A power consumption model for multi-rotor small unmanned aircraft systems

Liu, Zhilong; Sengupta, Raja; Kurzhanskiy, Alex A.

doi:10.1109/icuas.2017.7991310

Cited by 110 publications

(100 citation statements)

References 2 publications

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“…We consider the UAV to be a low-altitude autonomous multi-rotor drone. The previous studies, 30,42,43 reveal that the power consumed by such a drone while in the air does 30 ). Also, we assume that after leaving for the mission, the drone stays all the time in the air, even when transferring the energy to the EDs.…”

Section: Energy Consumption and Wireless Power Transfer Modelsmentioning

confidence: 93%

Wireless power transfer from unmanned aerial vehicle to low-power wide area network nodes: Performance and business prospects for LoRaWAN

Tiurlikova

Stepanov

Mikhaylov

2019

International Journal of Distributed Sensor Networks

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Supported by the remarkable progress across many technological domains, the Internet of Things (IoT) ecosystem demonstrates steady growth over the few past years. This growth enables a number of new exciting applications. Nonetheless, hardly one can say today that the utility of the IoT is used to its full potential. This fact is especially notable for the monitoring applications deployed in remote areas. To address the needs of these use cases, in the article we propose a solution based on the combination of three key technologies: the low-power wide area networks, the unmanned aerial vehicles, and the wireless power transfer. In the article, we first detail the novel concept of a wireless power transfer-enabled unmanned aerial vehicle employed to charge the LoRaWAN sensor nodes. Then, via extensive simulations and analysis of an illustrative LoRaWAN application, we investigate both technical and, notably, business performance indicators, and compare them against the ones for a baseline scenario with no unmanned aerial vehicle. Our results illustratively demonstrate that in the long-term perspective, the inclusion of a wireless power transfer-enabled drone may drastically reduce the system's operating expenses. At the very same time, our results highlight the limits, bottlenecks, and trade-offs related to the proposed concept, thus providing the basis and calling for further investigation.

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Section: Energy Consumption and Wireless Power Transfer Modelsmentioning

confidence: 93%

Wireless power transfer from unmanned aerial vehicle to low-power wide area network nodes: Performance and business prospects for LoRaWAN

Tiurlikova

Stepanov

Mikhaylov

2019

International Journal of Distributed Sensor Networks

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“…1 Ground speed is the horizontal speed of an aircraft relative to the ground [33]. 2 The positioning errors of fourth-generation long-term evolution (4G LTE) network devices are typically in the range between 10 and 50 meters, depending on the adopted positioning protocol [36].…”

Section: User Location Modelmentioning

confidence: 99%

“…Here, W u = m u g 0 is the weight of the UAV, and m u and g 0 denote the mass of the UAV and the gravitational acceleration, respectively. c 1 , c 2 , c 3 , and c 4 are UAV aerodynamic power consumption parameters [33].…”

Section: F Aerodynamic Power Consumptionmentioning

confidence: 99%

“…The proposed suboptimal iterative algorithm converges to a locally optimal solution of (50) in polynomial time [27]. Mass of the UAV and the gravitational acceleration, mu and g0 6 kg and 9.8 m/s 2 [33] Time UAV aerodynamic power consumption coefficients, c3 and c4 0.0439 kg/m and 0.0306 Ns/m [33], [40] Minimum required SINR at user k, Γreq k 14 dB…”

Section: Suboptimal Solution Of the Optimization Problemmentioning

confidence: 99%

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Multiuser MISO UAV Communications in Uncertain Environments With No-Fly Zones: Robust Trajectory and Resource Allocation Design

Sun

et al. 2020

IEEE Trans. Commun.

148

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In this paper, we investigate robust resource allocation algorithm design for multiuser downlink multiple-input single-output (MISO) unmanned aerial vehicle (UAV) communication systems, where we account for the various uncertainties that are unavoidable in such systems and, if left unattended, may severely degrade system performance.We jointly optimize the two-dimensional (2-D) trajectory and the transmit beamforming vector of the UAV for minimization of the total power consumption. The algorithm design is formulated as a non-convex optimization problem taking into account the imperfect knowledge of the angle of departure (AoD) caused by UAV jittering, user location uncertainty, wind speed uncertainty, and polygonal no-fly zones (NFZs). Despite the non-convexity of the optimization problem, we solve it optimally by employing monotonic optimization theory and semidefinite programming relaxation which yields the optimal 2-D trajectory and beamforming policy. Since the developed optimal resource allocation algorithm entails a high computational complexity, we also propose a suboptimal iterative low-complexity scheme based on successive convex approximation to strike a balance between optimality and computational complexity. Our simulation results reveal not only the significant power savings enabled by the proposed algorithms compared to two baseline schemes, but also confirm their robustness with respect to UAV jittering, wind speed uncertainty, and user location uncertainty. Moreover, our results unveil that the joint presence of wind speed uncertainty and NFZs has a considerable impact on the UAV trajectory. Nevertheless, by counteracting the wind speed uncertainty with the proposed robust design, we can simultaneously minimize the total UAV power consumption and ensure a secure trajectory that does not trespass any NFZ. ). This paper was presented in part at IEEE Globecom 2018 [1]. 2 be employed as aerial base stations to offer temporary communication links in a timely and cost-effective manner. Moreover, due to their high mobility and maneuverability, UAVs can adapt their trajectories based on the actual environment and terrain which improves system performance [3]. As a result, UAV-assisted communication systems have drawn significant attention from both academia and industry. For instance, the authors of [4] studied suboptimal UAV trajectory design for maximization of the energy-efficiency of UAV communication systems. The authors of [5] proposed a suboptimal joint trajectory, power allocation, and user scheduling algorithm for maximization of the minimum user throughput in multi-UAV systems.Secure UAV communications was investigated in [6] where the trajectory of a UAV and its transmit power were jointly optimized to maximize the system secrecy rate. The authors of [7] proposed solar-powered UAV communication systems and studied the jointly optimal resource allocation and UAV trajectory design for maximization of the system sum throughput. In fact, the throughput of UAV communication systems can be further i...

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“…We can compute the energy cost of traveling from a node i to a node j, denoted e k d,total,ij , as a sum of the energy cost associated with ascending to that height, denoted e k a,ij , descending from that height, denoted e k d,ij , flying the distance between the two nodes, denoted e k t,ij , and the costs associated with performing the rendezvous maneuver (if applicable). We use the energy model described in [19] in which the authors reported results within 10% of tested quadrotors. We stress that our future analysis is not dependent on this model, but requires an energy model that is dependent on the changing mass of the quadrotor.…”

Section: Edge Energy Costs Of the Uav In Flightmentioning

confidence: 99%

The Multi-objective Dynamic Traveling Salesman Problem: Last Mile Delivery with Unmanned Aerial Vehicles Assistance

Remer

Malikopoulos

2019

2019 American Control Conference (ACC)

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In this paper, we present an approach to optimizing the last-mile delivery route of a truck using coordination with unmanned aerial vehicles (UAVs). First, a traveling salesman problem is formulated to determine the truck's route. Then, a scheduling problem is formulated to determined the routes for the UAVs. A genetic algorithm is used to solve these problems, and simulated results are presented.

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A power consumption model for multi-rotor small unmanned aircraft systems

Cited by 110 publications

References 2 publications

Wireless power transfer from unmanned aerial vehicle to low-power wide area network nodes: Performance and business prospects for LoRaWAN

Wireless power transfer from unmanned aerial vehicle to low-power wide area network nodes: Performance and business prospects for LoRaWAN

Multiuser MISO UAV Communications in Uncertain Environments With No-Fly Zones: Robust Trajectory and Resource Allocation Design

The Multi-objective Dynamic Traveling Salesman Problem: Last Mile Delivery with Unmanned Aerial Vehicles Assistance

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