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Open AccessArticle

A Method Based on Multi-Sensor Data Fusion for UAV Safety Distance Diagnosis

1
College of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming 650504, China
2
College of Science, Kunming University of Science and Technology, Kunming 650504, China
*
Author to whom correspondence should be addressed.
Electronics 2019, 8(12), 1467; https://doi.org/10.3390/electronics8121467
Received: 5 November 2019 / Revised: 26 November 2019 / Accepted: 28 November 2019 / Published: 3 December 2019
(This article belongs to the Special Issue Advanced Control Systems for Electric Drives)
With the increasing application of unmanned aerial vehicles (UAVs) to the inspection of high-voltage overhead transmission lines, the study of the safety distance between drones and wires has received extensive attention. The determination of the safety distance between the UAV and the transmission line is of great significance to improve the reliability of the inspection operation and ensure the safe and stable operation of the power grid and inspection equipment. Since there is no quantitative data support for the safety distance of overhead transmission lines in UAV patrol, it is impossible to provide accurate navigation information for UAV safe obstacle avoidance. This paper proposes a mathematical model based on a multi-sensor data fusion algorithm. The safety distance of the line drone is diagnosed. In these tasks, firstly, the physical model of the UAV in the complex electromagnetic field is established to determine the influence law of the UAV on the electric field distortion and analyze the maximum electric and magnetic field strength that the UAV can withstand. Then, based on the main factors affecting the UAV such as the maximum wind speed, inspection speed, positioning error, and the size of the drone, the adaptive weighted fusion algorithm is used to perform first-level data fusion on the homogeneous sensor data. Then, based on the improved evidence, the theory performs secondary fusion on the combined heterogeneous sensor data. According to the final processing result and the type of proposition set, we diagnose the current safety status of the drone to achieve an adaptive adjustment of the safety distance threshold. Lastly, actual measurement data is used to verify the mathematical model. The experimental results show that the mathematical model can accurately identify the safety status of the drone and adaptively adjust the safety distance according to the diagnosis result and surrounding environment information. View Full-Text
Keywords: overhead transmission line; UAV inspection; safe distance; multi-source data fusion; adaptive threshold overhead transmission line; UAV inspection; safe distance; multi-source data fusion; adaptive threshold
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Zhang, W.; Ning, Y.; Suo, C. A Method Based on Multi-Sensor Data Fusion for UAV Safety Distance Diagnosis. Electronics 2019, 8, 1467.

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