Kyeongtae Jeong, Chaeyeon Yu, Donghoon Lee, Sungjin Kim
Recent studies have been focusing on unmanned aircraft systems (UASs) to inspect safety issues in the construction industry. A UAS can monitor a broad range in real time and identify unsafe situations and objects at the jobsite. The related studies mostly focus on technological development, and there are few studies investigating potential performance that can be obtained by implementing UASs in the construction domain. Hence, the main objective of this research is to evaluate the potential of UAS-based construction safety inspection. To achieve the goal, this study developed a system dynamic (SD) model, and scenario analysis was conducted. When compared to the existing methods, the use of a UAS resulted in improved safety inspection performance, reduced possibility of incidents, reduced worker fatigue, and reduced amount of delayed work. The results of this research verified that UAS-based safety inspections can be more effective than existing methods. The results of this study can contribute to the understanding of UAS-based construction safety inspection technologies and the potential of the technology.
{"title":"A Computational Model for Simulating the Performance of UAS-Based Construction Safety Inspection through a System Approach","authors":"Kyeongtae Jeong, Chaeyeon Yu, Donghoon Lee, Sungjin Kim","doi":"10.3390/drones7120696","DOIUrl":"https://doi.org/10.3390/drones7120696","url":null,"abstract":"Recent studies have been focusing on unmanned aircraft systems (UASs) to inspect safety issues in the construction industry. A UAS can monitor a broad range in real time and identify unsafe situations and objects at the jobsite. The related studies mostly focus on technological development, and there are few studies investigating potential performance that can be obtained by implementing UASs in the construction domain. Hence, the main objective of this research is to evaluate the potential of UAS-based construction safety inspection. To achieve the goal, this study developed a system dynamic (SD) model, and scenario analysis was conducted. When compared to the existing methods, the use of a UAS resulted in improved safety inspection performance, reduced possibility of incidents, reduced worker fatigue, and reduced amount of delayed work. The results of this research verified that UAS-based safety inspections can be more effective than existing methods. The results of this study can contribute to the understanding of UAS-based construction safety inspection technologies and the potential of the technology.","PeriodicalId":36448,"journal":{"name":"Drones","volume":"56 20","pages":""},"PeriodicalIF":4.8,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138593032","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}
Christian Eckert, Kim I. Monteforte, Daniel P. Harrison, Brendan P. Kelaher
Understanding the atmospheric conditions in remote areas contributes to assessing local weather phenomena. Obtaining vertical profiles of the atmosphere in isolated locations can introduce significant challenges for the deployment and maintenance of equipment, as well as regulatory obstacles. Here, we assessed the potential of consumer drones equipped with lightweight atmospheric sensors to collect vertical meteorological profiles off One Tree Island (Great Barrier Reef), located approximately 85 km off the east coast of Australia. We used a DJI Matrice 300 drone with two InterMet Systems iMet-XQ2 UAV sensors, capturing data on atmospheric pressure, temperature, relative humidity, and wind up to an altitude of 1500 m. These flights were conducted three times per day (9 a.m., 12 noon, and 3 p.m.) and compared against ground-based weather sensors. Over the Austral summer/autumn, we completed 72 flights, obtaining 24 complete sets of daily measurements of atmospheric characteristics over the entire vertical profile. On average, the atmospheric temperature and dewpoint temperature were significantly influenced by the time of sampling, and also varied among days. The mean daily temperature and dewpoint temperature reached their peaks at 3 p.m., with the temperature gradually rising from its morning low. The mean dewpoint temperature obtained its lowest point around noon. We also observed wind speed variations, but changes in patterns throughout the day were much less consistent. The drone-mounted atmospheric sensors exhibited a consistent warm bias in temperature compared to the reference weather station. Relative humidity showed greater variability with no clear bias pattern, indicating potential limitations in the humidity sensor’s performance. Microscale temperature inversions were prevalent around 1000 m, peaking around noon and present in approximately 27% of the profiles. Overall, the drone-based vertical profiles helped characterise atmospheric dynamics around One Tree Island Reef and demonstrated the utility of consumer drones in providing cost-effective meteorological information in remote, environmentally sensitive areas.
了解偏远地区的大气状况有助于评估当地的天气现象。在孤立的地点获取大气的垂直剖面可能会给设备的部署和维护带来重大挑战，以及监管障碍。在这里，我们评估了配备轻型大气传感器的消费级无人机在距离澳大利亚东海岸约85公里的One Tree Island(大堡礁)收集垂直气象剖面的潜力。我们使用了一架带有两个InterMet Systems iMet-XQ2无人机传感器的大疆matrix 300无人机，捕获了海拔1500米的大气压力、温度、相对湿度和风的数据。这些飞行每天进行三次(上午9点，中午12点和下午3点)，并与地面气象传感器进行比较。在南半球的夏季和秋季，我们完成了72次飞行，在整个垂直剖面上获得了24套完整的每日大气特征测量数据。平均而言，大气温度和露点温度受采样时间的影响显著，且随采样时间的变化而变化。日平均气温和露点温度在下午3点达到峰值，从早上的低点逐渐上升。平均露点温度在中午左右达到最低点。我们也观察到风速的变化，但全天的变化模式不太一致。与参考气象站相比，无人机安装的大气传感器在温度上表现出一致的暖偏。相对湿度表现出较大的变异性，但没有明显的偏置模式，这表明湿度传感器的性能存在潜在的局限性。微尺度温度逆温在1000米左右普遍存在，在中午左右达到峰值，约占剖面的27%。总体而言，基于无人机的垂直剖面有助于描述One Tree Island Reef周围的大气动力学特征，并展示了消费级无人机在偏远、环境敏感地区提供具有成本效益的气象信息方面的实用性。
{"title":"Exploring Meteorological Conditions and Microscale Temperature Inversions above the Great Barrier Reef through Drone-Based Measurements","authors":"Christian Eckert, Kim I. Monteforte, Daniel P. Harrison, Brendan P. Kelaher","doi":"10.3390/drones7120695","DOIUrl":"https://doi.org/10.3390/drones7120695","url":null,"abstract":"Understanding the atmospheric conditions in remote areas contributes to assessing local weather phenomena. Obtaining vertical profiles of the atmosphere in isolated locations can introduce significant challenges for the deployment and maintenance of equipment, as well as regulatory obstacles. Here, we assessed the potential of consumer drones equipped with lightweight atmospheric sensors to collect vertical meteorological profiles off One Tree Island (Great Barrier Reef), located approximately 85 km off the east coast of Australia. We used a DJI Matrice 300 drone with two InterMet Systems iMet-XQ2 UAV sensors, capturing data on atmospheric pressure, temperature, relative humidity, and wind up to an altitude of 1500 m. These flights were conducted three times per day (9 a.m., 12 noon, and 3 p.m.) and compared against ground-based weather sensors. Over the Austral summer/autumn, we completed 72 flights, obtaining 24 complete sets of daily measurements of atmospheric characteristics over the entire vertical profile. On average, the atmospheric temperature and dewpoint temperature were significantly influenced by the time of sampling, and also varied among days. The mean daily temperature and dewpoint temperature reached their peaks at 3 p.m., with the temperature gradually rising from its morning low. The mean dewpoint temperature obtained its lowest point around noon. We also observed wind speed variations, but changes in patterns throughout the day were much less consistent. The drone-mounted atmospheric sensors exhibited a consistent warm bias in temperature compared to the reference weather station. Relative humidity showed greater variability with no clear bias pattern, indicating potential limitations in the humidity sensor’s performance. Microscale temperature inversions were prevalent around 1000 m, peaking around noon and present in approximately 27% of the profiles. Overall, the drone-based vertical profiles helped characterise atmospheric dynamics around One Tree Island Reef and demonstrated the utility of consumer drones in providing cost-effective meteorological information in remote, environmentally sensitive areas.","PeriodicalId":36448,"journal":{"name":"Drones","volume":"30 16","pages":""},"PeriodicalIF":4.8,"publicationDate":"2023-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138604013","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}
Research on developing a smart security system is based on Artificial Intelligence with an unmanned aerial vehicle (UAV) to detect and monitor alert situations, such as fire accidents and theft/intruders in the building or factory, which is based on the Internet of Things (IoT) network. The system includes a Passive Pyroelectric Infrared Detector for human detection and an analog flame sensor to sense the appearance of the concerned objects and then transmit the signal to the workstation via Wi-Fi based on the microcontroller Espressif32 (Esp32). The computer vision models YOLOv8 (You Only Look Once version 8) and Cascade Classifier are trained and implemented into the workstation, which is able to identify people, some potentially dangerous objects, and fire. The drone is also controlled by three algorithms—distance maintenance, automatic yaw rotation, and potentially dangerous object avoidance—with the support of a proportional–integral–derivative (PID) controller. The Smart Drone Surveillance System has good commands for automatic tracking and streaming of the video of these specific circumstances and then transferring the data to the involved parties such as security or staff.
{"title":"Smart Drone Surveillance System Based on AI and on IoT Communication in Case of Intrusion and Fire Accident","authors":"M. Hoang","doi":"10.3390/drones7120694","DOIUrl":"https://doi.org/10.3390/drones7120694","url":null,"abstract":"Research on developing a smart security system is based on Artificial Intelligence with an unmanned aerial vehicle (UAV) to detect and monitor alert situations, such as fire accidents and theft/intruders in the building or factory, which is based on the Internet of Things (IoT) network. The system includes a Passive Pyroelectric Infrared Detector for human detection and an analog flame sensor to sense the appearance of the concerned objects and then transmit the signal to the workstation via Wi-Fi based on the microcontroller Espressif32 (Esp32). The computer vision models YOLOv8 (You Only Look Once version 8) and Cascade Classifier are trained and implemented into the workstation, which is able to identify people, some potentially dangerous objects, and fire. The drone is also controlled by three algorithms—distance maintenance, automatic yaw rotation, and potentially dangerous object avoidance—with the support of a proportional–integral–derivative (PID) controller. The Smart Drone Surveillance System has good commands for automatic tracking and streaming of the video of these specific circumstances and then transferring the data to the involved parties such as security or staff.","PeriodicalId":36448,"journal":{"name":"Drones","volume":"78 22","pages":""},"PeriodicalIF":4.8,"publicationDate":"2023-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138606349","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}
Swarm models hold significant importance as they provide the collective behavior of self-organized systems. Boids model is a fundamental framework for studying emergent behavior in swarms systems. It addresses problems related to simulating the emergent behavior of autonomous agents, such as alignment, cohesion, and repulsion, to imitate natural flocking movements. However, traditional models of Boids often lack pinning and the adaptability to quickly adapt to the dynamic environment. To address this limitation, we introduce reinforcement learning into the framework of Boids to solve the problem of disorder and the lack of pinning. The aim of this approach is to enable drone swarms to quickly and effectively adapt to dynamic external environments. We propose a method based on the Q-learning network to improve the cohesion and repulsion parameters in the Boids model to achieve continuous obstacle avoidance and maximize spatial coverage in the simulation scenario. Additionally, we introduce a virtual leader to provide pinning and coordination stability, reflecting the leadership and coordination seen in drone swarms. To validate the effectiveness of this method, we demonstrate the model’s capabilities through empirical experiments with drone swarms, and show the practicality of the RL-Boids framework.
{"title":"Reinforcement Learning-Based Formation Pinning and Shape Transformation for Swarms","authors":"Zhaoqi Dong, Qizhen Wu, Lei Chen","doi":"10.3390/drones7110673","DOIUrl":"https://doi.org/10.3390/drones7110673","url":null,"abstract":"Swarm models hold significant importance as they provide the collective behavior of self-organized systems. Boids model is a fundamental framework for studying emergent behavior in swarms systems. It addresses problems related to simulating the emergent behavior of autonomous agents, such as alignment, cohesion, and repulsion, to imitate natural flocking movements. However, traditional models of Boids often lack pinning and the adaptability to quickly adapt to the dynamic environment. To address this limitation, we introduce reinforcement learning into the framework of Boids to solve the problem of disorder and the lack of pinning. The aim of this approach is to enable drone swarms to quickly and effectively adapt to dynamic external environments. We propose a method based on the Q-learning network to improve the cohesion and repulsion parameters in the Boids model to achieve continuous obstacle avoidance and maximize spatial coverage in the simulation scenario. Additionally, we introduce a virtual leader to provide pinning and coordination stability, reflecting the leadership and coordination seen in drone swarms. To validate the effectiveness of this method, we demonstrate the model’s capabilities through empirical experiments with drone swarms, and show the practicality of the RL-Boids framework.","PeriodicalId":36448,"journal":{"name":"Drones","volume":"36 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136346841","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}
Jacob Virtue, Darren Turner, Guy Williams, Stephanie Zeliadt, Henry Walshaw, Arko Lucieer
Seabird surveys are used to monitor population demography and distribution and help us understand anthropogenic pressures on seabird species. Burrow-nesting seabirds are difficult to survey. Current ground survey methods are invasive, time-consuming and detrimental to colony health. Data derived from short transects used in ground surveys are extrapolated to derive whole-colony population estimates, which introduces sampling bias due to factors including uneven burrow distribution and varying terrain. We investigate a new survey technique for nocturnally active burrow-nesting seabirds using unoccupied aerial vehicles (UAVs) and thermal sensor technology. We surveyed a three-hectare short-tailed shearwater (Ardenna tenuirostris) colony in Tasmania, Australia. Occupied burrows with resident chicks produced pronounced thermal signatures. This survey method captured a thermal response of every occupied burrow in the colony. Count automation techniques were developed to detect occupied burrows. To validate the results, we compared automated and manual counts of thermal imagery. Automated counts of occupied burrows were 9.3% higher and took approximately 5% of the time needed for manual counts. Using both manual and automated counts, we estimated that there were 5249–5787 chicks for the 2021/2022 breeding season. We provide evidence that high-resolution UAV thermal remote sensing and count automation can improve population estimates of burrow-nesting seabirds.
{"title":"Burrow-Nesting Seabird Survey Using UAV-Mounted Thermal Sensor and Count Automation","authors":"Jacob Virtue, Darren Turner, Guy Williams, Stephanie Zeliadt, Henry Walshaw, Arko Lucieer","doi":"10.3390/drones7110674","DOIUrl":"https://doi.org/10.3390/drones7110674","url":null,"abstract":"Seabird surveys are used to monitor population demography and distribution and help us understand anthropogenic pressures on seabird species. Burrow-nesting seabirds are difficult to survey. Current ground survey methods are invasive, time-consuming and detrimental to colony health. Data derived from short transects used in ground surveys are extrapolated to derive whole-colony population estimates, which introduces sampling bias due to factors including uneven burrow distribution and varying terrain. We investigate a new survey technique for nocturnally active burrow-nesting seabirds using unoccupied aerial vehicles (UAVs) and thermal sensor technology. We surveyed a three-hectare short-tailed shearwater (Ardenna tenuirostris) colony in Tasmania, Australia. Occupied burrows with resident chicks produced pronounced thermal signatures. This survey method captured a thermal response of every occupied burrow in the colony. Count automation techniques were developed to detect occupied burrows. To validate the results, we compared automated and manual counts of thermal imagery. Automated counts of occupied burrows were 9.3% higher and took approximately 5% of the time needed for manual counts. Using both manual and automated counts, we estimated that there were 5249–5787 chicks for the 2021/2022 breeding season. We provide evidence that high-resolution UAV thermal remote sensing and count automation can improve population estimates of burrow-nesting seabirds.","PeriodicalId":36448,"journal":{"name":"Drones","volume":"56 15","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136283422","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 paper introduces a practical navigation approach for nonholonomic Unmanned Aerial Vehicles (UAVs) in 3D environment settings with numerous stationary and dynamic obstacles. To achieve the intended outcome, Dynamic Programming (DP) is combined with a reactive control algorithm. The DP allows the UAVs to navigate among known static barriers and obstacles. Additionally, the reactive controller uses data from the onboard sensor to avoid unforeseen obstacles. The proposed strategy is illustrated through computer simulation results. In simulations, the UAV successfully navigates around dynamic obstacles while maintaining its route to the target. These results highlight the ability of our proposed approach to ensure safe and efficient UAV navigation in complex and obstacle-laden environments.
{"title":"A Hybrid Global/Reactive Algorithm for Collision-Free UAV Navigation in 3D Environments with Steady and Moving Obstacles","authors":"Satish C. Verma, Siyuan Li, Andrey V. Savkin","doi":"10.3390/drones7110675","DOIUrl":"https://doi.org/10.3390/drones7110675","url":null,"abstract":"This paper introduces a practical navigation approach for nonholonomic Unmanned Aerial Vehicles (UAVs) in 3D environment settings with numerous stationary and dynamic obstacles. To achieve the intended outcome, Dynamic Programming (DP) is combined with a reactive control algorithm. The DP allows the UAVs to navigate among known static barriers and obstacles. Additionally, the reactive controller uses data from the onboard sensor to avoid unforeseen obstacles. The proposed strategy is illustrated through computer simulation results. In simulations, the UAV successfully navigates around dynamic obstacles while maintaining its route to the target. These results highlight the ability of our proposed approach to ensure safe and efficient UAV navigation in complex and obstacle-laden environments.","PeriodicalId":36448,"journal":{"name":"Drones","volume":"3 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136283830","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}
Chang Wang, Jiaqing Wang, Changyun Wei, Yi Zhu, Dong Yin, Jie Li
Collaborative autonomous landing of a quadrotor Unmanned Aerial Vehicle (UAV) on a moving Unmanned Ground Vehicle (UGV) presents challenges due to the need for accurate real-time tracking of the UGV and the adjustment for the landing policy. To address this challenge, we propose a progressive learning framework for generating an optimal landing policy based on vision without the need of communication between the UAV and the UGV. First, we propose the Landing Vision System (LVS) to offer rapid localization and pose estimation of the UGV. Then, we design an Automatic Curriculum Learning (ACL) approach to learn the landing tasks under different conditions of UGV motions and wind interference. Specifically, we introduce a neural network-based difficulty discriminator to schedule the landing tasks according to their levels of difficulty. Our method achieves a higher landing success rate and accuracy compared with the state-of-the-art TD3 reinforcement learning algorithm.
{"title":"Vision-Based Deep Reinforcement Learning of UAV-UGV Collaborative Landing Policy Using Automatic Curriculum","authors":"Chang Wang, Jiaqing Wang, Changyun Wei, Yi Zhu, Dong Yin, Jie Li","doi":"10.3390/drones7110676","DOIUrl":"https://doi.org/10.3390/drones7110676","url":null,"abstract":"Collaborative autonomous landing of a quadrotor Unmanned Aerial Vehicle (UAV) on a moving Unmanned Ground Vehicle (UGV) presents challenges due to the need for accurate real-time tracking of the UGV and the adjustment for the landing policy. To address this challenge, we propose a progressive learning framework for generating an optimal landing policy based on vision without the need of communication between the UAV and the UGV. First, we propose the Landing Vision System (LVS) to offer rapid localization and pose estimation of the UGV. Then, we design an Automatic Curriculum Learning (ACL) approach to learn the landing tasks under different conditions of UGV motions and wind interference. Specifically, we introduce a neural network-based difficulty discriminator to schedule the landing tasks according to their levels of difficulty. Our method achieves a higher landing success rate and accuracy compared with the state-of-the-art TD3 reinforcement learning algorithm.","PeriodicalId":36448,"journal":{"name":"Drones","volume":"138 39","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136351660","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}
Daehan Ha, Seongah Jeong, Jinkyu Kang, Joonhyuk Kang
Unmanned aerial vehicle (UAV)-assisted simultaneous wireless information and power transfer (SWIPT) systems have recently gained significant attraction in internet-of-things (IoT) applications that have limited or no infrastructure. Specifically, the free mobility of UAVs in three-dimensional (3D) space allows us good-quality channel links, thereby enhancing the communication environment and improving performance in terms of achievable rates, latency, and energy efficiency. Meanwhile, IoT devices can extend their battery life by harvesting the energy following the SWIPT protocol, which leads to an increase in the overall system lifespan. In this paper, we propose a secure UAV-assisted SWIPT system designed to optimize the secrecy energy efficiency (SEE) of a ground network, wherein a base station (BS) transmits confidential messages to an energy-constrained device in the presence of a passive eavesdropper. Here, we employ a UAV acting as a helper node to improve the SEE of the system and to aid in the energy harvesting (EH) of the battery-limited ground device following the SWIPT protocol. To this end, we formulate the SEE maximization problem by jointly optimizing the transmit powers of the BS and UAV, the power-splitting ratio for EH operations, and the UAV’s flight path. The solution is obtained via a proposed algorithm that leverages successive convex approximation (SCA) and Dinkelbach’s method. Through simulations, we corroborate the feasibility and effectiveness of the proposed algorithm compared to conventional partial optimization approaches.
{"title":"Secrecy Energy Efficiency Maximization for Secure Unmanned-Aerial-Vehicle-Assisted Simultaneous Wireless Information and Power Transfer Systems","authors":"Daehan Ha, Seongah Jeong, Jinkyu Kang, Joonhyuk Kang","doi":"10.3390/drones7110672","DOIUrl":"https://doi.org/10.3390/drones7110672","url":null,"abstract":"Unmanned aerial vehicle (UAV)-assisted simultaneous wireless information and power transfer (SWIPT) systems have recently gained significant attraction in internet-of-things (IoT) applications that have limited or no infrastructure. Specifically, the free mobility of UAVs in three-dimensional (3D) space allows us good-quality channel links, thereby enhancing the communication environment and improving performance in terms of achievable rates, latency, and energy efficiency. Meanwhile, IoT devices can extend their battery life by harvesting the energy following the SWIPT protocol, which leads to an increase in the overall system lifespan. In this paper, we propose a secure UAV-assisted SWIPT system designed to optimize the secrecy energy efficiency (SEE) of a ground network, wherein a base station (BS) transmits confidential messages to an energy-constrained device in the presence of a passive eavesdropper. Here, we employ a UAV acting as a helper node to improve the SEE of the system and to aid in the energy harvesting (EH) of the battery-limited ground device following the SWIPT protocol. To this end, we formulate the SEE maximization problem by jointly optimizing the transmit powers of the BS and UAV, the power-splitting ratio for EH operations, and the UAV’s flight path. The solution is obtained via a proposed algorithm that leverages successive convex approximation (SCA) and Dinkelbach’s method. Through simulations, we corroborate the feasibility and effectiveness of the proposed algorithm compared to conventional partial optimization approaches.","PeriodicalId":36448,"journal":{"name":"Drones","volume":"26 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135037171","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 paper presents the Vertidrome Airside Level of Service (VALoS) framework, a novel performance metric designed to evaluate airside traffic flow operations at vertidromes in the context of Urban Air Mobility (UAM). As the UAM industry rapidly evolves, the need for a comprehensive evaluation framework becomes increasingly important. The VALoS framework provides a performance-based approach to evaluating vertidrome traffic flow performance, considering metrics like average passenger delay, air taxi in-flight delay, and vertidrome punctuality. Unlike existing Level of Service approaches, the VALoS framework unifies the requirements of various stakeholders, the passenger, the air taxi operator, and the vertidrome operator each with their own performance metric and target. It provides a multi-faceted approach covering airside air and ground traffic flows, arrivals and departures, and performance changes during strategic planning and tactical execution phases. The VALoS is evaluated at 15-min intervals while considering changing stakeholder performance targets and operational uncertainties. For the reference use case, the study demonstrates the significant impact of short-term disruptions, while stochastic deviations can be neglected. Higher traffic volumes due to changing demand/capacity ratios result in higher VALoS variability. The VALoS framework, together with a fast-time simulation, provides a versatile method for exploring future vertidrome traffic flows and supporting strategic vertidrome airside planning and integration. This integrated approach is essential for the evolving UAM vertidrome industry; aligning the interests of different stakeholders and promoting sustainable and efficient vertidrome planning and operation.
{"title":"Vertidrome Airside Level of Service: Performance-Based Evaluation of Vertiport Airside Operations","authors":"Karolin Schweiger, Franz Knabe","doi":"10.3390/drones7110671","DOIUrl":"https://doi.org/10.3390/drones7110671","url":null,"abstract":"This paper presents the Vertidrome Airside Level of Service (VALoS) framework, a novel performance metric designed to evaluate airside traffic flow operations at vertidromes in the context of Urban Air Mobility (UAM). As the UAM industry rapidly evolves, the need for a comprehensive evaluation framework becomes increasingly important. The VALoS framework provides a performance-based approach to evaluating vertidrome traffic flow performance, considering metrics like average passenger delay, air taxi in-flight delay, and vertidrome punctuality. Unlike existing Level of Service approaches, the VALoS framework unifies the requirements of various stakeholders, the passenger, the air taxi operator, and the vertidrome operator each with their own performance metric and target. It provides a multi-faceted approach covering airside air and ground traffic flows, arrivals and departures, and performance changes during strategic planning and tactical execution phases. The VALoS is evaluated at 15-min intervals while considering changing stakeholder performance targets and operational uncertainties. For the reference use case, the study demonstrates the significant impact of short-term disruptions, while stochastic deviations can be neglected. Higher traffic volumes due to changing demand/capacity ratios result in higher VALoS variability. The VALoS framework, together with a fast-time simulation, provides a versatile method for exploring future vertidrome traffic flows and supporting strategic vertidrome airside planning and integration. This integrated approach is essential for the evolving UAM vertidrome industry; aligning the interests of different stakeholders and promoting sustainable and efficient vertidrome planning and operation.","PeriodicalId":36448,"journal":{"name":"Drones","volume":" 15","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135191013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The exponential growth of unmanned aerial vehicles (UAVs) or drones in recent years has raised concerns about their safe operation, especially in beyond-line-of-sight (BLOS) scenarios. Existing unmanned aircraft system traffic management (UTM) heavily relies on commercial communication networks, which may become ineffective if network infrastructures are damaged or disabled. For this challenge, we propose a novel approach that leverages vehicle-to-vehicle (V2V) communications to enhance UAV safety and efficiency in UAV operations. In this study, we present a UAV information collection and sharing system named Drone Mapper®, enabled by V2V communications, so that UAVs can share their locations with each another as well as with the ground operation station. Additionally, we introduce an autonomous flight coordination control system (AFCCS) that augments UAV safety operations by providing two essential functionalities: UAV collision avoidance and UAV formation flight, both of which work based on V2V communications. To evaluate the performance of the developed AFCCS, we conducted comprehensive field experiments focusing on UAV collision avoidance and formation flight. The experimental results demonstrate the effectiveness of the proposed system and show seamless operations among multiple UAVs.
{"title":"Vehicle-to-Vehicle Based Autonomous Flight Coordination Control System for Safer Operation of Unmanned Aerial Vehicles","authors":"Lin Shan, Ryu Miura, Takashi Matsuda, Miho Koshikawa, Huan-Bang Li, Takeshi Matsumura","doi":"10.3390/drones7110669","DOIUrl":"https://doi.org/10.3390/drones7110669","url":null,"abstract":"The exponential growth of unmanned aerial vehicles (UAVs) or drones in recent years has raised concerns about their safe operation, especially in beyond-line-of-sight (BLOS) scenarios. Existing unmanned aircraft system traffic management (UTM) heavily relies on commercial communication networks, which may become ineffective if network infrastructures are damaged or disabled. For this challenge, we propose a novel approach that leverages vehicle-to-vehicle (V2V) communications to enhance UAV safety and efficiency in UAV operations. In this study, we present a UAV information collection and sharing system named Drone Mapper®, enabled by V2V communications, so that UAVs can share their locations with each another as well as with the ground operation station. Additionally, we introduce an autonomous flight coordination control system (AFCCS) that augments UAV safety operations by providing two essential functionalities: UAV collision avoidance and UAV formation flight, both of which work based on V2V communications. To evaluate the performance of the developed AFCCS, we conducted comprehensive field experiments focusing on UAV collision avoidance and formation flight. The experimental results demonstrate the effectiveness of the proposed system and show seamless operations among multiple UAVs.","PeriodicalId":36448,"journal":{"name":"Drones","volume":" 27","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135241604","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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