Magnetic Resonant Coupling Wireless Power Transfer for Lightweight Batteryless UAVs

Torrisi, Alessandro; Brunelli, Davide

doi:10.1109/speedam48782.2020.9161953

Cited by 11 publications

(2 citation statements)

References 11 publications

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“…The far-field directional radio frequency (RF) wave [47] and electromagnetic resonance (ER) [48] are the most commonly used WPT technologies. Specifically, the former has a long transmission distance, while it has the strict requirements on the positions of transmitters.…”

Section: B Wireless Charging Modelmentioning

confidence: 99%

Joint Scheduling and Trajectory Optimization of Charging UAV in Wireless Rechargeable Sensor Networks

Liu

Pan

Sun

et al. 2022

IEEE Internet Things J.

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Wireless rechargeable sensor networks with a charging unmanned aerial vehicle (CUAV) have the broad application prospects in the power supply of the rechargeable sensor nodes (SNs). However, how to schedule a CUAV and design the trajectory to improve the charging efficiency of the entire system is still a vital problem. In this paper, we formulate a joint-CUAV scheduling and trajectory optimization problem (JSTOP) to simultaneously minimize the hovering points of CUAV, the number of the repeatedly covered SNs and the flying distance of CUAV for charging all SNs. Due to the complexity of JSTOP, it is decomposed into two optimization subproblems that are CUAV scheduling optimization problem (CSOP) and CUAV trajectory optimization problem (CTOP). CSOP is a hybrid optimization problem that consists of the continuous and discrete solution space, and the solution dimension in CSOP is not fixed since it should be changed with the number of hovering points of CUAV. Moreover, CTOP is a completely discrete optimization problem. Thus, we propose a particle swarm optimization (PSO) with a flexible dimension mechanism, a K-means operator and a punishment-compensation mechanism (PSOFKP) and a PSO with a discretization factor, a 2-opt operator and a path crossover reduction mechanism (PSOD2P) to solve the converted CSOP and CTOP, respectively. Simulation results evaluate the benefits of PSOFKP and PSOD2P under different scales and settings of the network, and the stability of the proposed algorithms is verified.

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Section: B Wireless Charging Modelmentioning

confidence: 99%

Joint Scheduling and Trajectory Optimization of Charging UAV in Wireless Rechargeable Sensor Networks

Liu

Pan

Sun

et al. 2022

IEEE Internet Things J.

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“…These batteryless devices enable a new application space such as body implants and wearables [10,11], and even deployments in extreme locations [12]. The architecture of a typical batteryless device consists of an energy harvester block that stores the ambient energy from several sources (e.g., solar [2], [5], radio-frequency (RF) [6], [13]) into an energy buffer, i.e., typically a capacitor. The stored energy in the capacitor is used to power the ultra-low-power microcontroller as well as other system components such as sensors and communication circuitry.…”

Section: Introductionmentioning

confidence: 99%

Enabling Transiently-Powered Communication via Backscattering Energy State Information

Torrisi

Yıldırım

Brunelli

2021

Lecture Notes in Electrical Engineering

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The growing interest in ultra-low-power wireless sensors powered directly by energy harvesters has revealed one of the major drawbacks of such battery-less devices, which is engaging communication between nodes, without wasting energy due to unavailable receivers. Backscatter communication enables low-power communication by eliminating energy-hungry hardware components and can communicate if IoT devices are ready to receive even at zero-energy onboard. In this paper, we present the design of a backscatter radio mechanism that is used as a feedback channel to transmit the energy state information almost for free. Simulation results demonstrate the effectiveness of our approach designed according to the novel approach of "transient computing".

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Coupling mechanism optimization to improve misalignment tolerance in UAV wireless charging systems

Rong,

Chang,

et al. 2024

J. Power Electron.

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Magnetic Resonant Coupling Wireless Power Transfer for Lightweight Batteryless UAVs

Cited by 11 publications

References 11 publications

Joint Scheduling and Trajectory Optimization of Charging UAV in Wireless Rechargeable Sensor Networks

Joint Scheduling and Trajectory Optimization of Charging UAV in Wireless Rechargeable Sensor Networks

Enabling Transiently-Powered Communication via Backscattering Energy State Information

Coupling mechanism optimization to improve misalignment tolerance in UAV wireless charging systems

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