Rails are a highly secretive group of marshland obligate species that are difficult to consistently survey and detect. Current survey efforts utilize either call-playback or autonomous recording devices, but the low detection probabilities for rails create challenges for long-term systematic monitoring. Between 8 April and 16 May 2022, we flew a small aerial drone equipped with a thermal camera to survey for six species of rail (Back Rail [Laterallus jamaicensis]; Yellow Rail [Coturnicops noveboracensis]; Sora [Porzana carolina]; Virginia Rail [Rallus limicola]; Clapper Rail [R. crepitans]; King Rail [R. elegans]) along the Gulf Coast of Texas in order to assess the feasibility of long-term drone monitoring. We successfully conducted 34 flights and detected rails 55.5% of the time at known occupied points. We achieved 27 total rail detections, including 12 Black Rail/Yellow Rail detections. Of the birds detected, 81% exhibited no response to the drone’s first approach. We intend for this preliminary data to shape future survey protocol for secretive species occupying difficult to navigate terrain.
{"title":"Preliminary Assessment of Thermal Imaging Equipped Aerial Drones for Secretive Marsh Bird Detection","authors":"Tabitha W. Olsen, Trey Barron, Christopher Butler","doi":"10.1139/dsa-2022-0046","DOIUrl":"https://doi.org/10.1139/dsa-2022-0046","url":null,"abstract":"Rails are a highly secretive group of marshland obligate species that are difficult to consistently survey and detect. Current survey efforts utilize either call-playback or autonomous recording devices, but the low detection probabilities for rails create challenges for long-term systematic monitoring. Between 8 April and 16 May 2022, we flew a small aerial drone equipped with a thermal camera to survey for six species of rail (Back Rail [Laterallus jamaicensis]; Yellow Rail [Coturnicops noveboracensis]; Sora [Porzana carolina]; Virginia Rail [Rallus limicola]; Clapper Rail [R. crepitans]; King Rail [R. elegans]) along the Gulf Coast of Texas in order to assess the feasibility of long-term drone monitoring. We successfully conducted 34 flights and detected rails 55.5% of the time at known occupied points. We achieved 27 total rail detections, including 12 Black Rail/Yellow Rail detections. Of the birds detected, 81% exhibited no response to the drone’s first approach. We intend for this preliminary data to shape future survey protocol for secretive species occupying difficult to navigate terrain.","PeriodicalId":202289,"journal":{"name":"Drone Systems and Applications","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121670203","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}
Jose Pablo Sibaja Brenes, A. Terada, R. Alfaro-Solís, Mario Cambronero-Luna, Danilo Umaña-Castro, Daniel Porras-Ramírez, R. Sánchez-Gutiérrez, Mariela Martínez Arroyo, Ian Godfrey, M. Martínez-Cruz
For the first time ever, samples were collected from volcanic lake waters in Costa Rica using an Unmanned Aerial Vehicle (drone), which represents a major achievement in human-machine interaction, and innovation in the technology sector. A Matrice 600 Pro drone was used for remote sampling in the hyperacid crater lake of the Poás volcano, the mildly acidic Lake Botos, and the nearly neutral Lake Hule. A bailer bottle of 250 mL and a HOBO temperature probe, mounted on the drone, were deployed using a specially designed delivery-retrieval system. A comparison was carried out relating to the geochemistry of lake water collected by drone as opposed to the hand-collected samples. The SO4-2/Cl ratios of the two samples at Poás hyperacid crater lake were similar, (1.1 ± 0.2) on average, an indication of a lake with homogenous water composition. The Lake Hule showed a similar composition to that registered twenty years ago. The waters from Lake Botos showed some differences, which may be explained by the influence of springs at the bottom of the lake, but the Wilcoxon signed-rank test showed a satisfactory level of similarity. Autonomous navigation proves to be very useful for faster, more efficient, reliable, and less hazardous sampling of volcanic lakes.
{"title":"Drone monitoring of volcanic lakes in Costa Rica: a new approach","authors":"Jose Pablo Sibaja Brenes, A. Terada, R. Alfaro-Solís, Mario Cambronero-Luna, Danilo Umaña-Castro, Daniel Porras-Ramírez, R. Sánchez-Gutiérrez, Mariela Martínez Arroyo, Ian Godfrey, M. Martínez-Cruz","doi":"10.1139/dsa-2022-0023","DOIUrl":"https://doi.org/10.1139/dsa-2022-0023","url":null,"abstract":"For the first time ever, samples were collected from volcanic lake waters in Costa Rica using an Unmanned Aerial Vehicle (drone), which represents a major achievement in human-machine interaction, and innovation in the technology sector. A Matrice 600 Pro drone was used for remote sampling in the hyperacid crater lake of the Poás volcano, the mildly acidic Lake Botos, and the nearly neutral Lake Hule. A bailer bottle of 250 mL and a HOBO temperature probe, mounted on the drone, were deployed using a specially designed delivery-retrieval system. A comparison was carried out relating to the geochemistry of lake water collected by drone as opposed to the hand-collected samples. The SO4-2/Cl ratios of the two samples at Poás hyperacid crater lake were similar, (1.1 ± 0.2) on average, an indication of a lake with homogenous water composition. The Lake Hule showed a similar composition to that registered twenty years ago. The waters from Lake Botos showed some differences, which may be explained by the influence of springs at the bottom of the lake, but the Wilcoxon signed-rank test showed a satisfactory level of similarity. Autonomous navigation proves to be very useful for faster, more efficient, reliable, and less hazardous sampling of volcanic lakes.","PeriodicalId":202289,"journal":{"name":"Drone Systems and Applications","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115851440","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}
Ryan Ward, Brett Readman, Brennan O'Yeung, W. Hinman
In this study, a methodology for the high-level conceptual design, optimization, and evaluation of amphibious remotely piloted and autonomous fixed-wing aircraft to support wildfire air attack strategies is presented. Of particular interest are questions of scale, water source utilization, and optimization of high-level aircraft parameters in a regional context. The Canadian province of British Columbia is used as a case study due to the relevance of wildfire control in that region. The present strategy incorporates a detailed analysis of available water bodies, tanker base locations, and their distance from historical wildfire locations and explores how these regionally specific details impact optimal aircraft design parameters. Results are obtained for optimal lake size as well as the primary design characteristics of the corresponding optimal aircraft. Two filling strategies are evaluated, namely, a "stop and go" strategy and a traditional skimming strategy. The results indicate the potential of fleets of optimized aircraft to supply high flow rates while capitalizing on the established benefits of using remotely piloted and autonomous systems. It is hoped this work will encourage future study into improved models and the further development of drone technology for this application - including necessary beyond visual line of sight technology and infrastructure.
{"title":"Conceptual optimization of remotely piloted amphibious aircraft for wildfire air attack","authors":"Ryan Ward, Brett Readman, Brennan O'Yeung, W. Hinman","doi":"10.1139/dsa-2022-0051","DOIUrl":"https://doi.org/10.1139/dsa-2022-0051","url":null,"abstract":"In this study, a methodology for the high-level conceptual design, optimization, and evaluation of amphibious remotely piloted and autonomous fixed-wing aircraft to support wildfire air attack strategies is presented. Of particular interest are questions of scale, water source utilization, and optimization of high-level aircraft parameters in a regional context. The Canadian province of British Columbia is used as a case study due to the relevance of wildfire control in that region. The present strategy incorporates a detailed analysis of available water bodies, tanker base locations, and their distance from historical wildfire locations and explores how these regionally specific details impact optimal aircraft design parameters. Results are obtained for optimal lake size as well as the primary design characteristics of the corresponding optimal aircraft. Two filling strategies are evaluated, namely, a \"stop and go\" strategy and a traditional skimming strategy. The results indicate the potential of fleets of optimized aircraft to supply high flow rates while capitalizing on the established benefits of using remotely piloted and autonomous systems. It is hoped this work will encourage future study into improved models and the further development of drone technology for this application - including necessary beyond visual line of sight technology and infrastructure.","PeriodicalId":202289,"journal":{"name":"Drone Systems and Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128756288","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}
Employment of Unmanned Aerial Vehicles (UAVs) or drones as swarms of coordinating nodes offers multiple advantages for commercial as well as military applications. However, the complex communication requirements of these swarms, coupled with high data rates of advanced UAV payloads require innovative techniques for optimizing data throughput. Channel capacity being the key resource, optimum communication architecture and network topology is critical to ensure QoS while remaining within transmission power constraints. This paper proposes a capacity maximization approach for swarm communications architectures using Mixed Integer Non-Linear Programming (MINLP). These techniques are designed to tackle optimization applications involving both discrete variables and nonlinear system dynamics. Mathematical model formulated considering system constraints and desired objective function establishes applicability of MINLP. Since MINLP problems are NP hard in general, computational overheads and search space exponentially grows with number of nodes in the swarm. Therefore, Outer Approximation Algorithm (OAA) has been applied that achieves near-optimal solutions with reduced convergence time and complexity compared to exhaustive search. Applicability of algorithm regardless of selected communication architecture has been established through realistic simulations.
{"title":"Communication Capacity Maximization in Drone Swarms","authors":"Farrukh Javed, R. Anjum, Humayun Zubair Khan","doi":"10.1139/dsa-2023-0002","DOIUrl":"https://doi.org/10.1139/dsa-2023-0002","url":null,"abstract":"Employment of Unmanned Aerial Vehicles (UAVs) or drones as swarms of coordinating nodes offers multiple advantages for commercial as well as military applications. However, the complex communication requirements of these swarms, coupled with high data rates of advanced UAV payloads require innovative techniques for optimizing data throughput. Channel capacity being the key resource, optimum communication architecture and network topology is critical to ensure QoS while remaining within transmission power constraints. This paper proposes a capacity maximization approach for swarm communications architectures using Mixed Integer Non-Linear Programming (MINLP). These techniques are designed to tackle optimization applications involving both discrete variables and nonlinear system dynamics. Mathematical model formulated considering system constraints and desired objective function establishes applicability of MINLP. Since MINLP problems are NP hard in general, computational overheads and search space exponentially grows with number of nodes in the swarm. Therefore, Outer Approximation Algorithm (OAA) has been applied that achieves near-optimal solutions with reduced convergence time and complexity compared to exhaustive search. Applicability of algorithm regardless of selected communication architecture has been established through realistic simulations.","PeriodicalId":202289,"journal":{"name":"Drone Systems and Applications","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128053168","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}
Prashant Kumar, Bisheswar Choudhury, Amandeep Singh, J. Ramkumar, Deepu Philip, A. K. Ghosh
This study proposes a framework for developing a realistic model for throttle and servo control algorithms for a Powered Parafoil Unmanned Aerial Vehicle (PPUAV) using Artificial Neural Networks (ANN). Two servo motors on an L-shaped platform, controls and steers the PPUAV. Six degrees of freedom (DOF) mathematical model of a dynamic parafoil system is built to test the technique's efficacy using a simulation in which disturbances mimic actual flights. A guiding law is then established, including the cross-track error and the line of sight approach. Furthermore, a path-following controller is constructed using the proportional-integral-derivative (PID), and a simulation platform was created to evaluate numerical data illustrating the route's validity following the technique. PPUAV was developed, built, and instrumented to collect real-time flight data to test the controller. These dynamic characteristics were sent into the ANN for training. A diverging-converging design was identified to obtain the best consistency between predicted and observed Throttle and servo control values. For a comparable flight route, the control signal of the simulated model is compared to that of the actual and ANN predicted models. The comparative findings show that the ANN-predicted and actual control inputs were almost identical, with an 80-99 % match. However, the simulated response showed deviation from the actual control input, with an accuracy of 50-80%.
{"title":"Modeling and Prediction of Powered Parafoil Unmanned Aerial Vehicle Throttle and Servo Controls through Artificial Neural Networks","authors":"Prashant Kumar, Bisheswar Choudhury, Amandeep Singh, J. Ramkumar, Deepu Philip, A. K. Ghosh","doi":"10.1139/dsa-2022-0040","DOIUrl":"https://doi.org/10.1139/dsa-2022-0040","url":null,"abstract":"This study proposes a framework for developing a realistic model for throttle and servo control algorithms for a Powered Parafoil Unmanned Aerial Vehicle (PPUAV) using Artificial Neural Networks (ANN). Two servo motors on an L-shaped platform, controls and steers the PPUAV. Six degrees of freedom (DOF) mathematical model of a dynamic parafoil system is built to test the technique's efficacy using a simulation in which disturbances mimic actual flights. A guiding law is then established, including the cross-track error and the line of sight approach. Furthermore, a path-following controller is constructed using the proportional-integral-derivative (PID), and a simulation platform was created to evaluate numerical data illustrating the route's validity following the technique. PPUAV was developed, built, and instrumented to collect real-time flight data to test the controller. These dynamic characteristics were sent into the ANN for training. A diverging-converging design was identified to obtain the best consistency between predicted and observed Throttle and servo control values. For a comparable flight route, the control signal of the simulated model is compared to that of the actual and ANN predicted models. The comparative findings show that the ANN-predicted and actual control inputs were almost identical, with an 80-99 % match. However, the simulated response showed deviation from the actual control input, with an accuracy of 50-80%.","PeriodicalId":202289,"journal":{"name":"Drone Systems and Applications","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121638848","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}
M. Leclerc, John Bass, Mathieu Labbé, David Dozois, Jonathan Delisle, David Rancourt, Alexis Lussier Desbiens
Underground stope mapping is crucial to evaluate the quantity of blasted rock and the site integrity. In recent years, lidar-equipped drones have been used to map stopes with higher precision and without blind spots. However, they have limitations, like large size, challenging lidar positioning on the drone, limited flight time for detailed visual inspections, and unreliable communication underground. This paper discusses the development of a compact tethered drone called the NetherDrone, specifically designed for stope inspections. The NetherDrone uses custom ducted propulsion to increase thrust efficiency by 50%. It reduces the propellers’ diameter and overall frame while maintaining an adequate lifting capability with low power consumption. The drone features an onboard 120 m tether spool for communication and power transmission, as well as a rotating arm to deploy the cable and reduce yaw moments from the tether tension. Flights in a real stope demonstrated that the drone could effectively move at least 50 m deep into a complex stope, complete a detailed lidar scan, visually scan one face of the stope in close proximity during 20 min, travel a total distance of 270 m, and maintain communications with an operator at all-time through the tether
{"title":"NetherDrone: A tethered and ducted propulsion multirotor drone for complex underground mining stopes inspection","authors":"M. Leclerc, John Bass, Mathieu Labbé, David Dozois, Jonathan Delisle, David Rancourt, Alexis Lussier Desbiens","doi":"10.1139/dsa-2023-0001","DOIUrl":"https://doi.org/10.1139/dsa-2023-0001","url":null,"abstract":"Underground stope mapping is crucial to evaluate the quantity of blasted rock and the site integrity. In recent years, lidar-equipped drones have been used to map stopes with higher precision and without blind spots. However, they have limitations, like large size, challenging lidar positioning on the drone, limited flight time for detailed visual inspections, and unreliable communication underground. This paper discusses the development of a compact tethered drone called the NetherDrone, specifically designed for stope inspections. The NetherDrone uses custom ducted propulsion to increase thrust efficiency by 50%. It reduces the propellers’ diameter and overall frame while maintaining an adequate lifting capability with low power consumption. The drone features an onboard 120 m tether spool for communication and power transmission, as well as a rotating arm to deploy the cable and reduce yaw moments from the tether tension. Flights in a real stope demonstrated that the drone could effectively move at least 50 m deep into a complex stope, complete a detailed lidar scan, visually scan one face of the stope in close proximity during 20 min, travel a total distance of 270 m, and maintain communications with an operator at all-time through the tether","PeriodicalId":202289,"journal":{"name":"Drone Systems and Applications","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122840136","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}
K. Tennakoon, O. de Silva, Awantha Jayasiri, G. Mann, R. Gosine
This article proposes a mobile robot localization system developed using Google Indoor Street View (GISV) and Convolutional Neural Network (CNN) based visual place recognition. The proposed localization system consists of two main modules. The first is a place recognition module based on GISV and a net Vector of Locally Aggregated Descriptors (VLAD)-based CNN. The second is a factor graph-based optimization module. In this work, we show that a CNN-based approach can be utilized to overcome the lack of visually distinct features in indoor environments and changes in images that can occur when using different cameras at different points in time for localization. The proposed CNN-based localization system is implemented using reference and query images obtained from two different sources (GISV and a camera attached to a mobile robot). It has been experimentally validated using a custom indoor dataset captured at the Memorial University of Newfoundland (MUN) engineering building basement. The main results of this paper show that GISV-based place recognition reduces the percentage drift by 4 % for the dataset and achieves a Root Mean Square Error (RMSE) of 2 m for position and 2.5° for orientation.
{"title":"Factor Graph Localization for Mobile Robots using Google Indoor Street View and CNN-based Place Recognition","authors":"K. Tennakoon, O. de Silva, Awantha Jayasiri, G. Mann, R. Gosine","doi":"10.1139/dsa-2022-0045","DOIUrl":"https://doi.org/10.1139/dsa-2022-0045","url":null,"abstract":"This article proposes a mobile robot localization system developed using Google Indoor Street View (GISV) and Convolutional Neural Network (CNN) based visual place recognition. The proposed localization system consists of two main modules. The first is a place recognition module based on GISV and a net Vector of Locally Aggregated Descriptors (VLAD)-based CNN. The second is a factor graph-based optimization module. In this work, we show that a CNN-based approach can be utilized to overcome the lack of visually distinct features in indoor environments and changes in images that can occur when using different cameras at different points in time for localization. The proposed CNN-based localization system is implemented using reference and query images obtained from two different sources (GISV and a camera attached to a mobile robot). It has been experimentally validated using a custom indoor dataset captured at the Memorial University of Newfoundland (MUN) engineering building basement. The main results of this paper show that GISV-based place recognition reduces the percentage drift by 4 % for the dataset and achieves a Root Mean Square Error (RMSE) of 2 m for position and 2.5° for orientation.","PeriodicalId":202289,"journal":{"name":"Drone Systems and Applications","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127821418","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 paper describes the partial automation of drones (also referred to as uncrewed air vehicles, UAVs or aerial robots) in a populated area within the visual line-of-sight of its pilot. Mission responsiveness is improved by reducing the number of human crew members and avoiding the need for area clearance, while carefully managing the workload of those remaining to ensure no compromise on safety. The work employs a system-centric approach with regards to integrating human and automation tasks based on their capabilities and use of standard procedures whilst prioritizing the predictability and simplicity of the overall system. Safety claims about the proposed system are posed and rigorously analyzed through a structured safety case. The proposed system is applied to a bridge inspection case study with simulation results and scenario analysis.
{"title":"Semi-autonomous drone control with safety analysis","authors":"Hirad Goudarzi, Arthur G. Richards","doi":"10.1139/dsa-2022-0031","DOIUrl":"https://doi.org/10.1139/dsa-2022-0031","url":null,"abstract":"This paper describes the partial automation of drones (also referred to as uncrewed air vehicles, UAVs or aerial robots) in a populated area within the visual line-of-sight of its pilot. Mission responsiveness is improved by reducing the number of human crew members and avoiding the need for area clearance, while carefully managing the workload of those remaining to ensure no compromise on safety. The work employs a system-centric approach with regards to integrating human and automation tasks based on their capabilities and use of standard procedures whilst prioritizing the predictability and simplicity of the overall system. Safety claims about the proposed system are posed and rigorously analyzed through a structured safety case. The proposed system is applied to a bridge inspection case study with simulation results and scenario analysis.","PeriodicalId":202289,"journal":{"name":"Drone Systems and Applications","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127280281","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}
R. Millar, Leila Hashemi, Armin Mahmoodi, Robert Walter Meyer, J. Laliberté
This paper presents and assesses the feasibility and potential of a novel concept: the operation of multiple unmanned vehicles (UAV) commanded and supported by a manned “Tender” air vehicle carrying a pilot and flight manager(s). The "Tender" is equipped to flexibly and economically monitor and manage multiple diverse UAVs over otherwise inaccessible terrain through wireless communication. Further, this paper seeks to find the optimal trajectories for UAVs to collect data from sensors in a predefined continuous space. We formulate the path-planning problem for a cooperative, and a diverse swarm of UAVs tasked with optimizing multiple objectives simultaneously with the goal of maximizing accumulated data within a given flight time within cloud data processing constraints as well as minimizing the probable imposed risk during UAVs mission. To this end, as the problem is formulated as a convex optimization model, and we propose a low complexity Multi-Objective Reinforcement Learning (MORL) algorithm with a provable performance guarantee to solve the problem efficiently. We show that the MORL architecture can be successfully trained and allows each UAV to map each observation of the network state to an action to make optimal movement decisions.
{"title":"Integrating Unmanned and Manned UAVs data network based on combined Bayesian Belief network and Multi-objective reinforcement learning algorithm","authors":"R. Millar, Leila Hashemi, Armin Mahmoodi, Robert Walter Meyer, J. Laliberté","doi":"10.1139/dsa-2022-0043","DOIUrl":"https://doi.org/10.1139/dsa-2022-0043","url":null,"abstract":"This paper presents and assesses the feasibility and potential of a novel concept: the operation of multiple unmanned vehicles (UAV) commanded and supported by a manned “Tender” air vehicle carrying a pilot and flight manager(s). The \"Tender\" is equipped to flexibly and economically monitor and manage multiple diverse UAVs over otherwise inaccessible terrain through wireless communication. Further, this paper seeks to find the optimal trajectories for UAVs to collect data from sensors in a predefined continuous space. We formulate the path-planning problem for a cooperative, and a diverse swarm of UAVs tasked with optimizing multiple objectives simultaneously with the goal of maximizing accumulated data within a given flight time within cloud data processing constraints as well as minimizing the probable imposed risk during UAVs mission. To this end, as the problem is formulated as a convex optimization model, and we propose a low complexity Multi-Objective Reinforcement Learning (MORL) algorithm with a provable performance guarantee to solve the problem efficiently. We show that the MORL architecture can be successfully trained and allows each UAV to map each observation of the network state to an action to make optimal movement decisions.","PeriodicalId":202289,"journal":{"name":"Drone Systems and Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128912838","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}
As technology and innovations in unmanned aerial vehicles progress, so does the need for regulations in place to create safe and controlled flying scenarios. The Federal Aviation Administration (FAA) is a governing body under the United States Department of Transportation that is responsible for a wide range of regulatory activities related to the United States airspace. In a recently published final rule, the FAA addresses several concerns such as the need for a system to identify all aircrafts flying in national airspace, as well as the implementation of a separate system from the prevalent Automatic Dependent Surveillance–Broadcast system to prevent interference with manned aircrafts. Their solution to these concerns is the deployment of remote identification (RID) on all unmanned aircraft systems (UAS) flying under its implied jurisdiction. While US governing agencies retain the use of the word UAS for now, the International Civil Aviation Organization terminology is remotely piloted aircraft systems. The FAA describes the RID implementation as a “ Digital license plate” for all UAS flying in the United States airspace. They outline additional policies including several options for compliance, operating rules, and design and production guidelines for manufacturers. As the September 2023 deadline for compliance draws near, this article highlights possible deployment applications and challenges.
{"title":"Navigating the skies: examining the FAA’s remote identification rule for unmanned aircraft systems","authors":"A. Phadke, Josh Boyd, F. A. Medrano, M. Starek","doi":"10.1139/dsa-2023-0029","DOIUrl":"https://doi.org/10.1139/dsa-2023-0029","url":null,"abstract":"As technology and innovations in unmanned aerial vehicles progress, so does the need for regulations in place to create safe and controlled flying scenarios. The Federal Aviation Administration (FAA) is a governing body under the United States Department of Transportation that is responsible for a wide range of regulatory activities related to the United States airspace. In a recently published final rule, the FAA addresses several concerns such as the need for a system to identify all aircrafts flying in national airspace, as well as the implementation of a separate system from the prevalent Automatic Dependent Surveillance–Broadcast system to prevent interference with manned aircrafts. Their solution to these concerns is the deployment of remote identification (RID) on all unmanned aircraft systems (UAS) flying under its implied jurisdiction. While US governing agencies retain the use of the word UAS for now, the International Civil Aviation Organization terminology is remotely piloted aircraft systems. The FAA describes the RID implementation as a “ Digital license plate” for all UAS flying in the United States airspace. They outline additional policies including several options for compliance, operating rules, and design and production guidelines for manufacturers. As the September 2023 deadline for compliance draws near, this article highlights possible deployment applications and challenges.","PeriodicalId":202289,"journal":{"name":"Drone Systems and Applications","volume":"11 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123872717","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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