Abstract
The paper considers the problem of autonomous orientation of rotorcraft. The task of orientation is an integral part of the task of autonomous piloting. From a fundamental standpoint, the vast majority of methods for solving this problem work in a strictly deterministic environment and are based on a developed mathematical apparatus. From a technological point of view, well-known approaches are based on a combination of satellite positioning technologies and sensors. The advantages of such technologies are low cost and the ability to reduce the computational complexity of algorithms by increasing the number of sensors used at various stages of solving the problem. But such technologies do not receive mass adoption because they have low positioning accuracy and are dependent on external observation conditions. The main problem that impedes the solution of this problem is the balance between the accuracy of positioning, the computational complexity of the algorithms and the stability of the system in non-deterministic environments. To solve this problem, a technology is proposed for presenting information on the position of the aircraft relative to the object of observation, based on complex linguistic variables. The techniques for representing the position of an object in two- and three-dimensional space are described. The technology of position coding in the RGB color palette, used to calculate the position of the object, as well as for the purpose of training the system by the operator in the future, is presented.
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Change history
14 July 2022
In the original version of the book, the following belated correction has been incorporated: The author name has been changed from “Jaafer Daiebel” to “Jaffar Daeibal” in the Frontmatter, Backmatter and in Chapter 47. The book and the chapter have been updated with the change.
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Daeibal, J., Sergeev, N. (2020). Technology of Self-orientation of Aircraft Relative to External Objects. In: Silhavy, R. (eds) Applied Informatics and Cybernetics in Intelligent Systems. CSOC 2020. Advances in Intelligent Systems and Computing, vol 1226. Springer, Cham. https://doi.org/10.1007/978-3-030-51974-2_47
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