4-Leg Landing Platform with Sensing for Reliable Rough Landing of Multi-copters

Cheon, Donghun; Choi, Ji Wook; Lee, Jang-Myung

doi:10.1007/978-3-030-66645-3_31

Cited by 1 publication

(1 citation statement)

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“…To detect the drone landing in the experiment, a four FSR sensor configuration, which was adapted from [29,38,55] was set up on the squared platform (Figure 9a). The sensors were installed at the edges of the platform using a small spiral spring and 20 × 20 cm 2 of wood (Figure 9b).…”

Section: Detection Of the Drone On The Wpt Circuitmentioning

confidence: 99%

Wireless Drone Charging Station Using Class-E Power Amplifier in Vertical Alignment and Lateral Misalignment Conditions

et al. 2022

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Recent major advancements in drone charging station design are related to the differences in coil design between the material (copper or aluminum) and inner thickness (diameter design) to address power transfer optimization and increased efficiency. The designs are normally challenged with reduced weight on the drone’s side, which can lead to reduced payload or misalignment position issues between receiver and transmitter, limiting the performance of wireless charging. In this work, the coil combination was tested in vertical alignment from 2 cm to 50 cm, and in lateral misalignment positions that were stretched across 2, 5, 8, 10, and 15 cm ranges. Simulated and experimental results demonstrated improved transfer distances when the drone battery load was 100 Ω. With the proposed design, the vertical transfer power that was achieved was 21.12 W, 0.460 A, with 81.5% transfer efficiency, while the maximum lateral misalignment air gap that was achieved was 2 cm with 19.22 W and 74.15% efficiency. This study provides evidence that the developed circuit that is based on magnetic resonant coupling (MRC) is an effective technique towards improving power transfer efficiency across different remote and unmanned Internet of Things (IoT) applications, including drones for radiation monitoring and smart agriculture.

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Section: Detection Of the Drone On The Wpt Circuitmentioning

confidence: 99%