Development of Physics-Based Virtual Training Simulator for Inspections of Steel Transmission Towers

Industrial drones are widely used for the inspection of power transmission lines of steel transmission towers for the safety of workers, reduced inspection time and cost. However, mistakes in drone operations by unskilled operators may lead to major accidents, resulting in a lot of time and cost to...

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Veröffentlicht in:Journal of electrical engineering & technology 2024, 19(3), , pp.1943-1953
Hauptverfasser: Chae, Chang-Hun, Ko, Kwang-Hee
Format: Artikel
Sprache:eng
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Zusammenfassung:Industrial drones are widely used for the inspection of power transmission lines of steel transmission towers for the safety of workers, reduced inspection time and cost. However, mistakes in drone operations by unskilled operators may lead to major accidents, resulting in a lot of time and cost to recover from the accidents. Therefore, the education and training of drone operators in this inspection job are essential. Training using physical drones is quite limited. First, a large space is needed where drones are controlled. Second, a sufficient amount of time for training cannot be provided due to the capacity of batteries that may only last approximately ten minutes. Moreover, the education and training for the power transmission line inspection task are much more dangerous and complicated because it is difficult to train near the steel transmission towers. We develop a training simulator using virtual reality technology to overcome these difficulties. The virtual training simulator provides an immersive training environment by incorporating into the simulator various physical factors such as the weight, movement direction, and velocity of the drone, and the direction and speed of the wind. Also, it allows multiple users to operate drones and camera gimbals at the same time for effective training. The simulator was tested using a simulation scenario. Actual users evaluated its effectiveness in the training of the inspection task.
ISSN:1975-0102
2093-7423
DOI:10.1007/s42835-023-01692-9