Solar Power Can Substantially Prolong Maximum Achievable Airtime of Quadcopter Drones

Lin, Ching‐Fuh; Lin, Ta-Jung; Liao, Weisheng; Lan, Hsiang; Lin, Jiun-Yu; Chiu, Chi-Han; Danner, Aaron J.

doi:10.1002/advs.202001497

Cited by 10 publications

(5 citation statements)

References 34 publications

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“…Over the past several years, tremendous efforts have been spent in improving energetic efficiency of robotic flight at small scales [3][4][5][6][7][8] . Besides, multiple workaround solutions have been explored, including the usage of alternative energy sources [9][10][11][12] . Furthermore, multimodal operation-a bioinspired approach, such as hybrid aerial and terrestrial locomotion has been experimentally shown to substantially expand the working range provided suitable operating conditions [13][14][15][16] .…”

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confidence: 99%

Energy efficient perching and takeoff of a miniature rotorcraft

et al. 2023

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The flight time of aircraft rapidly decreases with smaller scales because the lift-to-drag ratio decreases when scaling down. Aerial-surface locomotion, or perching is one energy efficient solution to prolong the fight time by maintaining the drone at a high vantage point. Current perching strategies require additional components to ensure robots firmly attach to the surfaces, and able to detach afterwards, resulting in increased power consumption owing to the added weight. Here, we report a 32-g rotorcraft with the ability to repeatedly perch and take off from overhangs and walls on different wet and dry substances. A propelling thrust is used to support the robot to keep rotorcraft balance against the surface. Integrating with the mussel-inspired wet adhesives, the rotorcraft dispenses the additional components required for attachment and taking off. The final rotorcraft is 32.15 g, only 1.09 g heavier than the original prototype, but shows a 50% and 85% reduction in power consumption when perching on ceilings and walls respectively. The saved power leads to a fourfold increase in the total mission time.

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confidence: 99%

Energy efficient perching and takeoff of a miniature rotorcraft

et al. 2023

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“…The endurance of drones can be improved using various methods, such as changing the battery [ 44 , 45 ], charging the battery via wires [ 46 , 47 ], wireless recharging [ 48 ], solar cells [ 49 , 50 ], laser-beam in-flight recharging [ 49 , 51 ], and tethered drones [ 49 ]. An average time of 90 min is needed to fully charge an empty battery.…”

Section: Methodsmentioning

confidence: 99%

Algorithms for Delivery of Data by Drones in an Isolated Area Divided into Squares

Deaconu

Udroiu

Nănău

2021

Sensors

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Drones are frequently used for the delivery of materials or other goods, and to facilitate the capture and transmission of data. Moreover, drone networks have gained significant interest in a number of scenarios, such as in quarantined or isolated areas, following technical damage due to a disaster, or in non-urbanized areas without communication infrastructure. In this context, we propose a network of drones that are able to fly on a map covered by regular polygons, with a well-established mobility schedule, to carry and transfer data. Two means exist to equidistantly cover an area with points, namely, grouping the points into equilateral triangles or squares. In this study, a network of drones that fly in an aerial area divided into squares was proposed and investigated. This network was compared with the case in which the area is divided into equilateral triangles. The cost of the square drone network was lower than that of the triangular network with the same cell length, but the efficiency factors were better for the latter. Two situations related to increasing the drone autonomy using drone charging or battery changing stations were analyzed. This study proposed a Delay Tolerant Network (DTN) to optimize the transmission of data. Multiple simulation studies based on experimental flight tests were performed using the proposed algorithm versus five traditional DTN methods. A light Wi-Fi Arduino development board was used for the data transfer between drones and stations using delivery protocols. The efficiency of data transmission using single-copy and multiple-copy algorithms was analyzed. Simulation results showed a better performance of the proposed Time-Dependent Drone (TD-Drone) Dijkstra algorithm compared with the Epidemic, Spray and Wait, PRoPHET, MaxProp, and MaxDelivery routing protocols.

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“…After offloading some of the vehicle's computer tasks to the UAV-MEC server, the vehicle performs the rest of the tasks locally [22]. We consider that UAV-MECs have an energy supply through solar energy [23], wind [24], or wireless power transfer (WPT) technology [25], which are considered expensive compared to the direct power supply of the ground MEC server; then we will take the energy consumption into account.…”

Section: System Modelmentioning

confidence: 99%

Intelligent Pricing Model for Task Offloading in Unmanned Aerial Vehicle Mounted Mobile Edge Computing for Vehicular Network

Baktayan

Al-Baltah

Ghani

2022

JCOMSS

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In the fifth-generation (5G) cellular network, the Mobile Network Operator (MNO), and the Mobile Edge Computing (MEC) platform will play an important role in providing services to an increasing number of vehicles. Due to vehicle mobility and the rise of computation-intensive and delaysensitive vehicular applications, it is challenging to achieve the rigorous latency and reliability requirements of vehicular communication. The MNO, with the MEC server mounted on an unmanned aerial vehicle (UAV), should make a profit by providing its computing services and capabilities to moving vehicles. This paper proposes the use of dynamic pricing for computation offloading in UAV-MEC for vehicles. The novelty of this paper is in how the price influences offloading demand and decides how to reduce network costs (delay and energy) while maximizing UAV operator revenue, but not the offloading benefits with the mobility of vehicles and UAV. The optimization problem is formulated as a Markov Decision Process (MDP). The MDP can be solved by the Deep Reinforcement Learning (DRL) algorithm, especially the Deep Deterministic Policy Gradient (DDPG). Extensive simulation results demonstrate that the proposed pricing model outperforms greedy by 26% and random by 51% in terms of delay. In terms of system utility, the proposed pricing model outperforms greedy only by 17%. In terms of server congestion, the proposed pricing model outperforms random by 19% and is almost the same as greedy.

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Solar Power Can Substantially Prolong Maximum Achievable Airtime of Quadcopter Drones

Cited by 10 publications

References 34 publications

Energy efficient perching and takeoff of a miniature rotorcraft

Energy efficient perching and takeoff of a miniature rotorcraft

Algorithms for Delivery of Data by Drones in an Isolated Area Divided into Squares

Intelligent Pricing Model for Task Offloading in Unmanned Aerial Vehicle Mounted Mobile Edge Computing for Vehicular Network

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