is crucial to refine ad-hoc networking solutions, enhancing the agility and effectiveness of these systems. This article explores various aspects, including network architecture, formation strategies, communication protocols, and security concerns in multi-UAV networks for disaster monitoring. It also examines the potential of enabling technologies like edge computing and artificial intelligence to bolster network performance and security. Further, the article provides a detailed overview of the key challenges and open issues, outlining various research prospects in the evolving field of multi-UAV networks for disaster response.
{"title":"Multi-UAV Networks for Disaster Monitoring: Challenges and Opportunities from a Network Perspective","authors":"Indu Chandran, Kizheppatt Vipin","doi":"10.1139/dsa-2023-0079","DOIUrl":"https://doi.org/10.1139/dsa-2023-0079","url":null,"abstract":"Disasters, whether natural or man-made, demand rapid and comprehensive responses. Unmanned Aerial Vehicles (UAVs), or drones, have become essential in disaster scenarios, serving as crucial communication relays in areas with compromised infrastructure. They establish temporary networks, aiding coordination among emergency responders and facilitating timely assistance to survivors. Recent advancements in sensing technology have transformed emergency response by combining collaborative power of these networks with real-time data processing. However, challenges still to consider these networks for disaster monitoring applications, particularly in deployment strategies, data processing, routing, and security. Extensive research is crucial to refine ad-hoc networking solutions, enhancing the agility and effectiveness of these systems. This article explores various aspects, including network architecture, formation strategies, communication protocols, and security concerns in multi-UAV networks for disaster monitoring. It also examines the potential of enabling technologies like edge computing and artificial intelligence to bolster network performance and security. Further, the article provides a detailed overview of the key challenges and open issues, outlining various research prospects in the evolving field of multi-UAV networks for disaster response.","PeriodicalId":202289,"journal":{"name":"Drone Systems and Applications","volume":"62 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140424018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Erin N Macke, Landon Richard Jones, Raymond Iglay, Jared A Elmore
Drones are becoming a common tool for animal monitoring; however, sound emitted from drones may disturb animals and bias survey results. Understanding noise levels produced by different flight maneuvers, altitudes [i.e., above ground level (AGL)], and drone models could mitigate animal disturbance during surveys. We measured maximum sound (dB) emitted during three flight maneuvers (hovering, flyover, turning) among eight AGLs (15-120 m) and two vertical maneuvers (ascending, descending) for 4 commercially available quadcopter drone models (DJI Matrice 300, Matrice 200, Phantom 3, and Autel Evo II), accounting for wind speed and comparing to ambient (background) noise. Ascending, descending, and hovering produced more noise compared to flyover and turning maneuvers. One large drone (Matrice 200, 4.7 kg) produced more noise than the two smaller drones (Evo II, 1.2 kg and Phantom 3, 1.1 kg). However, the largest drone (Matrice 300, 6.4 kg) produced noise similar to smaller models and was the quietest among all models from 75–120 m AGL, providing potential size advantages with less noise disturbance. Our results indicate that flights consisting of flyover and turning maneuvers likely cause less noise disturbance than surveys with prolonged hovering over animals.
{"title":"Drone noise differs by flight maneuver and model: implications for animal surveys","authors":"Erin N Macke, Landon Richard Jones, Raymond Iglay, Jared A Elmore","doi":"10.1139/dsa-2023-0054","DOIUrl":"https://doi.org/10.1139/dsa-2023-0054","url":null,"abstract":"Drones are becoming a common tool for animal monitoring; however, sound emitted from drones may disturb animals and bias survey results. Understanding noise levels produced by different flight maneuvers, altitudes [i.e., above ground level (AGL)], and drone models could mitigate animal disturbance during surveys. We measured maximum sound (dB) emitted during three flight maneuvers (hovering, flyover, turning) among eight AGLs (15-120 m) and two vertical maneuvers (ascending, descending) for 4 commercially available quadcopter drone models (DJI Matrice 300, Matrice 200, Phantom 3, and Autel Evo II), accounting for wind speed and comparing to ambient (background) noise. Ascending, descending, and hovering produced more noise compared to flyover and turning maneuvers. One large drone (Matrice 200, 4.7 kg) produced more noise than the two smaller drones (Evo II, 1.2 kg and Phantom 3, 1.1 kg). However, the largest drone (Matrice 300, 6.4 kg) produced noise similar to smaller models and was the quietest among all models from 75–120 m AGL, providing potential size advantages with less noise disturbance. Our results indicate that flights consisting of flyover and turning maneuvers likely cause less noise disturbance than surveys with prolonged hovering over animals.","PeriodicalId":202289,"journal":{"name":"Drone Systems and Applications","volume":"37 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140444392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Narmada M. Balasooriya, O. de Silva, Awantha Jayasiri, G. Mann
This paper proposed a novel point-based neural network landing zone detection architecture that can operate with a VLOAM navigation pipeline and investigates the accuracy-runtime trade-offs of the method for real-time applications. Based on the Semantic3D benchmark leaderboard, ConvPoint architecture was selected as the target model for the task. The work investigated different combinations of hyperparameters, i.e., batch size and sampling size, in terms of the performance metrics, i.e., inference time, throughput, and accuracy. Validation of the method was performed using custom datasets captured on a DJI M600 drone and a Bell 412 aircraft to generate the LZ module's maps at a target update rate (~ 1 Hz) while operating within a VLOAM navigation pipeline. Accurate detection of water bodies, marshlands, and low vegetation as non-landable is crucial for VTOL operations. From the results described in this paper, it is evident that to get a comparatively accurate detection of water areas in the given dataset, a larger sampling size should be set, which also can lead to lower throughput (higher inference time). This bottleneck can be resolved by fusing the semantic labels generated by the point cloud segmentation with the pixel labels generated by the color image semantic segmentation of the same region and by using a broader range of datasets to train the neural network model.
{"title":"AI-based Landing Zone Detection for Vertical Takeoff and Land LiDAR Localization and Mapping Pipelines","authors":"Narmada M. Balasooriya, O. de Silva, Awantha Jayasiri, G. Mann","doi":"10.1139/dsa-2022-0038","DOIUrl":"https://doi.org/10.1139/dsa-2022-0038","url":null,"abstract":"This paper proposed a novel point-based neural network landing zone detection architecture that can operate with a VLOAM navigation pipeline and investigates the accuracy-runtime trade-offs of the method for real-time applications. Based on the Semantic3D benchmark leaderboard, ConvPoint architecture was selected as the target model for the task. The work investigated different combinations of hyperparameters, i.e., batch size and sampling size, in terms of the performance metrics, i.e., inference time, throughput, and accuracy. Validation of the method was performed using custom datasets captured on a DJI M600 drone and a Bell 412 aircraft to generate the LZ module's maps at a target update rate (~ 1 Hz) while operating within a VLOAM navigation pipeline. Accurate detection of water bodies, marshlands, and low vegetation as non-landable is crucial for VTOL operations. From the results described in this paper, it is evident that to get a comparatively accurate detection of water areas in the given dataset, a larger sampling size should be set, which also can lead to lower throughput (higher inference time). This bottleneck can be resolved by fusing the semantic labels generated by the point cloud segmentation with the pixel labels generated by the color image semantic segmentation of the same region and by using a broader range of datasets to train the neural network model.","PeriodicalId":202289,"journal":{"name":"Drone Systems and Applications","volume":"53 40","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139844917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Narmada M. Balasooriya, O. de Silva, Awantha Jayasiri, G. Mann
This paper proposed a novel point-based neural network landing zone detection architecture that can operate with a VLOAM navigation pipeline and investigates the accuracy-runtime trade-offs of the method for real-time applications. Based on the Semantic3D benchmark leaderboard, ConvPoint architecture was selected as the target model for the task. The work investigated different combinations of hyperparameters, i.e., batch size and sampling size, in terms of the performance metrics, i.e., inference time, throughput, and accuracy. Validation of the method was performed using custom datasets captured on a DJI M600 drone and a Bell 412 aircraft to generate the LZ module's maps at a target update rate (~ 1 Hz) while operating within a VLOAM navigation pipeline. Accurate detection of water bodies, marshlands, and low vegetation as non-landable is crucial for VTOL operations. From the results described in this paper, it is evident that to get a comparatively accurate detection of water areas in the given dataset, a larger sampling size should be set, which also can lead to lower throughput (higher inference time). This bottleneck can be resolved by fusing the semantic labels generated by the point cloud segmentation with the pixel labels generated by the color image semantic segmentation of the same region and by using a broader range of datasets to train the neural network model.
{"title":"AI-based Landing Zone Detection for Vertical Takeoff and Land LiDAR Localization and Mapping Pipelines","authors":"Narmada M. Balasooriya, O. de Silva, Awantha Jayasiri, G. Mann","doi":"10.1139/dsa-2022-0038","DOIUrl":"https://doi.org/10.1139/dsa-2022-0038","url":null,"abstract":"This paper proposed a novel point-based neural network landing zone detection architecture that can operate with a VLOAM navigation pipeline and investigates the accuracy-runtime trade-offs of the method for real-time applications. Based on the Semantic3D benchmark leaderboard, ConvPoint architecture was selected as the target model for the task. The work investigated different combinations of hyperparameters, i.e., batch size and sampling size, in terms of the performance metrics, i.e., inference time, throughput, and accuracy. Validation of the method was performed using custom datasets captured on a DJI M600 drone and a Bell 412 aircraft to generate the LZ module's maps at a target update rate (~ 1 Hz) while operating within a VLOAM navigation pipeline. Accurate detection of water bodies, marshlands, and low vegetation as non-landable is crucial for VTOL operations. From the results described in this paper, it is evident that to get a comparatively accurate detection of water areas in the given dataset, a larger sampling size should be set, which also can lead to lower throughput (higher inference time). This bottleneck can be resolved by fusing the semantic labels generated by the point cloud segmentation with the pixel labels generated by the color image semantic segmentation of the same region and by using a broader range of datasets to train the neural network model.","PeriodicalId":202289,"journal":{"name":"Drone Systems and Applications","volume":"9 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139784864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Phadke, F. Medrano, Chandra N. Sekharan, Tianxing Chu
In the fast-evolving field of Uncrewed Aerial Vehicle (UAV) swarm research, there is a growing emphasis on validating results through simulation rather than hands-on hardware experiments. This article delves into this shift, focusing on fundamental research questions on whether simulation tests verify results with hardware experiments, if they mention reasons for not using hardware, and if they provide plans for future implementation using hardware. By examining relevant trends, this study aims to be among the first to address the question of whether the advancements in simulation platforms and disruption modeling have reduced the perceived need for real-world hardware-based tests to verify performance metrics. Supported by data from articles spanning a decade, this report examines global trends in UAV swarm research and experimentation. Variables such as the country, swarm size, and implementation method are reviewed to reveal current trends in how UAV swarm research is conducted and validated. It is concluded that the increase in the simulation-only deployments used by UAV swarm researchers is being readily accepted by the academic community, viewing it as a viable solution to avoid regulations on the UAV industry as well as a reflection on the advanced simulation and modeling methods being developed to support them.
{"title":"An Analysis of Trends in UAV Swarm Implementations in Current Research: Simulation Versus Hardware","authors":"A. Phadke, F. Medrano, Chandra N. Sekharan, Tianxing Chu","doi":"10.1139/dsa-2023-0099","DOIUrl":"https://doi.org/10.1139/dsa-2023-0099","url":null,"abstract":"In the fast-evolving field of Uncrewed Aerial Vehicle (UAV) swarm research, there is a growing emphasis on validating results through simulation rather than hands-on hardware experiments. This article delves into this shift, focusing on fundamental research questions on whether simulation tests verify results with hardware experiments, if they mention reasons for not using hardware, and if they provide plans for future implementation using hardware. By examining relevant trends, this study aims to be among the first to address the question of whether the advancements in simulation platforms and disruption modeling have reduced the perceived need for real-world hardware-based tests to verify performance metrics. Supported by data from articles spanning a decade, this report examines global trends in UAV swarm research and experimentation. Variables such as the country, swarm size, and implementation method are reviewed to reveal current trends in how UAV swarm research is conducted and validated. It is concluded that the increase in the simulation-only deployments used by UAV swarm researchers is being readily accepted by the academic community, viewing it as a viable solution to avoid regulations on the UAV industry as well as a reflection on the advanced simulation and modeling methods being developed to support them.","PeriodicalId":202289,"journal":{"name":"Drone Systems and Applications","volume":"45 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139849487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. Poghosyan, V. Poghosyan, Sergey Abrahamyan, A. Lazyan, Hrachya Astsatryan, Y. Alaverdyan, Karen Eguiazarian
Unmanned Aerial Vehicle (UAV) swarms have gained significant attention for their potential applications in various fields. The effective coordination and control of UAV swarms require the development of robust mathematical models that can capture their complex dynamics. The paper introduces mathematical models and relevant paradigms based on the design and analysis of self-organizing swarms of UAVs. The logical and technological construction of the model relies on the theorems developed by authors for obtaining full information exchange during the swarm quasi-random walk. The suggested rotor-router model interprets the discrete-time walk accompanied by the deterministic evolution of configurations of rotors randomly placed on the vertices of the swarm graph. The recommended optimal and fault-tolerant gossip/broadcast schemes support the resilience of swarm to internal failures and external attacks, and cryptographic protocols approve the security. The proposed cloud network topology serves as the implementation framework for the model, encompassing various connectivity options to ensure the expected behavior of the UAV swarms.
{"title":"Cloud-Based Mathematical Models for Self-organizing Swarms of UAVs: Design and Analysis","authors":"S. Poghosyan, V. Poghosyan, Sergey Abrahamyan, A. Lazyan, Hrachya Astsatryan, Y. Alaverdyan, Karen Eguiazarian","doi":"10.1139/dsa-2023-0039","DOIUrl":"https://doi.org/10.1139/dsa-2023-0039","url":null,"abstract":"Unmanned Aerial Vehicle (UAV) swarms have gained significant attention for their potential applications in various fields. The effective coordination and control of UAV swarms require the development of robust mathematical models that can capture their complex dynamics. The paper introduces mathematical models and relevant paradigms based on the design and analysis of self-organizing swarms of UAVs. The logical and technological construction of the model relies on the theorems developed by authors for obtaining full information exchange during the swarm quasi-random walk. The suggested rotor-router model interprets the discrete-time walk accompanied by the deterministic evolution of configurations of rotors randomly placed on the vertices of the swarm graph. The recommended optimal and fault-tolerant gossip/broadcast schemes support the resilience of swarm to internal failures and external attacks, and cryptographic protocols approve the security. The proposed cloud network topology serves as the implementation framework for the model, encompassing various connectivity options to ensure the expected behavior of the UAV swarms.","PeriodicalId":202289,"journal":{"name":"Drone Systems and Applications","volume":" 38","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139787879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Phadke, F. Medrano, Chandra N. Sekharan, Tianxing Chu
In the fast-evolving field of Uncrewed Aerial Vehicle (UAV) swarm research, there is a growing emphasis on validating results through simulation rather than hands-on hardware experiments. This article delves into this shift, focusing on fundamental research questions on whether simulation tests verify results with hardware experiments, if they mention reasons for not using hardware, and if they provide plans for future implementation using hardware. By examining relevant trends, this study aims to be among the first to address the question of whether the advancements in simulation platforms and disruption modeling have reduced the perceived need for real-world hardware-based tests to verify performance metrics. Supported by data from articles spanning a decade, this report examines global trends in UAV swarm research and experimentation. Variables such as the country, swarm size, and implementation method are reviewed to reveal current trends in how UAV swarm research is conducted and validated. It is concluded that the increase in the simulation-only deployments used by UAV swarm researchers is being readily accepted by the academic community, viewing it as a viable solution to avoid regulations on the UAV industry as well as a reflection on the advanced simulation and modeling methods being developed to support them.
{"title":"An Analysis of Trends in UAV Swarm Implementations in Current Research: Simulation Versus Hardware","authors":"A. Phadke, F. Medrano, Chandra N. Sekharan, Tianxing Chu","doi":"10.1139/dsa-2023-0099","DOIUrl":"https://doi.org/10.1139/dsa-2023-0099","url":null,"abstract":"In the fast-evolving field of Uncrewed Aerial Vehicle (UAV) swarm research, there is a growing emphasis on validating results through simulation rather than hands-on hardware experiments. This article delves into this shift, focusing on fundamental research questions on whether simulation tests verify results with hardware experiments, if they mention reasons for not using hardware, and if they provide plans for future implementation using hardware. By examining relevant trends, this study aims to be among the first to address the question of whether the advancements in simulation platforms and disruption modeling have reduced the perceived need for real-world hardware-based tests to verify performance metrics. Supported by data from articles spanning a decade, this report examines global trends in UAV swarm research and experimentation. Variables such as the country, swarm size, and implementation method are reviewed to reveal current trends in how UAV swarm research is conducted and validated. It is concluded that the increase in the simulation-only deployments used by UAV swarm researchers is being readily accepted by the academic community, viewing it as a viable solution to avoid regulations on the UAV industry as well as a reflection on the advanced simulation and modeling methods being developed to support them.","PeriodicalId":202289,"journal":{"name":"Drone Systems and Applications","volume":" 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139789666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study investigates the applicability of drone technology in examining Stracciacappe, a minor archaeological site through low-altitude aerial photography (LAAP). Using multispectral and thermal sensors mounted on DJI Phantom Multispectral and DJI Mavic Enterprise Advanced drones, several flight missions were conducted in November 2020, May 2021, and April 2022. The effectiveness of analyzing multispectral and thermal raw images was limited by the area's irregular vegetation, which hindered the clear detection of archaeological anomalies. However, microtopographic analysis employing various visualization techniques (VT) revealed significant traces, aligning with the site’s description found in numerous documentary sources. This includes the identification of two distinct areas within the castrum:the elevated cassarum and the burgus,along with potential traces of defensive structures within these areas. Drone analysis delineated a cassarum comprising a tower, palatium, and defensive walls, while the burgus seemed devoid of buildings, supporting the notion of a village primarily constructed with perishable materials. Thus, the study highlights the importance of using diverse sensor-based drone analyses to enhance archaeological investigations at minor sites.
{"title":"Investigating archaeological remains at Stracciacappe, Rome: comparing traditional sources with UAV-based multispectral, thermal and microtopographic analysis","authors":"Gabriele Ciccone","doi":"10.1139/dsa-2023-0114","DOIUrl":"https://doi.org/10.1139/dsa-2023-0114","url":null,"abstract":"This study investigates the applicability of drone technology in examining Stracciacappe, a minor archaeological site through low-altitude aerial photography (LAAP). Using multispectral and thermal sensors mounted on DJI Phantom Multispectral and DJI Mavic Enterprise Advanced drones, several flight missions were conducted in November 2020, May 2021, and April 2022. The effectiveness of analyzing multispectral and thermal raw images was limited by the area's irregular vegetation, which hindered the clear detection of archaeological anomalies. However, microtopographic analysis employing various visualization techniques (VT) revealed significant traces, aligning with the site’s description found in numerous documentary sources. This includes the identification of two distinct areas within the castrum:the elevated cassarum and the burgus,along with potential traces of defensive structures within these areas. Drone analysis delineated a cassarum comprising a tower, palatium, and defensive walls, while the burgus seemed devoid of buildings, supporting the notion of a village primarily constructed with perishable materials. Thus, the study highlights the importance of using diverse sensor-based drone analyses to enhance archaeological investigations at minor sites.","PeriodicalId":202289,"journal":{"name":"Drone Systems and Applications","volume":"409 24","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139848092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study investigates the applicability of drone technology in examining Stracciacappe, a minor archaeological site through low-altitude aerial photography (LAAP). Using multispectral and thermal sensors mounted on DJI Phantom Multispectral and DJI Mavic Enterprise Advanced drones, several flight missions were conducted in November 2020, May 2021, and April 2022. The effectiveness of analyzing multispectral and thermal raw images was limited by the area's irregular vegetation, which hindered the clear detection of archaeological anomalies. However, microtopographic analysis employing various visualization techniques (VT) revealed significant traces, aligning with the site’s description found in numerous documentary sources. This includes the identification of two distinct areas within the castrum:the elevated cassarum and the burgus,along with potential traces of defensive structures within these areas. Drone analysis delineated a cassarum comprising a tower, palatium, and defensive walls, while the burgus seemed devoid of buildings, supporting the notion of a village primarily constructed with perishable materials. Thus, the study highlights the importance of using diverse sensor-based drone analyses to enhance archaeological investigations at minor sites.
{"title":"Investigating archaeological remains at Stracciacappe, Rome: comparing traditional sources with UAV-based multispectral, thermal and microtopographic analysis","authors":"Gabriele Ciccone","doi":"10.1139/dsa-2023-0114","DOIUrl":"https://doi.org/10.1139/dsa-2023-0114","url":null,"abstract":"This study investigates the applicability of drone technology in examining Stracciacappe, a minor archaeological site through low-altitude aerial photography (LAAP). Using multispectral and thermal sensors mounted on DJI Phantom Multispectral and DJI Mavic Enterprise Advanced drones, several flight missions were conducted in November 2020, May 2021, and April 2022. The effectiveness of analyzing multispectral and thermal raw images was limited by the area's irregular vegetation, which hindered the clear detection of archaeological anomalies. However, microtopographic analysis employing various visualization techniques (VT) revealed significant traces, aligning with the site’s description found in numerous documentary sources. This includes the identification of two distinct areas within the castrum:the elevated cassarum and the burgus,along with potential traces of defensive structures within these areas. Drone analysis delineated a cassarum comprising a tower, palatium, and defensive walls, while the burgus seemed devoid of buildings, supporting the notion of a village primarily constructed with perishable materials. Thus, the study highlights the importance of using diverse sensor-based drone analyses to enhance archaeological investigations at minor sites.","PeriodicalId":202289,"journal":{"name":"Drone Systems and Applications","volume":" 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139787942","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. Poghosyan, V. Poghosyan, Sergey Abrahamyan, A. Lazyan, Hrachya Astsatryan, Y. Alaverdyan, Karen Eguiazarian
Unmanned Aerial Vehicle (UAV) swarms have gained significant attention for their potential applications in various fields. The effective coordination and control of UAV swarms require the development of robust mathematical models that can capture their complex dynamics. The paper introduces mathematical models and relevant paradigms based on the design and analysis of self-organizing swarms of UAVs. The logical and technological construction of the model relies on the theorems developed by authors for obtaining full information exchange during the swarm quasi-random walk. The suggested rotor-router model interprets the discrete-time walk accompanied by the deterministic evolution of configurations of rotors randomly placed on the vertices of the swarm graph. The recommended optimal and fault-tolerant gossip/broadcast schemes support the resilience of swarm to internal failures and external attacks, and cryptographic protocols approve the security. The proposed cloud network topology serves as the implementation framework for the model, encompassing various connectivity options to ensure the expected behavior of the UAV swarms.
{"title":"Cloud-Based Mathematical Models for Self-organizing Swarms of UAVs: Design and Analysis","authors":"S. Poghosyan, V. Poghosyan, Sergey Abrahamyan, A. Lazyan, Hrachya Astsatryan, Y. Alaverdyan, Karen Eguiazarian","doi":"10.1139/dsa-2023-0039","DOIUrl":"https://doi.org/10.1139/dsa-2023-0039","url":null,"abstract":"Unmanned Aerial Vehicle (UAV) swarms have gained significant attention for their potential applications in various fields. The effective coordination and control of UAV swarms require the development of robust mathematical models that can capture their complex dynamics. The paper introduces mathematical models and relevant paradigms based on the design and analysis of self-organizing swarms of UAVs. The logical and technological construction of the model relies on the theorems developed by authors for obtaining full information exchange during the swarm quasi-random walk. The suggested rotor-router model interprets the discrete-time walk accompanied by the deterministic evolution of configurations of rotors randomly placed on the vertices of the swarm graph. The recommended optimal and fault-tolerant gossip/broadcast schemes support the resilience of swarm to internal failures and external attacks, and cryptographic protocols approve the security. The proposed cloud network topology serves as the implementation framework for the model, encompassing various connectivity options to ensure the expected behavior of the UAV swarms.","PeriodicalId":202289,"journal":{"name":"Drone Systems and Applications","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139847574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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