Low-Altitude Navigation for Multi-Rotor Drones in Urban Areas

Bahabry, Ahmed; Wan, Xiangpeng; Ghazzai, Hakim; Menouar, Hamid; Vesonder, Gregg; Massoud, Yehia

doi:10.1109/access.2019.2925531

Cited by 43 publications

(27 citation statements)

References 36 publications

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“…Kwon et al [69] defined the flight airspace levels of UAVs at different heights by classifying the building height levels and then formed a UAV airspace map over the city in Yeonpyeong, Seoul. Bulusu et al [70] evaluated the capacity performance of urban low-altitude airspace based on throughput and other indicators; Bahabry et al [71] provided a layout for UAV charging piles in cities and researched the collision-free navigation of UAVs.…”

Section: A Theoretical System Of the Low-altitude Air Routesmentioning

confidence: 99%

Recent Research Progress of Unmanned Aerial Vehicle Regulation Policies and Technologies in Urban Low Altitude

et al. 2020

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With the rapid expansion in the number of Unmanned Aircraft Vehicles (UAVs) available and the development of modern technologies, the commercial applications of UAVs in urban areas, such as urban remote sensing (RS), express services, urban road traffic monitoring, urban police security, urban air shows and so on, have increased greatly. However, most UAVs, especially light and small civil UAVs, have been operating in low-altitude airspace, and a conflict may exist between increasing the number of UAVs and the limited low airspace. To promote low-altitude airspace resource development and to standardize the operation and management of UAVs in urban regions, some global laws and regulations and key technologies for urban low-altitude applications of UAVs have been implemented. This paper reviews the development of current policies and key technologies concerning safe and efficient operations of the light-and-small civil UAVs in low altitude in urban areas. Discussions are made progressively on measures and methods of airspace restriction, airspace structuring and air route planning in China primarily and the rest of world. After surveying the practical industry tests and the initial studies of air routes, the survey results indicate that the construction of air route networks is a scientific and effective measure to standardize and improve the efficiency of low-altitude UAV operations. From the view point of safety and efficiency, the most valuable direction for UAV regulation in urban regions involves deepening the research which largely relies on urban RS and Geographic Information System (GIS) technology, and application demonstrations of low-altitude public air route networks. INDEX TERMS Low-altitude airspace, RS and GIS for UAV regulation, UAV regulation technology and policy, Urban region, UAV low-altitude air routes.

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Section: A Theoretical System Of the Low-altitude Air Routesmentioning

confidence: 99%

Recent Research Progress of Unmanned Aerial Vehicle Regulation Policies and Technologies in Urban Low Altitude

et al. 2020

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“…When UAVs do not use any alternative energy sources from the ambient environment, they may fail to several tasks due to the limited operating time and the sustainable sources of power are considered as an effective solution [32]. Several studies [33], [34], [35] propose multiple charging stations distributed optimally in urban areas to allow UAVs to recharge their batteries when needed. A solar-powered drone in the sky was able to fly for two weeks as a world record [29].…”

Section: Related Workmentioning

confidence: 99%

Planning the Future of Smart Cities With Swarms of Fully Autonomous Unmanned Aerial Vehicles Using a Novel Framework

Kuru

2021

IEEE Access

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“…However, most of the previous studies are based on complex optimization solutions, evolutionary algorithms, or heuristic approaches [26]- [29]. Nowadays, the new research tendency of using artificial intelligence (AI), precisely, Reinforcement Learning (RL) came out as a new strategy that can grant the flying units sufficient intelligence to make local decisions to accomplish missions.…”

Section: A Literature Reviewmentioning

confidence: 99%

A UAV-Assisted Data Collection for Wireless Sensor Networks: Autonomous Navigation and Scheduling

et al. 2020

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Nowadays, Wireless Sensor Networks (WSNs) are playing a vital and sustainable role in many verticals touching different aspects of our lives including civil, public, and military applications. WSNs majorly consist of a few to several sensor nodes, that are connected to each other via wireless communication links and require real-time or delayed data transfer. In this paper, we propose an autonomous Unmanned Aerial Vehicle (UAV)-enabled data gathering mechanism for delay-tolerant WSN applications. The objective is to employ a self-trained UAV as a flying mobile unit collecting data from ground sensor nodes spatially distributed in a given geographical area during a predefined period of time. In this approach, two Reinforcement Learning (RL) approaches, specifically Deep Deterministic Gradient Decent (DDPG) and Q-learning (QL) algorithms, are jointly employed to train the UAV to understand the environment and provide effective scheduling to accomplish its data collection mission. The DDPG is used to autonomously decide the best trajectory to adopt in an obstacle-constrained environment, while the QL is developed to determine the order of nodes to visit such that the data collection time is minimized. The schedule is obtained while considering the limited battery capacity of the flying unit, its need to return the charging station, the time windows of data acquisition, and the priority of certain sensor nodes. Customized reward functions are designed for each RL model and, through numerical simulations, we investigate their training performances. We also analyze the behavior of the autonomous UAV for different selected scenarios and corroborate the ability of the proposed approach in performing effective data collection. A comparison with the deterministic optimal solution is provided to validate the performance of the learning-based approach. INDEX TERMS Internet-of-things, data gathering, reinforcement learning, scheduling, unmanned areal vehicles

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Low-Altitude Navigation for Multi-Rotor Drones in Urban Areas

Cited by 43 publications

References 36 publications

Recent Research Progress of Unmanned Aerial Vehicle Regulation Policies and Technologies in Urban Low Altitude

Recent Research Progress of Unmanned Aerial Vehicle Regulation Policies and Technologies in Urban Low Altitude

Planning the Future of Smart Cities With Swarms of Fully Autonomous Unmanned Aerial Vehicles Using a Novel Framework

A UAV-Assisted Data Collection for Wireless Sensor Networks: Autonomous Navigation and Scheduling

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